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class="no-underline flex items-center"><img alt="CS50's Introduction to Computer Science" title="CS50's Introduction to Computer Science" loading="lazy" width="36" height="36" decoding="async" data-nimg="1" class="object-cover overflow-clip my-0 mr-2 w-9 h-9" style="color:transparent" srcSet="/_next/image?url=https%3A%2F%2Fprod-discovery.edx-cdn.org%2Fcdn-cgi%2Fimage%2Fwidth%3Dauto%2Cheight%3Dauto%2Cquality%3D75%2Cformat%3Dwebp%2Fmedia%2Fcourse%2Fimage%2Fda1b2400-322b-459b-97b0-0c557f05d017-a3d1899c3344.small.png&w=48&q=75 1x, /_next/image?url=https%3A%2F%2Fprod-discovery.edx-cdn.org%2Fcdn-cgi%2Fimage%2Fwidth%3Dauto%2Cheight%3Dauto%2Cquality%3D75%2Cformat%3Dwebp%2Fmedia%2Fcourse%2Fimage%2Fda1b2400-322b-459b-97b0-0c557f05d017-a3d1899c3344.small.png&w=96&q=75 2x" 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focus-visible:ring-ring focus-visible:ring-offset-2 flex flex-col max-w-[1128px] mx-auto" style="animation-duration:0s"><h3 class="mb-4">Courses</h3><div class="hidden lg:block"><div class="gap-4 py-4 dynamic-grid flex-wrap undefined"></div></div><div class="lg:hidden"><div class="gap-4 py-4 flex overflow-x-scroll undefined"></div></div><div class="flex justify-center mx-auto mt-5"></div></div><div data-state="inactive" data-orientation="horizontal" role="tabpanel" aria-labelledby="radix-:R1cjjttrkva:-trigger-Executive Education" hidden="" id="radix-:R1cjjttrkva:-content-Executive Education" tabindex="0" class="mt-2 ring-offset-background focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 flex flex-col max-w-[1128px] mx-auto"></div><div data-state="inactive" data-orientation="horizontal" role="tabpanel" aria-labelledby="radix-:R1cjjttrkva:-trigger-Masters" hidden="" id="radix-:R1cjjttrkva:-content-Masters" tabindex="0" class="mt-2 ring-offset-background focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 flex flex-col max-w-[1128px] mx-auto"></div><div data-state="inactive" data-orientation="horizontal" role="tabpanel" aria-labelledby="radix-:R1cjjttrkva:-trigger-Bachelors" hidden="" id="radix-:R1cjjttrkva:-content-Bachelors" tabindex="0" class="mt-2 ring-offset-background focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-ring focus-visible:ring-offset-2 flex flex-col max-w-[1128px] mx-auto"></div></div></div><div class="pt-0 pb-6 fullwidth bg-putty-100"><h3 class="mb-8 mt-0 text-2xl">Related Topics</h3><div class="flex gap-3 overflow-x-auto scroll-smooth not-prose"></div><div class="pt-4"></div></div><a class="subnav-item -mt-1" name="Featured Data Analysis Courses" id="featured-data-analysis-courses"></a><div class="flex flex-col gap-2 pb-10"><div class="Default_content__HO8we"><div id=""><h3>Data analysis course curriculum</h3><p>Professionals can learn data analysis through online courses, potentially even earning a data analysis certificate. Data analysis classes will vary based on the goals set by the provider, industry focus, and other factors. However, students can expect to learn foundational information such as: </p><ul><li><p>The full data analysis process, from data collection to sharing key findings.</p></li><li><p>Types of data structures, file formats, and data sources. </p></li><li><p>Tools for gathering, wrangling, mining, and analyzing data. </p></li><li><p>Data visualization for sharing data analysis findings with stakeholders. </p></li></ul><p>Aspiring data analysts may also need to learn how to use specific data analysis tools, which can be taught through different courses. Consider starting with the following key data analysis tools:²</p><ul><li><p><b>Microsoft Excel: </b>spreadsheet software that allows you to collect, clean, organize, and analyze data sets. </p></li><li><p><b>Python:</b> a programming language commonly used for data analysis. </p></li><li><p><b>R:</b> a programming language that can be used for data mining. </p></li><li><p><b>Structured Query Language (SQL): </b>a programming language used for managing relational databases, which can be used to analyze information within those databases. </p></li></ul><p>Those interested in specific fields may also consider taking an advanced data analysis course. For example, taking a big data analysis course can prepare data analysts to interpret large, diverse data sets to inform smarter business decisions.³ A learner may also be interested in a specific area of data analysis, such as bioinformatics, which focuses on analyzing biological data for scientific purposes, such as medical research.⁴ There are many data analysis courses focused in this area of study, in addition to other specializations.</p></div></div><a class="subnav-item -mt-1" name="Data analyst jobs" id="data-analyst-jobs"></a><div class="Default_content__HO8we"><div id=""><h2>Data analyst jobs</h2><p>The functions of the data analyst are applicable at almost every type of business or organization. Their findings drive decision-making and help companies manage business operations, product development, competition, strategy, and more. The broad application of the data analysis role means there is a demand for experts in a wide variety of industries.</p><p>Data analysis job requirements can vary depending on the industry and type of role. Some professionals may seek a data analysis career through a <a class="text-link underline" href="https://www.edx.org/bachelors">bachelor’s degree program</a>, while others may choose to learn data analysis through a <span data-boot-camp-link="https://edx.org/boot-camps/">boot camp program</span>. Examples of data analysis jobs include: </p><ul><li><p><b>Operations research analyst:</b> Uses data analytics to help organizations make informed decisions on how to allocate resources, set prices, develop schedules for production and other operational functions.⁵</p></li><li><p><b>Market research analyst: </b>Gathers and analyzes data to help determine what products and services are in demand, how to price them, and to whom to market the product.⁶</p></li></ul><h3>How to become a data analyst</h3><p>There are multiple paths to becoming a data analyst. The required level of education may depend on the data analyst job and industry someone is interested in. Some positions may require a <a class="text-link underline" href="https://www.edx.org/bachelors/computer-data-sciences">bachelor’s degree</a> or a <a class="text-link underline" href="https://www.edx.org/masters/online-masters-in-data-science">master’s degree in data science</a> or a related field, while others may require a <span data-boot-camp-link="https://www.edx.org/boot-camps/data-analytics">boot camp</span> for data analytics. </p><p>Anyone can learn data analysis, but certain traits can be helpful for roles in this field. Individuals who enjoy mathematics, computer science, statistics, and the research process may be a good fit for a data analysis career. To learn more about this subject, individuals can start with an introductory online data analysis course to see if it is a career path they’d like to pursue.</p></div></div></div><div class="fullwidth max-w-none py-12 md:py-16 not-prose bg-primary-gradient"><div class="container"><h2 class="text-4xl md:text-6xl my-5 text-white italic"><span class="text-primary-foreground">More opportunities</span> for you to learn</h2><p class="text-lg leading-9 text-white">We've added 500+ learning opportunities to create one of the world's most comprehensive free-to-degree online learning platforms.</p><ul class="block md:grid md:grid-cols-2 md:gap-x-[100px] mx-auto lg:grid-cols-3"></ul></div></div><a class="subnav-item -mt-1" name="Data analysis FAQ" id="data-analysis-f-a-q"></a><a class="subnav-item" id="frequently-asked-questions"></a><div class="flex flex-col py-4 lg:pt-16"><h2 class="mt-2 mb-4 text-3xl font-bold">Frequently Asked Questions</h2><div class="flex flex-col gap-3 [&_button]:no-underline [&_button]:text-gray-dark [&_button:hover]:text-gray-dark" data-orientation="vertical"><div data-state="closed" data-orientation="vertical" class="border-b AccordionTextItem_item__adF2E AccordionTextItem_item__adF2E"><h3 data-orientation="vertical" data-state="closed" class="flex"><button type="button" aria-controls="radix-:Rjasjjttrkva:" aria-expanded="false" data-state="closed" data-orientation="vertical" id="radix-:R3asjjttrkva:" class="flex flex-1 gap-10 text-left items-center justify-between py-4 text-sm font-medium transition-all hover:underline [&[data-state=open]>svg]:rotate-180 AccordionTextItem_trigger__CiZ_J AccordionTextItem_trigger__CiZ_J" data-radix-collection-item=""><span>Why is data analysis important to learn?</span><svg width="15" height="15" viewBox="0 0 15 15" fill="none" xmlns="http://www.w3.org/2000/svg" class="h-4 w-4 shrink-0 text-primary-500 transition-transform duration-200"><path d="M3.13523 6.15803C3.3241 5.95657 3.64052 5.94637 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aria-controls="radix-:Rlasjjttrkva:" aria-expanded="false" data-state="closed" data-orientation="vertical" id="radix-:R5asjjttrkva:" class="flex flex-1 gap-10 text-left items-center justify-between py-4 text-sm font-medium transition-all hover:underline [&[data-state=open]>svg]:rotate-180 AccordionTextItem_trigger__CiZ_J AccordionTextItem_trigger__CiZ_J" data-radix-collection-item=""><span>What software or tools are used in data analysis?</span><svg width="15" height="15" viewBox="0 0 15 15" fill="none" xmlns="http://www.w3.org/2000/svg" class="h-4 w-4 shrink-0 text-primary-500 transition-transform duration-200"><path d="M3.13523 6.15803C3.3241 5.95657 3.64052 5.94637 3.84197 6.13523L7.5 9.56464L11.158 6.13523C11.3595 5.94637 11.6759 5.95657 11.8648 6.15803C12.0536 6.35949 12.0434 6.67591 11.842 6.86477L7.84197 10.6148C7.64964 10.7951 7.35036 10.7951 7.15803 10.6148L3.15803 6.86477C2.95657 6.67591 2.94637 6.35949 3.13523 6.15803Z" fill="currentColor" fill-rule="evenodd" 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AccordionTextItem_trigger__CiZ_J AccordionTextItem_trigger__CiZ_J" data-radix-collection-item=""><span>Why is Python used for data analysis?</span><svg width="15" height="15" viewBox="0 0 15 15" fill="none" xmlns="http://www.w3.org/2000/svg" class="h-4 w-4 shrink-0 text-primary-500 transition-transform duration-200"><path d="M3.13523 6.15803C3.3241 5.95657 3.64052 5.94637 3.84197 6.13523L7.5 9.56464L11.158 6.13523C11.3595 5.94637 11.6759 5.95657 11.8648 6.15803C12.0536 6.35949 12.0434 6.67591 11.842 6.86477L7.84197 10.6148C7.64964 10.7951 7.35036 10.7951 7.15803 10.6148L3.15803 6.86477C2.95657 6.67591 2.94637 6.35949 3.13523 6.15803Z" fill="currentColor" fill-rule="evenodd" clip-rule="evenodd"></path></svg></button></h3><div data-state="closed" id="radix-:Rnasjjttrkva:" hidden="" role="region" aria-labelledby="radix-:R7asjjttrkva:" data-orientation="vertical" class="overflow-hidden text-sm data-[state=closed]:animate-accordion-up data-[state=open]:animate-accordion-down" style="--radix-accordion-content-height:var(--radix-collapsible-content-height);--radix-accordion-content-width:var(--radix-collapsible-content-width)"></div></div><div data-state="closed" data-orientation="vertical" class="border-b AccordionTextItem_item__adF2E AccordionTextItem_item__adF2E"><h3 data-orientation="vertical" data-state="closed" class="flex"><button type="button" aria-controls="radix-:Rpasjjttrkva:" aria-expanded="false" data-state="closed" data-orientation="vertical" id="radix-:R9asjjttrkva:" class="flex flex-1 gap-10 text-left items-center justify-between py-4 text-sm font-medium transition-all hover:underline [&[data-state=open]>svg]:rotate-180 AccordionTextItem_trigger__CiZ_J AccordionTextItem_trigger__CiZ_J" data-radix-collection-item=""><span>What is predictive data analysis?</span><svg width="15" height="15" viewBox="0 0 15 15" fill="none" xmlns="http://www.w3.org/2000/svg" class="h-4 w-4 shrink-0 text-primary-500 transition-transform duration-200"><path 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[&[data-state=open]>svg]:rotate-180 AccordionTextItem_trigger__CiZ_J AccordionTextItem_trigger__CiZ_J" data-radix-collection-item=""><span>Can I get a discount if I enroll 10+ employees in data analysis courses?</span><svg width="15" height="15" viewBox="0 0 15 15" fill="none" xmlns="http://www.w3.org/2000/svg" class="h-4 w-4 shrink-0 text-primary-500 transition-transform duration-200"><path d="M3.13523 6.15803C3.3241 5.95657 3.64052 5.94637 3.84197 6.13523L7.5 9.56464L11.158 6.13523C11.3595 5.94637 11.6759 5.95657 11.8648 6.15803C12.0536 6.35949 12.0434 6.67591 11.842 6.86477L7.84197 10.6148C7.64964 10.7951 7.35036 10.7951 7.15803 10.6148L3.15803 6.86477C2.95657 6.67591 2.94637 6.35949 3.13523 6.15803Z" fill="currentColor" fill-rule="evenodd" clip-rule="evenodd"></path></svg></button></h3><div data-state="closed" id="radix-:Rtasjjttrkva:" hidden="" role="region" aria-labelledby="radix-:Rdasjjttrkva:" data-orientation="vertical" class="overflow-hidden text-sm data-[state=closed]:animate-accordion-up data-[state=open]:animate-accordion-down" style="--radix-accordion-content-height:var(--radix-collapsible-content-height);--radix-accordion-content-width:var(--radix-collapsible-content-width)"></div></div></div></div><div class="Default_content__HO8we"><div id=""><p>Sources</p><p>¹<a class="text-link underline external" href="https://www.oracle.com/business-analytics/data-analytics/" target="_blank" rel="noopener noreferrer">What is Data Analytics?</a><i> Oracle.</i> Retrieved October 18, 2022. </p><p>²<a class="text-link underline external" href="https://towardsdatascience.com/comparison-of-data-analysis-tools-excel-r-python-and-bi-tools-6c4685a8ea6f" target="_blank" rel="noopener noreferrer">Comparison of Data Analysis Tools: Excel, R, Python and BI Tools</a>. (2019). <i>Towards Data Science.</i> Retrieved October 19, 2022. </p><p>³<a class="text-link underline external" href="https://www.ibm.com/analytics/big-data-analytics" target="_blank" rel="noopener noreferrer">Big Data Analytics</a>. <i>IBM. </i>Retrieved October 18, 2022.</p><p>⁴<a class="text-link underline external" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1122955/" target="_blank" rel="noopener noreferrer">Bioinformatics</a>. (2002). <i>Science, medicine, and the future.</i> Retrieved October 18, 2022. </p><p>⁵<a class="text-link underline external" href="https://www.bls.gov/ooh/math/operations-research-analysts.htm#tab-2" target="_blank" rel="noopener noreferrer">What Operations Research Analysts Do</a>. (2022). <i>Bureau of Labor Statistics.</i> Retrieved October 18, 2022. </p><p>⁶<a class="text-link underline external" href="https://www.bls.gov/ooh/business-and-financial/market-research-analysts.htm#tab-2" target="_blank" rel="noopener noreferrer">What Market Research Analysts Do</a>. (2022). <i>Bureau of Labor Statistics.</i> Retrieved October 18, 2022. </p><p>⁷<a class="text-link underline external" href="https://www.python.org/doc/essays/blurb/" target="_blank" rel="noopener noreferrer">What is Python?</a> (2022). <i>Python</i>. Retrieved July 5, 2022. </p><p>⁸<a class="text-link underline external" href="https://www.sas.com/en_us/insights/analytics/predictive-analytics.html" target="_blank" rel="noopener noreferrer">Predictive Analytics: What It Is and Why It Matters</a>. <i>SAS.</i> Retrieved October 18, 2022. </p><p>⁹<a class="text-link underline external" href="https://azure.microsoft.com/en-us/resources/cloud-computing-dictionary/what-is-big-data-analytics/#importance-of-data-analytics" target="_blank" rel="noopener noreferrer">What is big data analytics?</a><i> Microsoft Azure.</i> Retrieved October 19, 2022. </p><p></p></div></div></div></article></main></div><div class="bg-primary text-primary-foreground pt-16"><footer class="flex justify-between max-w-screen-xl mx-auto px-4 pb-4 "><div class="flex flex-col w-full"><div class="flex flex-wrap justify-between mb-6 w-full"><a href="/" class="mb-6"><img alt="edX homepage" title="edX homepage" data-ot-ignore="true" loading="lazy" width="127" height="67" 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For example, taking a big data analysis course can prepare data analysts to interpret large, diverse data sets to inform smarter business decisions.³ A learner may also be interested in a specific area of data analysis, such as bioinformatics, which focuses on analyzing biological data for scientific purposes, such as medical research.⁴ There are many data analysis courses focused in this area of study, in addition to other specializations.\"}]]}]}],[\"$\",\"a\",\"1\",{\"className\":\"subnav-item -mt-1\",\"name\":\"Data analyst jobs\",\"id\":\"data-analyst-jobs\",\"children\":\"$undefined\"}],[\"$\",\"$L317\",null,{\"id\":\"$undefined\",\"children\":[\"$\",\"div\",null,{\"id\":\"\",\"children\":[[\"$\",\"h2\",\"0\",{\"children\":\"Data analyst jobs\"}],[\"$\",\"p\",\"1\",{\"children\":\"The functions of the data analyst are applicable at almost every type of business or organization. Their findings drive decision-making and help companies manage business operations, product development, competition, strategy, and more. The broad application of the data analysis role means there is a demand for experts in a wide variety of industries.\"}],[\"$\",\"p\",\"2\",{\"children\":[\"Data analysis job requirements can vary depending on the industry and type of role. Some professionals may seek a data analysis career through a \",[\"$\",\"a\",\"1\",{\"className\":\"text-link underline\",\"href\":\"https://www.edx.org/bachelors\",\"children\":\"bachelor’s degree program\"}],\", while others may choose to learn data analysis through a \",[\"$\",\"span\",\"3\",{\"data-boot-camp-link\":\"https://edx.org/boot-camps/\",\"children\":\"boot camp program\"}],\". Examples of data analysis jobs include: \"]}],[\"$\",\"ul\",\"3\",{\"children\":[[\"$\",\"li\",\"0\",{\"children\":[\"$\",\"p\",null,{\"children\":[[\"$\",\"b\",\"0\",{\"children\":\"Operations research analyst:\"}],\" Uses data analytics to help organizations make informed decisions on how to allocate resources, set prices, develop schedules for production and other operational functions.⁵\"]}]}],[\"$\",\"li\",\"1\",{\"children\":[\"$\",\"p\",null,{\"children\":[[\"$\",\"b\",\"0\",{\"children\":\"Market research analyst: \"}],\"Gathers and analyzes data to help determine what products and services are in demand, how to price them, and to whom to market the product.⁶\"]}]}]]}],[\"$\",\"h3\",\"4\",{\"children\":\"How to become a data analyst\"}],[\"$\",\"p\",\"5\",{\"children\":[\"There are multiple paths to becoming a data analyst. The required level of education may depend on the data analyst job and industry someone is interested in. Some positions may require a \",[\"$\",\"a\",\"1\",{\"className\":\"text-link underline\",\"href\":\"https://www.edx.org/bachelors/computer-data-sciences\",\"children\":\"bachelor’s degree\"}],\" or a \",[\"$\",\"a\",\"3\",{\"className\":\"text-link underline\",\"href\":\"https://www.edx.org/masters/online-masters-in-data-science\",\"children\":\"master’s degree in data science\"}],\" or a related field, while others may require a \",[\"$\",\"span\",\"5\",{\"data-boot-camp-link\":\"https://www.edx.org/boot-camps/data-analytics\",\"children\":\"boot camp\"}],\" for data analytics. \"]}],[\"$\",\"p\",\"6\",{\"children\":\"Anyone can learn data analysis, but certain traits can be helpful for roles in this field. Individuals who enjoy mathematics, computer science, statistics, and the research process may be a good fit for a data analysis career. 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The ability to collect and interpret data can aid in the improvement of business operations, product development, strategy, and much more. Because data analysis is widely used across many industries, it can be a valuable talent to bring to the table as a potential employee. \"}]}]]}],[\"$\",\"$L31a\",null,{\"value\":\"What software or tools are used in data analysis?\",\"className\":\"AccordionTextItem_item__adF2E AccordionTextItem_item__adF2E\",\"children\":[[\"$\",\"$L31b\",null,{\"className\":\"AccordionTextItem_trigger__CiZ_J AccordionTextItem_trigger__CiZ_J\",\"children\":[false,[\"$\",\"span\",null,{\"children\":\"What software or tools are used in data analysis?\"}]]}],[\"$\",\"$L31c\",null,{\"className\":\"AccordionTextItem_content__G0vl3 AccordionTextItem_content__G0vl3\",\"children\":[\"$\",\"p\",null,{\"children\":\"Tools commonly used in data analysis include Microsoft Excel, Python, R, and SQL. These tools allow data analysts to collect, clean, organize, and analyze data sets. \"}]}]]}],[\"$\",\"$L31a\",null,{\"value\":\"Why is Python used for data analysis?\",\"className\":\"AccordionTextItem_item__adF2E AccordionTextItem_item__adF2E\",\"children\":[[\"$\",\"$L31b\",null,{\"className\":\"AccordionTextItem_trigger__CiZ_J AccordionTextItem_trigger__CiZ_J\",\"children\":[false,[\"$\",\"span\",null,{\"children\":\"Why is Python used for data analysis?\"}]]}],[\"$\",\"$L31c\",null,{\"className\":\"AccordionTextItem_content__G0vl3 AccordionTextItem_content__G0vl3\",\"children\":[\"$\",\"p\",null,{\"children\":\"Python is a dynamically typed, object-oriented, high-level programming language.⁷ Its built-in data structures make it useful for data analysis tasks. Libraries help programmers format, process, and clean large data sets. Data visualization packages in Python make it possible to create charts that showcase trends and insights.\"}]}]]}],[\"$\",\"$L31a\",null,{\"value\":\"What is predictive data analysis?\",\"className\":\"AccordionTextItem_item__adF2E AccordionTextItem_item__adF2E\",\"children\":[[\"$\",\"$L31b\",null,{\"className\":\"AccordionTextItem_trigger__CiZ_J AccordionTextItem_trigger__CiZ_J\",\"children\":[false,[\"$\",\"span\",null,{\"children\":\"What is predictive data analysis?\"}]]}],[\"$\",\"$L31c\",null,{\"className\":\"AccordionTextItem_content__G0vl3 AccordionTextItem_content__G0vl3\",\"children\":[\"$\",\"p\",null,{\"children\":\"Predictive data analysis is the process of collecting and interpreting data for the purpose of identifying trends, correlations, and causation. It includes using data, statistical algorithms, and machine learning techniques to accomplish these tasks. When businesses can understand how certain factors affect sales, such as buyer demographics, they can use that insight to model future campaigns.⁸\"}]}]]}],[\"$\",\"$L31a\",null,{\"value\":\"What are big data analysis techniques?\",\"className\":\"AccordionTextItem_item__adF2E AccordionTextItem_item__adF2E\",\"children\":[[\"$\",\"$L31b\",null,{\"className\":\"AccordionTextItem_trigger__CiZ_J AccordionTextItem_trigger__CiZ_J\",\"children\":[false,[\"$\",\"span\",null,{\"children\":\"What are big data analysis techniques?\"}]]}],[\"$\",\"$L31c\",null,{\"className\":\"AccordionTextItem_content__G0vl3 AccordionTextItem_content__G0vl3\",\"children\":[\"$\",\"p\",null,{\"children\":\"Big data analysis techniques allow companies to collect, process, and analyze high-volume, high-velocity data sets in order to inform business decisions. Data sets used in big data analytics can come from multiple sources, including user data from web and mobile devices, as well as social media platforms.⁹\"}]}]]}],[\"$\",\"$L31a\",null,{\"value\":\"Can I get a discount if I enroll 10+ employees in data analysis courses?\",\"className\":\"AccordionTextItem_item__adF2E AccordionTextItem_item__adF2E\",\"children\":[[\"$\",\"$L31b\",null,{\"className\":\"AccordionTextItem_trigger__CiZ_J AccordionTextItem_trigger__CiZ_J\",\"children\":[false,[\"$\",\"span\",null,{\"children\":\"Can I get a discount if I enroll 10+ employees in data analysis courses?\"}]]}],[\"$\",\"$L31c\",null,{\"className\":\"AccordionTextItem_content__G0vl3 AccordionTextItem_content__G0vl3\",\"children\":[\"$\",\"p\",null,{\"children\":[\"Yes. edX For Business offers subscription packages and volume discounts to cost-effectively upskill your employees and support your company culture of learning. An edX For Business curriculum adviser can curate online learning experiences to meet your workforce learning and development challenges. \",[\"$\",\"a\",\"1\",{\"className\":\"text-link underline\",\"href\":\"https://business.edx.org/business\",\"children\":\"Click here\"}],\" to begin your custom curation and learn about volume discounts.\"]}]}]]}]]}]]}]],[\"$\",\"$L317\",null,{\"id\":\"$undefined\",\"children\":[\"$\",\"div\",null,{\"id\":\"\",\"children\":[[\"$\",\"p\",\"0\",{\"children\":\"Sources\"}],[\"$\",\"p\",\"1\",{\"children\":[\"¹\",[\"$\",\"a\",\"1\",{\"className\":\"text-link underline external\",\"href\":\"https://www.oracle.com/business-analytics/data-analytics/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"children\":\"What is Data Analytics?\"}],[\"$\",\"i\",\"2\",{\"children\":\" Oracle.\"}],\" Retrieved October 18, 2022. \"]}],[\"$\",\"p\",\"2\",{\"children\":[\"²\",[\"$\",\"a\",\"1\",{\"className\":\"text-link underline external\",\"href\":\"https://towardsdatascience.com/comparison-of-data-analysis-tools-excel-r-python-and-bi-tools-6c4685a8ea6f\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"children\":\"Comparison of Data Analysis Tools: Excel, R, Python and BI Tools\"}],\". (2019). \",[\"$\",\"i\",\"3\",{\"children\":\"Towards Data Science.\"}],\" Retrieved October 19, 2022. \"]}],[\"$\",\"p\",\"3\",{\"children\":[\"³\",[\"$\",\"a\",\"1\",{\"className\":\"text-link underline external\",\"href\":\"https://www.ibm.com/analytics/big-data-analytics\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"children\":\"Big Data Analytics\"}],\". \",[\"$\",\"i\",\"3\",{\"children\":\"IBM. \"}],\"Retrieved October 18, 2022.\"]}],[\"$\",\"p\",\"4\",{\"children\":[\"⁴\",[\"$\",\"a\",\"1\",{\"className\":\"text-link underline external\",\"href\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1122955/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"children\":\"Bioinformatics\"}],\". (2002). \",[\"$\",\"i\",\"3\",{\"children\":\"Science, medicine, and the future.\"}],\" Retrieved October 18, 2022. \"]}],[\"$\",\"p\",\"5\",{\"children\":[\"⁵\",[\"$\",\"a\",\"1\",{\"className\":\"text-link underline external\",\"href\":\"https://www.bls.gov/ooh/math/operations-research-analysts.htm#tab-2\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"children\":\"What Operations Research Analysts Do\"}],\". (2022). \",[\"$\",\"i\",\"3\",{\"children\":\"Bureau of Labor Statistics.\"}],\" Retrieved October 18, 2022. \"]}],[\"$\",\"p\",\"6\",{\"children\":[\"⁶\",[\"$\",\"a\",\"1\",{\"className\":\"text-link underline external\",\"href\":\"https://www.bls.gov/ooh/business-and-financial/market-research-analysts.htm#tab-2\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"children\":\"What Market Research Analysts Do\"}],\". (2022). \",[\"$\",\"i\",\"3\",{\"children\":\"Bureau of Labor Statistics.\"}],\" Retrieved October 18, 2022. \"]}],[\"$\",\"p\",\"7\",{\"children\":[\"⁷\",[\"$\",\"a\",\"1\",{\"className\":\"text-link underline external\",\"href\":\"https://www.python.org/doc/essays/blurb/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"children\":\"What is Python?\"}],\" (2022). \",[\"$\",\"i\",\"3\",{\"children\":\"Python\"}],\". Retrieved July 5, 2022. \"]}],[\"$\",\"p\",\"8\",{\"children\":[\"⁸\",[\"$\",\"a\",\"1\",{\"className\":\"text-link underline external\",\"href\":\"https://www.sas.com/en_us/insights/analytics/predictive-analytics.html\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"children\":\"Predictive Analytics: What It Is and Why It Matters\"}],\". \",[\"$\",\"i\",\"3\",{\"children\":\"SAS.\"}],\" Retrieved October 18, 2022. \"]}],[\"$\",\"p\",\"9\",{\"children\":[\"⁹\",[\"$\",\"a\",\"1\",{\"className\":\"text-link underline external\",\"href\":\"https://azure.microsoft.com/en-us/resources/cloud-computing-dictionary/what-is-big-data-analytics/#importance-of-data-analytics\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer\",\"children\":\"What is big data analytics?\"}],[\"$\",\"i\",\"2\",{\"children\":\" Microsoft Azure.\"}],\" Retrieved October 19, 2022. \"]}],[\"$\",\"p\",\"10\",{\"children\":\"$undefined\"}]]}]}]]}]]}]}]]}],[\"$\",\"div\",null,{\"className\":\"bg-primary 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Enroll to learn more, complete the course and claim your badge!\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis course provides students with the fundamental knowledge required to use Excel spreadsheets to perform basic data analysis.The course consists of several videos, demos, examples, and hands-on labs to help you learn, and ends with a final assignment project which will help you put what you have learned into practice.\u003c/p\u003e\n\u003cp\u003eExcel is an important tool when working with data - whether it is for business, marketing, research, or data analysis purposes. This course is targeted at those people who have ambitions in Data Analytics or Data Scientist roles, as well as those who just have a need to use Excel to perform data analysis in their own company or environment.\u003c/p\u003e\n\u003cp\u003eLearners will gain useful experience in the cleaning and wrangling of data using functions, and in analyzing data using methods such as sorting, filtering, and pivot tables. This course starts with an introduction to spreadsheet applications such as Microsoft Excel and Google Sheets and discusses importing data from multiple different formats. During this introduction you will learn to carry out some basic-level data wrangling and data cleaning tasks and then you will expand your knowledge of data analysis via the use of sorting, filtering, and pivot tables in a spreadsheet.\u003c/p\u003e\n\u003cp\u003eThe emphasis is on applied learning and hands-on practice in this course, and with each hands-on lab, you will gain further experience in the manipulation and handling of data and start to understand the important role that spreadsheets can perform in a data analysis environment. The final assignment project will allow you to apply these newly acquired skills to analyze data in a business scenario. By the end of this course, you will have worked with several data sets and spreadsheets and demonstrated the basics of cleaning and analyzing data all without having to learn any code.\u003c/p\u003e\n\u003cp\u003eThis course makes it simple to get started using Excel for data analysis, as it does not require any previous spreadsheet or code-writing experience. The course also does not require you to perform any software downloads or installations. All that is required is a device with a modern web browser, and the ability to use (or create) a Microsoft account to access Excel online at no-cost. Although the hands-on labs steps are specifically related to using ‘Excel for the web’, if you already have the full desktop version of Excel, you should be able to use that to follow along quite easily with the labs.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"343:T694,\u003cp\u003eEvery single minute, computers across the world collect millions of gigabytes of data. What can you do to make sense of this mountain of data? How do data scientists use this data for the applications that power our modern world?\u003c/p\u003e\n\u003cp\u003eData science is an ever-evolving field, using algorithms and scientific methods to parse complex data sets. Data scientists use a range of programming languages, such as Python and R, to harness and analyze data. This course focuses on using Python in data science. By the end of the course, you’ll have a fundamental understanding of machine learning models and basic concepts around Machine Learning (ML) and Artificial Intelligence (AI).\u003c/p\u003e\n\u003cp\u003eUsing Python, learners will study regression models (Linear, Multilinear, and Polynomial) and classification models (kNN, Logistic), utilizing popular libraries such as sklearn, Pandas, matplotlib, and numPy. The course will cover key concepts of machine learning such as: picking the right complexity, preventing overfitting, regularization, assessing uncertainty, weighing trade-offs, and model evaluation. Participation in this course will build your confidence in using Python, preparing you for more advanced study in Machine Learning (ML) and Artificial Intelligence (AI), and advancement in your career.\u003c/p\u003e\n\u003cp\u003eLearners must have a minimum baseline of programming knowledge (preferably in Python) and statistics in order to be successful in this course. Python prerequisites can be met with an introductory Python course offered through CS50’s Introduction to Programming with Python, and statistics prerequisites can be met via Fat Chance or with Stat110 offered through HarvardX.\u003c/p\u003e344:T47f,\u003cp\u003eWant to study for an MBA but unsure of the basic data analysis still required? This online course prepares you for studying in an MBA program and in business generally.\u003c/p\u003e\n\u003cp\u003eData analysis appears throughout any rigorous MBA program and in today’s business environment understanding the fundamentals of collecting, presenting, describing an"])</script><script>self.__next_f.push([1,"d making inferences from data sets is essential for success.\u003c/p\u003e\n\u003cp\u003eThe goal of this course is to teach you fundamental data analysis skills so you are prepared for your MBA study and able to focus your efforts on core MBA curriculum, rather than continually playing catch-up with the underlying statistical knowledge needed.\u003c/p\u003e\n\u003cp\u003eWe also hope that learning these data analysis skills will equip you with the ability to understand, to a greater degree, the data you encounter in your working lives and in the world around you - an essential life-skill in today’s data driven environment\u003c/p\u003e\n\u003cp\u003eThis course assumes no prior knowledge of data analysis. Concepts are explained as clearly as possible and regular activities give you the opportunity to practice your skills and improve your confidence.\u003c/p\u003e345:T482,\u003cp\u003ePerhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors. \u003c/p\u003e\n\u003cp\u003eIn this course,part ofour\u003ca href=\"https://www.edx.org/professional-certificate/harvardx-data-science\"\u003eProfessional Certificate Program in Data Science\u003c/a\u003e, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system. \u003c/p\u003e\n\u003cp\u003eYou will learn about training data, and how to use a set of data to discover potentially predictive relationships. As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning.\u003c/p\u003e346:T4f0,\u003cp\u003e\u003cem\u003ePlease Note: Learners who successfully complete this IBM course "])</script><script>self.__next_f.push([1,"can earn a skill badge —a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eKickstart your learning of Python for data science, as well as programming in general with this introduction to Python course. \u003cspan lang=\"EN\"\u003eThis beginner-friendly Python course will quickly take you from zero to programming in Python in a matter of hours and give you a taste of how to start working with data in Python. ~~~~\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eUpon its completion, you'll be able to write your own Python scripts and perform basic hands-on data analysis using our Jupyter-based lab environment. If you want to learn Python from scratch, this course is for you.\u003c/p\u003e\n\u003cp\u003eYou can start creating your own data science projects and collaborating with other data scientists using \u003ca href=\"http://cocl.us/PythonforDataScienceMainPage\"\u003eIBM Watson Studio\u003c/a\u003e. When you sign up, you will receive free access to Watson Studio. Start now and take advantage of this platform and learn the basics of programming, machine learning, and data visualization with this introductory course.\u003c/p\u003e347:T5be,\u003cp\u003eThe first in our \u003ca href=\"https://www.edx.org/professional-certificate/harvardx-data-science\" title=\"Harvard's Professional Certificate Program in Data Science\"\u003eProfessional Certificate Program in Data Science\u003c/a\u003e, this course will introduce you to the basics of R programming. You can better retain R when you learn it to solve a specific problem, so you'll use a real-world dataset about crime in the United States. You will learn the R skills needed to answer essential questions about differences in crime across the different states. \u003c/p\u003e\n\u003cp\u003eWe'll cover R's functions and data types, then tackle how to operate on vectors and when to use advanced functions like sorting. You'll learn how to apply general programming features like \"if-else,\" and \"for loop\" commands, and how to wrangle, analyze and visualize data. \u003c/p\u003e\n\u003cp\u003eRather than cov"])</script><script>self.__next_f.push([1,"ering every R skill you might need, you'll build a strong foundation to prepare you for the more in-depth courses later in the series, where we cover concepts like probability, inference, regression, and machine learning. We help you develop a skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux, version control with git and GitHub, and reproducible document preparation with RStudio. \u003c/p\u003e\n\u003cp\u003eThe demand for skilled data science practitioners is rapidly growing, and this series prepares you to tackle real-world data analysis challenges.\u003c/p\u003e348:T49f,\u003cp\u003eAs part of our \u003ca href=\"https://www.edx.org/professional-certificate/harvardx-data-science\"\u003eProfessional Certificate Program in Data Science\u003c/a\u003e, this course covers the basics of data visualization and exploratory data analysis. We will use three motivating examples and ggplot2, a data visualization package for the statistical programming language R. We will start with simple datasets and then graduate to case studies about world health, economics, and infectious disease trends in the United States. \u003c/p\u003e\n\u003cp\u003eWe'll also be looking at how mistakes, biases, systematic errors, and other unexpected problems often lead to data that should be handled with care. The fact that it can be difficult or impossible to notice a mistake within a dataset makes data visualization particularly important. \u003c/p\u003e\n\u003cp\u003eThe growing availability of informative datasets and software tools has led to increased reliance on data visualizations across many areas. Data visualization provides a powerful way to communicate data-driven findings, motivate analyses, and detect flaws. This course will give you the skills you need to leverage data to reveal valuable insights and advance your career.\u003c/p\u003e349:T743,\u003cp\u003e\u003cem\u003eIf you have specific questions about this course, please contact us at\u003c/em\u003e \u003cem\u003e\u003ca href=\"mailto:sds-mm@mit.edu\"\u003esds-mm@mit.edu\u003c/a\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData science requires multi-disciplinary skills ranging from mathematics"])</script><script>self.__next_f.push([1,", statistics, machine learning, problem solving to programming, visualization, and communication skills. In this course, learners will combine these foundational and practical skills with domain knowledge to ask and answer questions using real data.\u003c/p\u003e\n\u003cp\u003eThis course will start with a review of common statistical and computational tools such as hypothesis testing, regression, and gradient descent methods. Then, learners will study common models and methods to analyze specific types of data in four different domain areas:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEpigenetic Codes and Data Visualization\u003c/li\u003e\n\u003cli\u003eCriminal Networks and Network Analysis\u003c/li\u003e\n\u003cli\u003ePrices, Economics and Time Series\u003c/li\u003e\n\u003cli\u003eEnvironmental Data and Spatial Statistics\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLearners will be guided to analyze a real data set from each of these areas of focus, and present their findings in written reports. They will also discuss relevant and practical issues with peers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis course is part of the MITx MicroMasters Program in Statistics and Data Science. It is at a similar pace and level of rigor as an on-campus course at MIT. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit\u003c/strong\u003e \u003cstrong\u003e\u003ca href=\"https://micromasters.mit.edu/ds/\"\u003ehttps://micromasters.mit.edu/ds/\u003c/a\u003e.\u003c/strong\u003e\u003c/p\u003e34a:Tc54,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIf you’re interested in data analysis and interpretation, then this is the data science course for you. We start by learning the mathematical definition of distance and use this to motivate the use of the singular value decomposition (SVD) for dimension reduction of high-dimensional data sets, and multi-dimensional scaling and its connection to principle component analysis. We will learn about the \u003cem\u003ebatch effect,\u003c/em\u003e the most challenging data analytical problem in genomics today, and describe how the techniques can be used to detect and adjust for batch effects. Specifically, we will describe the principal component analysis and factor analysis and demonstrate how these concepts are applied to data visualization and data analysis of high-throughput experimental data.\u003c/p\u003e\n\u003cp\u003eFinally, we give a brief introduction to machine learning and apply it to high-throughput, large-scale data. We describe the general idea behind clustering analysis and descript K-means and hierarchical clustering and demonstrate how these are used in genomics and describe prediction algorithms such as k-nearest neighbors along with the concepts of training sets, test sets, error rates and cross-validation.\u003c/p\u003e\n\u003cp\u003eGiven the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.\u003c/p\u003e\n\u003cp\u003eThese courses make up two Professional Certificates and are self-paced:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis for Life Sciences:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistics-and-r\"\u003ePH525.1x: Statistics and R for the Life Sciences\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-linear-models-and-matrix-algebra\"\u003ePH525.2x: Introduction to Linear Models and Matrix Algebra\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistical-inference-and-modeling-for-high-throug\"\u003ePH525.3x: Statistical Inference and Modeling for High-throughput Experiments\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/high-dimensional-data-analysis\"\u003ePH525.4x: High-Dimensional Data Analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eGenomics Data Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-bioconductor-annotation-and-analys\"\u003ePH525.5x: Introduction to Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/case-studies-in-functional-genomics\"\u003ePH525.6x: Case Studies in Functional Genomics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/advanced-bioconductor\"\u003ePH525.7x: Advanced Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis class was supported in part by NIH grant R25GM114818.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"34b:T42b,\u003cp\u003eStatistical inference and modeling are indispensable for analyzing data affected by chance, and thus essential for data scientists. In this course, you will learn these key concepts through a motivating case study on election forecasting. \u003c/p\u003e\n\u003cp\u003eThis course will show you how inference and modeling can be applied to develop the statistical approaches that make polls an effective tool and we'll show you how to do this using R. You will learn concepts necessary to define estimates and margins of errors and learn how you can use these to make predictions relatively well and also provide an estimate of the precision of your forecast. \u003c/p\u003e\n\u003cp\u003eOnce you learn this you will be able to understand two concepts that are ubiquitous in data science: confidence intervals, and p-values. Then, to understand statements about the probability of a candidate winning, you will learn about Bayesian modeling. Finally, at the end of the course, we will put it all together to recreate a simplified version of an election forecast model and apply it to the 2016 election.\u003c/p\u003e34c:T47c,\u003cp\u003eLinear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding. This course, part ofour\u003ca href=\"https://www.edx.org/professional-certificate/harvardx-data-science\"\u003eProfessional Certificate Program in Data Science\u003c/a\u003e, covers how to implement linear regression and adjust for confounding in practice using R. \u003c/p\u003e\n\u003cp\u003eIn data science applications, it is very common to be interested in the relationship between two or more variables. The motivating case study we examine in this course relates to the data-driven approach used to construct baseball teams described in Moneyball. We will try to determine which measured outcomes best predict baseball runs by using linear regression. \u003c/p\u003e\n\u003cp\u003eWe will also examine confounding, where extraneous variables affect the relationship between two or more other variables, leading to spurious associations. Linear regression is a powerful technique"])</script><script>self.__next_f.push([1," for removing confounders, but it is not a magical process. It is essential to understand when it is appropriate to use, and this course will teach you when to apply this technique.\u003c/p\u003e34d:T418,\u003cp\u003eIn this course, part of our \u003ca href=\"https://www.edx.org/professional-certificate/harvardx-data-science\"\u003eProfessional Certificate Program in Data Science\u003c/a\u003e,we cover several standard steps of the data wrangling process like importing data into R, tidying data, string processing, HTML parsing, working with dates and times, and text mining. Rarely are all these wrangling steps necessary in a single analysis, but a data scientist will likely face them all at some point. \u003c/p\u003e\n\u003cp\u003eVery rarely is data easily accessible in a data science project. It's more likely for the data to be in a file, a database, or extracted from documents such as web pages, tweets, or PDFs. In these cases, the first step is to import the data into R and tidy the data, using the tidyverse package. The steps that convert data from its raw form to the tidy form is called data wrangling. \u003c/p\u003e\n\u003cp\u003eThis process is a critical step for any data scientist. Knowing how to wrangle and clean data will enable you to make critical insights that would otherwise be hidden.\u003c/p\u003e34e:T51a,\u003cp\u003eIn the information age, data is all around us. Within this data are answers to compelling questions across many societal domains (politics, business, science, etc.). But if you had access to a large dataset, would you be able to find the answers you seek?\u003c/p\u003e\n\u003cp\u003eThis course, part of the Data Science MicroMasters program, will introduce you to a collection of powerful, open-source, tools needed to analyze data and to conduct data science. Specifically, you'll learn how to use:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epython\u003c/li\u003e\n\u003cli\u003ejupyter notebooks\u003c/li\u003e\n\u003cli\u003epandas\u003c/li\u003e\n\u003cli\u003enumpy\u003c/li\u003e\n\u003cli\u003ematplotlib\u003c/li\u003e\n\u003cli\u003egit\u003c/li\u003e\n\u003cli\u003eand many other tools.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou will learn these tools all within the context of solving compelling data science problems.\u003c/p\u003e\n\u003cp\u003eAfter completing this course, you'll be able to find an"])</script><script>self.__next_f.push([1,"swers within large datasets by using python tools to import data, explore it, analyze it, learn from it, visualize it, and ultimately generate easily sharable reports.\u003c/p\u003e\n\u003cp\u003eBy learning these skills, you'll also become a member of a world-wide community which seeks to build data science tools, explore public datasets, and discuss evidence-based findings. Last but not least, this course will provide you with the foundation you need to succeed in later courses in the Data Science MicroMasters program.\u003c/p\u003e34f:T7b5,\u003cp\u003eModern systems today must be designed for agility in order to outpace the competition. Concepts like Agile, DevOps, and Data Science were once considered only for the technology-based companies. Today that means every company. Because there is no greater currency than timely information for optimizing operations and meeting the needs of customers.\u003c/p\u003e\n\u003cp\u003eModern product management requires that every development and operations value stream is identified and continuously improved. This means using Lean and DevOps principles to streamline handoffs and information flows across teams. It means reorienting towards self-service and automation wherever possible. And to avoid incrementalism, it means a robust Agile development process to keep innovations important and aggressive enough to make noticeable improvements in value delivery.\u003c/p\u003e\n\u003cp\u003eAgile systems in a DevOps environment requires that products are built completely differently from a traditional designs. Modularity, open set architectures, and flexible data management paradigms are a starting point. The evolutionary nature of the product with so much change enables functionality, design, and technology to drive and influence each other simultaneously. And beneath it all is a data collection and feedback loop essential for anticipating and reacting to business needs both for operations and marketing.\u003c/p\u003e\n\u003cp\u003eData science and analytics are the lifeblood of any product organization, and enable product managers to tackle risks early. Luckily, new technologie"])</script><script>self.__next_f.push([1,"s allow us to collect and integrate data without extreme upfront constraints and onerous controls. This means all data is fair game, and when tagged and stored properly, can be made available at nearly any scale for preparation, visualization, analysis, and modeling.\u003c/p\u003e\n\u003cp\u003eWe’ll teach you the paradigms, processes, and introduce some key technologies that make the data-driven product organization the optimal competitor in the market.\u003c/p\u003e350:Tb9f,"])</script><script>self.__next_f.push([1,"\u003cp\u003eResearch has been traditionally viewed as a purely academic undertaking, especially in limited-resource healthcare systems. Clinical trials, the hallmark of medical research, are expensive to perform, and take place primarily in countries which can afford them. Around the world, the blood pressure thresholds for hypertension, or the blood sugar targets for patients with diabetes, are established based on research performed in a handful of countries. There is an implicit assumption that the findings and validity of studies carried out in the US and other Western countries generalize to patients around the world.\u003c/p\u003e\n\u003cp\u003eThis course was created by members of MIT Critical Data, a global consortium that consists of healthcare practitioners, computer scientists, and engineers from academia, industry, and government, that seeks to place data and research at the front and center of healthcare operations.\u003c/p\u003e\n\u003cp\u003eBig data is proliferating in diverse forms within the healthcare field, not only because of the adoption of electronic health records, but also because of the growing use of wireless technologies for ambulatory monitoring. The world is abuzz with applications of data science in almost every field – commerce, transportation, banking, and more recently, healthcare. These breakthroughs are due to rediscovered algorithms, powerful computers to run them, and most importantly, the availability of bigger and better data to train the algorithms. This course provides an introductory survey of data science tools in healthcare through several hands-on workshops and exercises.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWho this course is aimed at\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe most daunting global health issues right now are the result of interconnected crises. In this course, we highlight the importance of a multidisciplinary approach to health data science. It is intended for front-line clinicians and public health practitioners, as well as computer scientists, engineers and social scientists, whose goal is to understand health and disease better using digital data captured in the process of care.\u003c/p\u003e\n\u003cp\u003eWe highly recommend that this course be taken as part of a team consisting of clinicians and computer scientists or engineers. Learners from the healthcare sector are likely to have difficulties with the programming aspect while the computer scientists and engineers will not be familiar with the clinical context of the exercises and workshops.\u003c/p\u003e\n\u003cp\u003eThe MIT Critical Data team would like to acknowledge the contribution of the following members: Aldo Arevalo, Alistair Johnson, Alon Dagan, Amber Nigam, Amelie Mathusek, Andre Silva, Chaitanya Shivade, Christopher Cosgriff, Christina Chen, Daniel Ebner, Daniel Gruhl, Eric Yamga, Grigorich Schleifer, Haroun Chahed, Jesse Raffa, Jonathan Riesner, Joy Tzung-yu Wu, Kimiko Huang, Lawerence Baker, Marta Fernandes, Mathew Samuel, Philipp Klocke, Pragati Jaiswal, Ryan Kindle, Shrey Lakhotia, Tom Pollard, Yueh-Hsun Chuang, Ziyi Hou.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"351:T871,"])</script><script>self.__next_f.push([1,"\u003cp\u003eToday the principles and techniques of reproducible research are more important than ever, across diverse disciplines from astrophysics to political science. No one wants to do research that can’t be reproduced. Thus, this course is really for anyone who is doing any data intensive research. While many of us come from a biomedical background, this course is for a broad audience of data scientists. \u003c/p\u003e\n\u003cp\u003eTo meet the needs of the scientific community, this course will examine the fundamentals of methods and tools for reproducible research. Led by experienced faculty from the Harvard T.H. Chan School of Public Health, you will participate in six modules that will include several case studies that illustrate the significant impact of reproducible research methods on scientific discovery. \u003c/p\u003e\n\u003cp\u003eThis course will appeal to students and professionals in biostatistics, computational biology, bioinformatics, and data science. The course content will blend video lectures, case studies, peer-to-peer engagements and use of computational tools and platforms (such as R/RStudio, and Git/Github), culminating in a final presentation of a final reproducible research project. \u003c/p\u003e\n\u003cp\u003eWe’ll cover Fundamentals of Reproducible Science; Case Studies; Data Provenance; Statistical Methods for Reproducible Science; Computational Tools for Reproducible Science; and Reproducible Reporting Science. These concepts are intended to translate to fields throughout the data sciences: physical and life sciences, applied mathematics and statistics, and computing. \u003c/p\u003e\n\u003cp\u003eConsider this course a survey of best practices: we’d like to make you aware of pitfalls in reproducible data science, some failure - and success - stories in the past, and tools and design patterns that might help make it all easier. But ultimately it’ll be up to you to take the skills you learn from this course to create your own environment in which you can easily carry out reproducible research, and to encourage and integrate with similar environments for your collaborators and colleagues. We look forward to seeing you in this course and the research you do in the future!\u003c/p\u003e"])</script><script>self.__next_f.push([1,"352:T50f,\u003cp\u003e\u003cem\u003ePlease Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eDespite and influx in computing power and access to data over the last couple of decades, our ability to use data within the decision-making process is either lost or not maximized all too often. We do not have a strong grasp of the questions asked and how to apply the data correctly to resolve the issues at hand.\u003c/p\u003e\n\u003cp\u003eThe purpose of this course is to share the methods, models and practices that can be applied within data science, to ensure that the data used in problem-solving is relevant and properly manipulated to address business and real-world challenges.\u003c/p\u003e\n\u003cp\u003eYou will learn how to identify a problem, collect and analyze data, build a model, and understand the feedback after model deployment.\u003c/p\u003e\n\u003cp\u003eAdvancing your ability to manage, decipher and analyze new and big data is vital to working in data science. 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You will then analyze your experimental data.\u003c/p\u003e\n\u003cp\u003eWhy take this course?\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eTo get hands on design experience with integrated optics\u003c/li\u003e\n\u003cli\u003eTo learn how to use advanced optical design tools\u003c/li\u003e\n\u003cli\u003eTo get your design fabricated, and obtain experimental data\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe focus of this course is a design project, guided by lectures, tutorials and activities. As a first-time designer, you will design an interferometer, which is a widely used device in many applications such as communications (modulation, switching) and sensing. Specifically, it is Mach-Zehnder Interferometer, consisting of fibre grating couplers, two splitters, and optical waveguides. 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For example, you’ll learn how to:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eUse \u003cstrong\u003eregression analysis\u003c/strong\u003e to predict future sales based on historical data.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eImplement \u003cstrong\u003ecluster analysis\u003c/strong\u003e to segment customers and develop personalized marketing strategies.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eApply \u003cstrong\u003etime series analysis\u003c/strong\u003e to forecast product demand and optimize inventory management.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese practical applications ensure that you’re not just learning theory, but gaining hands-on experience that will help you excel in your career.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"35a:T56f,\u003cp\u003eIn this course, you will learn about some of the advanced skills you will need for real-world healthcare data analysis. You will continue to practice these skills using the statistical programming software called R and examples from the healthcare industry. The topics covered in this course will help you to engage in the more advanced data wrangling that is often necessary for data analysis and to make data-informed decisions in the healthcare field. While the course focuses on application and the use of these statistical methods, there is some discussion of the mathematical underpinning, relevant formulae, and assumptions necessary for understanding the application of statistical methods. \u003c/p\u003e\n\u003cp\u003eThis self-paced course is comprised of written content, video content, step-by-step follow-along activities, and assessments to reinforce your learning (Assessments available to Verified Track learners only). \u003c/p\u003e\n\u003cp\u003eThe course is comprised of 6 modules that you should complete in order, as each subsequent module builds on the previous one. \u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eModule 1: Causal Inference and Tools for Model Specification\u003c/li\u003e\n\u003cli\u003eModule 2: Matching to Reduce Model Dependence\u003c/li\u003e\n\u003cli\u003eModule 3: Simpson's Paradox and Fixed Effects\u003c/li\u003e\n\u003cli\u003eModule 4: Random Effects\u003c/li\u003e\n\u003cli\u003eModule 5: Repeated Measures and Longitudinal Data\u003c/li\u003e\n\u003cli\u003eModule 6: Missing Data and Bootstrapping\u003c/li\u003e\n\u003c/ul\u003e35b:T450,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eData science tools have revolutionized the way farmers and agricultural professionals approach their work. Python data analysis tools, such as pandas and seaborn, enable farmers to make data-driven decisions using soil, water, and economic data accounts. Pandas is a Python library used to simplify handling large sets of data. Seaborn is a data visualization library used to quickly create graphs.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis hands-on guided project will prepare you to handle agricultural datasets using these Python tools. Y\u003cspan lang=\"EN-US\"\u003eou will develop job-ready skills, like how to download, prepare, analyze, and vi"])</script><script>self.__next_f.push([1,"sualize data using Python libraries, including pandas and seaborn. You will learn how to build a trend line in order to forecast future trends, and finally, you will learn how to create interactive maps which show data change over time.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eYou will be provided with access to a Cloud-based IDE, which has all of the required software, including Python, pre-installed. All you need is a recent version of a modern web browser to complete this project.\u003c/p\u003e35c:T6e3,\u003cp\u003eThis Statistics for Data Science course is designed to introduce you to the basic principles of statistical methods and procedures used for data analysis. After completing this course you will have practical knowledge of crucial topics in statistics including - data gathering, summarizing data using descriptive statistics, displaying and visualizing data, examining relationships between variables, probability distributions, expected values, hypothesis testing, introduction to ANOVA (analysis of variance), regression and correlation analysis. You will take a hands-on approach to statistical analysis using Python and Jupyter Notebooks – the tools of choice for Data Scientists and Data Analysts.\u003c/p\u003e\n\u003cp\u003eAt the end of the course, you will complete a project to apply various concepts in the course to a Data Science problem involving a real-life inspired scenario and demonstrate an understanding of the foundational statistical thinking and reasoning. The focus is on developing a clear understanding of the different approaches for different data types, developing an intuitive understanding, making appropriate assessments of the proposed methods, using Python to analyze our data, and interpreting the output accurately. This course is suitable for a variety of professionals and students intending to start their journey in data and statistics-driven roles such as Data Scientists, Data Analysts, Business Analysts, Statisticians, and Researchers. It does not require any computer science or statistics background. We strongly recommend taking the Python for "])</script><script>self.__next_f.push([1,"Data Science course before starting this course to get familiar with the Python programming language, Jupyter notebooks, and libraries. An optional refresher on Python is also provided.\u003c/p\u003e35d:T750,\u003cp\u003eData Science along with artificial intelligence (AI) and its various components such as statistical learning (SL), machine learning (ML) and deep learning algorithms (DL) are recognized as main drivers of organizational value creation. According to Dr Jim Gray, Data Science is the fourth paradigm which drives innovative solutions to organizational problems.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIn this course we will start with basic concepts in probability such as joint and conditional probabilities. We will discuss the implementation of these concepts in ML algorithms for Market Basket Analysis and Recommender Systems. After covering basic probability concepts, we move on to random variables, discrete and continuous probability distributions, sampling, estimation and central limit theorem.\u003c/p\u003e\n\u003cp\u003eAn important step in ML model building is feature selection to avoid overfitting and underfitting. ML models such as regression and logistic regression use hypothesis testing to select features. We will discuss various hypothesis tests and how they are used in feature selection. \u003c/p\u003e\n\u003cp\u003eEvery ML model has an optimization stage, either to fine-tune the feature weights, or to find an optimal set of features. We will discuss important optimization techniques, and algorithms such as Gradient Descent, that play an important role in AI and ML model development.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eData must be represented in a matrix for AI and ML model development. Matrix operations such as matrix inverse and multiplication are elementary steps in model development. These fundamental concepts in linear algebra will be discussed.\u003c/p\u003e\n\u003cp\u003eThis course is suitable for students/practitioners interested in improving their knowledge in the fundamental concepts of Data Science. The course will also prepare the learner for a career in the field "])</script><script>self.__next_f.push([1,"of Data Analytics.\u003c/p\u003e35e:T566,\u003cp\u003eThis course will introduce you to the world of data science and cover all the major aspects of deriving insights from data sets. In this course, we will show you how to acquire data, clean it for easier analysis, explore and derive insights, convert it into specific features, and model it using machine learning algorithms.\u003c/p\u003e\n\u003cp\u003eFinally you'll use insights and predictions from your models to make definitive statements about your data. This course will be presented almost entirely in Jupyter notebook form. Jupyter notebooks are one of the primary tools used by data scientists today. They integrate code data images, and interactive widgets in a seamless presentation format.\u003c/p\u003e\n\u003cp\u003eWe also include interactive notebooks that test and exercise every aspect of the data science. Rather than start with a large amount of theory. This course takes a top down approach towards teaching. We first start with the complete worked example, showing you the full flow of a useful data analysis skill.\u003c/p\u003e\n\u003cp\u003eAs we continue in the course, we'll unpack that example, going deeper and deeper into each component until you understand exactly what each line of code in the example is doing. By the end of the course, you'll be able to write the entire example from scratch on your own. We use this approach to give you a broad understanding about what each data science skill entails.\u003c/p\u003e35f:T5aa,\u003cp\u003eThe availability of low cost and ubiquitous sensors in city infrastructure provides high granular data at unprecedented spatiotemporal scales. “Smart Cities” envision to utilize this data to provide a healthy, happy and sustainable urban ecosystem by integrating the information and communication technology (ICT), Internet of things (IoT) and citizen participation to effectively manage and utilize city infrastructure and services. “Data Science” is an interdisciplinary field of scientific methods, processes, algorithms and systems to extract knowledge from data in various forms and provides fast and efficient"])</script><script>self.__next_f.push([1," understanding of the current dynamics of cities and ways to improve different services. This course will introduce scientific techniques that will allow the analysis, inference and prediction of large-scale data (e.g. GPS vehicular data, social media data, mobile phone data, individual social network data, etc.) that are present in city networks. Basics of the data science methods to analyze these datasets will be presented. The course will focus both on the methods and their application to smart-city problems. Python will be used to demonstrate the application of each method on datasets available to the instructor. Examples of problems that will be discussed include ridesharing platforms, smart and energy-efficient buildings, evacuation modeling, decision making during extreme events \u0026amp; urban resilience.\u003c/p\u003e360:T560,\u003cp\u003eConcern about the harmful effects of machine learning algorithms and big data AI models (bias and more) has resulted in greater attention to the fundamentals of data ethics. News stories appear regularly about credit algorithms that discriminate against women, medical algorithms that discriminate against African Americans, hiring algorithms that base decisions on gender, and more. In most cases, the data scientists who developed and deployed these decision making algorithms and data processes had no such intentions, and were unaware of the harmful impact of their work.\u003c/p\u003e\n\u003cp\u003eThis data science ethics course, the second in the data science ethics program for both practitioners and managers, provides guidance and practical tools to build better models, do better data analysis and avoid these problems. You’ll learn about ****\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eTools for model interpretability\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGlobal versus local model interpretability methods\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMetrics for model fairness\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAuditing your model for bias and fairness\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRemedies for biased models\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe course offers real world problems and datasets, a framework data scientists can use"])</script><script>self.__next_f.push([1," to develop their projects, and an audit process to follow in reviewing them. Case studies with ethical considerations, along with Python code, are provided.\u003c/p\u003e361:T49f,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eData science tools have revolutionized the way farmers and agricultural professionals approach their work. Python data analysis tools, such as pandas and seaborn, enable farmers to make data-driven decisions using soil, water, and economic data accounts. Pandas is a Python library used to simplify handling large sets of data. Seaborn is a data visualization library used to quickly create graphs. \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis hands-on guided project will prepare you to handle agricultural datasets using these Python tools. Y\u003cspan lang=\"EN-US\"\u003eou will develop job-ready skills, like how to download, prepare, analyze, and visualize data using Python libraries, including pandas and seaborn. You will learn how to build a trend line in order to forecast future trends, and finally, you will learn how to create interactive maps which show data change over time.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eYou will be provided with access to a Cloud-based IDE, which has all of the required software, including Python, pre-installed. All you need is a recent version of a modern web browser to complete this project.\u003c/p\u003e362:T12a1,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cstrong\u003eTo learn more about this MicroMasters program, please visit\u003ca href=\"https://micromasters.mit.edu/ds/\"\u003ehttps://micromasters.mit.edu/ds/\u003c/a\u003e.\u003cbr /\u003e\n\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis course is an assessment that tests your knowledge on the course content from \u003ca href=\"https://mitx-micromasters.zendesk.com/hc/en-us/articles/360035543812-What-is-14-310x-Data-Analysis-for-Social-Scientists-Is-it-a-prerequisite-for-14-310Fx-Data-Analysis-in-Social-Science-Do-I-have-to-pay-for-it-\"\u003e14.310x - Data Analysis for Social Scientists\u003c/a\u003e. Learners are eligible to take this assessment only if they have passed \u003ca href=\"https://mitx-micromasters.zendesk.com/hc/en-us/articles/360035543812-What-is-14-310x-Data-Analysis-for-Social-Scientists-Is-it-a-prerequisite-for-14-310Fx-Data-Analysis-in-Social-Science-Do-I-have-to-pay-for-it-\"\u003e14.310x - Data Analysis for Social Scientists\u003c/a\u003e. \u003cbr /\u003e\nAll learners who have become eligible within the past year have already been automatically enrolled for this assessment. If you are eligible but have not been enrolled or have not received an email notification, please contact us asap at \u003ca href=\"mailto:sds-mm@mit.edu\"\u003esds-mm@mit.edu\u003c/a\u003e with your edX username, along with a proof (learners record or screenshot of your progress page) that you have obtained 50% or above in \u003ca href=\"https://mitx-micromasters.zendesk.com/hc/en-us/articles/360035543812-What-is-14-310x-Data-Analysis-for-Social-Scientists-Is-it-a-prerequisite-for-14-310Fx-Data-Analysis-in-Social-Science-Do-I-have-to-pay-for-it-\"\u003e14.310x - Data Analysis for Social Scientists\u003c/a\u003e. You must upgrade to become a verified learner to take this assessment.\u003ca href=\"https://mitx-micromasters.zendesk.com/hc/en-us/articles/360035543812-What-is-14-310x-Data-Analysis-for-Social-Scientists-Is-it-a-prerequisite-for-14-310Fx-Data-Analysis-in-Social-Science-Do-I-have-to-pay-for-it-\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e14.310x - Data Analysis for Social Scientists is a statistics and data analysis course that will introduce you to the essential notions of probability and statistics. It will cover techniques in modern data analysis: estimation, regression and econometrics, prediction, experimental design, randomized control trials (and A/B testing), machine learning, and data visualization. It will illustrate these concepts with applications drawn from real world examples and frontier research. Finally, it will provide instruction for how to use the statistical package R and opportunities for students to perform self-directed empirical analyses.\u003c/p\u003e\n\u003cp\u003eThis assessment course should only be taken by learners who have completed and passed \u003cem\u003e\u003ca href=\"https://mitx-micromasters.zendesk.com/hc/en-us/articles/360035543812-What-is-14-310x-Data-Analysis-for-Social-Scientists-Is-it-a-prerequisite-for-14-310Fx-Data-Analysis-in-Social-Science-Do-I-have-to-pay-for-it-\"\u003e14.310x - Data Analysis for Social Scientists\u003c/a\u003e\u003c/em\u003e and intend to pursue the MicroMasters credential in Statistics and Data Science. To get credit in this MicroMasters program:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eEnroll in both this assessment course and the content course \u003cem\u003e\u003ca href=\"https://mitx-micromasters.zendesk.com/hc/en-us/articles/360035543812-What-is-14-310x-Data-Analysis-for-Social-Scientists-Is-it-a-prerequisite-for-14-310Fx-Data-Analysis-in-Social-Science-Do-I-have-to-pay-for-it-\"\u003e14.310x - Data Analysis for Social Scientists\u003c/a\u003e\u003c/em\u003e (Note: There is no additional fee to enroll in the content course),\u003c/li\u003e\n\u003cli\u003eComplete the content course 14.310x with a passing grade,\u003c/li\u003e\n\u003cli\u003eCome back to this course and take the exam to earn your verified certificate that will count toward the MicroMasters credential in Statistics and Data Science.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eThis assessment course, along with the content course \u003cem\u003e\u003ca href=\"https://mitx-micromasters.zendesk.com/hc/en-us/articles/360035543812-What-is-14-310x-Data-Analysis-for-Social-Scientists-Is-it-a-prerequisite-for-14-310Fx-Data-Analysis-in-Social-Science-Do-I-have-to-pay-for-it-\"\u003e14.310x - Data Analysis for Social Scientists\u003c/a\u003e\u003c/em\u003e , is part of the \u003ca href=\"https://www.edx.org/micromasters/mitx-statistics-and-data-science\"\u003eMITx MicroMasters Program in Statistics and Data Science\u003c/a\u003e. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit \u003ca href=\"https://micromasters.mit.edu/ds/\"\u003ehttps://micromasters.mit.edu/ds/\u003c/a\u003e.\u003c/strong\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"363:T796,\u003cp\u003eThe ability to analyze data is a powerful skill that helps you make better decisions. Microsoft Excel is one of the top tools for data analysis and the built-in pivot tables are arguably the most popular analytic tool.\u003c/p\u003e\r\n\u003cp\u003eIn this course, you will learn how to perform data analysis using Excel's most popular features. You will learn how to create pivot tables from a range with rows and columns in Excel. You will see the power of Excel pivots in action and their ability to summarize data in flexible ways, enabling quick exploration of data and producing valuable insights from the accumulated data.\u003c/p\u003e\r\n\u003cp\u003ePivots are used in many different industries by millions of users who share the goal of reporting the performance of companies and organizations. In addition, Excel formulas can be used to aggregate data to create meaningful reports. To complement, pivot charts and slicers can be used together to visualize data and create easy to use dashboards.\u003c/p\u003e\r\n\u003cp\u003eYou should have a basic understanding of creating formulas and how cells are referenced by rows and columns within Excel to take this course. If required, you can can find many help topics on Excel at the Microsoft Office Support Site. You are welcome to use any supported version of Excel you have installed in your computer, however, the instructions are based on Excel 2016. You may not be able to complete all exercises as demonstrated in the lectures but workarounds are provided in the lab instructions or Discussion forum. Please note that Excel for Mac does not support many of the features demonstrated in this course.\u003c/p\u003e\r\n\u003cp\u003eAfter taking this course you'll be ready to continue to our more advanced Excel course, \u003ca href=\"https://aka.ms/edx-dat206x-about\"\u003eAnalyzing and Visualizing Data with Excel\u003c/a\u003e.\u003c/p\u003e\r\n\u003cp\u003e\u003cstrong\u003e*Note:\u003c/strong\u003e *This course will retire at the end of October. Please enroll only if you are able to finish your coursework in time.\u003c/p\u003e364:T5d3,\u003cp\u003eThe volume of data generated daily is staggering—more than 2.5 quintillion bytes ev"])</script><script>self.__next_f.push([1,"ery day. As the data surge continues to grow exponentially, organizations and individuals alike need to understand how to process and analyze this information to create strategic advantage.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe CS50 Professional Certificate Program: Computer Science for Data Science explores the limitless potential of computer science converging with the analytical power of R programming. Beginning with CS50: Introduction to Computer Science, learners will complete an intensive and comprehensive dive into the core concepts of computer science developed by renowned Harvard University Professor David J. Malan. The course will cover concepts like abstraction, algorithms, and data structures and management—serving as a foundation for how data is used to improve decision-making and critical thinking skills.\u003c/p\u003e\r\n\r\n\u003cp\u003eThrough CS50’s Introduction to Programming with R, you will elevate your skills as you discover the statistical power of R using real-world datasets to manipulate data, create colorful visualizations, and package and export R code for reproducibility.\u003c/p\u003e\r\n\r\n\u003cp\u003eWhether you're a data enthusiast, a seasoned computing professional, or interested in entering the fastest-growing industry, this professional certificate program unravels the complexities of today’s data landscape, equipping you with the skills needed to create efficient, accurate, and actionable data insights.\u003c/p\u003e365:T461,\u003cp\u003eData science is an ever-evolving field, constantly iterating and innovating as technologies and algorithms improve. In order to drive your career forward, you must stay on the cutting-edge of the newest programming languages, such as Python, to stand out from the rest.\u003c/p\u003e\r\n\r\n\u003cp\u003eBased around three courses, this Professional Certificate in Learning Python for Data Science focuses on hands-on learning—putting your Python skills into practice for applied data science. Each course will build upon each other, preparing you to solve complex business challenges using coding and data analysis. No prior coding experience required to"])</script><script>self.__next_f.push([1," enjoy this program.\u003c/p\u003e\r\n\r\n\u003cp\u003eTaught by experts in the field, you will learn the foundations of Python programming and statistics before moving into more advanced learning around Python for machine learning and AI—all while building your quantitative reasoning and statistical skills. By combining these tools, you will not only become a more invaluable contributor to your team and organization, but you also will kickstart your career in the in-demand field of data science.\u003c/p\u003e366:T4c8,\u003cp\u003eThe demand for skilled data science practitioners in industry, academia, and government is rapidly growing. The HarvardX Data Science program prepares you with the necessary knowledge base and useful skills to tackle real-world data analysis challenges. The program covers concepts such as probability, inference, regression, and machine learning and helps you develop an essential skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with Unix/Linux, version control with git and GitHub, and reproducible document preparation with RStudio.\u003c/p\u003e\r\n\r\n\u003cp\u003eIn each course, we use motivating case studies, ask specific questions, and learn by answering these through data analysis. Case studies include: Trends in World Health and Economics, US Crime Rates, The Financial Crisis of 2007-2008, Election Forecasting, Building a Baseball Team (inspired by Moneyball), and Movie Recommendation Systems.\u003c/p\u003e\r\n\r\n\u003cp\u003eThroughout the program, we will be using the R software environment. You will learn R, statistical concepts, and data analysis techniques simultaneously. We believe that you can better retain R knowledge when you learn how to solve a specific problem.\u003c/p\u003e367:T8d0,"])</script><script>self.__next_f.push([1,"\u003cp\u003eHave you thought about a career in data science and machine learning but didn’t know where to start?\u003c/p\u003e \r\n\r\n\u003cp\u003eDemand for professionals in the machine learning (ML) and artificial intelligence (AI) space is growing exponentially, with no signs of slowing thanks to the ever changing data science landscape. In fact, as the availability of machine learning tools becomes more accessible, companies will begin adopting them at a higher rate – continuing to drive the demand of data science analysts and engineers, especially those with experience in programming languages like Python.\u003c/p\u003e \r\n\r\n\u003cp\u003eIndustries such as finance, health care, e-commerce, and technology will increasingly be reliant on data to drive strategic value and product and service innovation – leveraging data-driven insights to gain competitive advantage – and seeking experts in data analysis and machine learning techniques to meet growth goals.\u003c/p\u003e \r\n\r\n\u003cp\u003eThis comprehensive certificate program is designed to provide learners with the practical knowledge in machine learning and its applications to launch a successful career path or transition into data science and machine learning using Python. The program delves into various facets of data analysis, predictive modeling, and machine learning techniques, providing hands-on experience with industry-standard tools like sklearn, Pandas, matplotlib, and numPy; and in methodologies, including decision trees and ultimately more complex algorithms like gradient boosting.\u003c/p\u003e \r\n\r\n\u003cp\u003eBy the end of this certificate, learners will gain hands-on experience building and analyzing complex data sets using Python and machine learning, developing the skills to enter a robust job market with diverse opportunities.\u003c/p\u003e \r\n\r\n\u003cp\u003eLearners should have experience in Python and statistics in order to be successful in the course. You may wish to explore \u003ca href=\"https://www.edx.org/learn/python/harvard-university-cs50-s-introduction-to-programming-with-python\"\u003eCS50’s Introduction to Programming with Python\u003c/a\u003e and statistics prerequisites, which can be met via \u003ca href=\"https://www.edx.org/learn/probability/harvard-university-fat-chance-probability-from-the-ground-up\"\u003eFat Chance\u003c/a\u003e or Stat110 offered through HarvardX.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"368:T8cc,"])</script><script>self.__next_f.push([1,"\u003cp\u003eHave you thought about a career in data science and machine learning but didn’t know where to start?\u003c/p\u003e \r\n\r\n\u003cp\u003eDemand for professionals in the machine learning (ML) and artificial intelligence (AI) space is growing exponentially, with no signs of slowing thanks to the ever changing data science landscape. In fact, as the availability of machine learning tools becomes more accessible, companies will begin adopting them at a higher rate – continuing to drive the demand of data science analysts and engineers, especially those with experience in programming languages like Python.\u003c/p\u003e \r\n\r\n\u003cp\u003eIndustries such as finance, health care, e-commerce, and technology will increasingly be reliant on data to drive strategic value and product and service innovation – leveraging data-driven insights to gain competitive advantage – and seeking experts in data analysis and machine learning techniques to meet growth goals.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis comprehensive certificate program is designed to provide learners with the practical knowledge in machine learning and its applications to launch a successful career path or transition into data science and machine learning using Python. The program delves into various facets of data analysis, predictive modeling, and machine learning techniques, providing hands-on experience with industry-standard tools like sklearn, Pandas, matplotlib, and numPy; and in methodologies, including decision trees and ultimately more complex algorithms like gradient boosting.\u003c/p\u003e\r\n\r\n\u003cp\u003eBy the end of this certificate, learners will gain hands-on experience building and analyzing complex data sets using Python and machine learning, developing the skills to enter a robust job market with diverse opportunities.\u003c/p\u003e\r\n\r\n\u003cp\u003eLearners should have experience in Python and statistics in order to be successful in the course. You may wish to explore \u003ca href=\"https://www.edx.org/learn/python/harvard-university-cs50-s-introduction-to-programming-with-python\"\u003eCS50’s Introduction to Programming with Python\u003c/a\u003e and statistics prerequisites, which can be met via \u003ca href=\"https://www.edx.org/learn/probability/harvard-university-fat-chance-probability-from-the-ground-up\"\u003eFat Chance\u003c/a\u003e or Stat110 offered through HarvardX.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"369:T574,\u003cp\u003eThe demand for data scientists is projected to grow 10x faster than other occupations (Source: US Bureau of Labor Statistics). This IBM Data Science Professional Certificate gives you the job-ready skills and practical experience you need to start your career in data science and machine learning. No prior computer science or programming experience is required.\u003c/p\u003e\r\n\r\n\u003cp\u003eData scientists analyze and interpret complex, large datasets using data mining, machine learning, and predictive modeling techniques. They then seek to uncover patterns, trends, and insights that help businesses make informed decisions.\u003c/P\u003e\r\n\r\n\u003cp\u003eDuring this program, you’ll learn Python programming, SQL for database querying, data manipulation with Pandas and Numpy, data visualization with Matplotlib and Seaborn, and machine learning with Scikit-learn. You’ll work hands-on with data science tools like Jupyter Notebooks, RStudio, and IBM watsonx. You'll use GitHub for version control and access data sources with APIs. Plus, you’ll gain valuable practical skills through hands-on labs, course projects, and a capstone project you can put on your resume and talk about in interviews.\u003c/p\u003e\r\n\r\n\u003cp\u003eIf you’re looking to get started in data science, this program gives you the job-ready skills you need to catch the eye of an employer. Enroll today and look forward to kickstarting a highly rewarding career.\u003c/p\u003e36a:T10c8,"])</script><script>self.__next_f.push([1,"\u003cp\u003eData scientists bring value to organizations across industries because they are able to solve complex challenges with data and drive important decision-making processes. Not only is there a huge demand, but there is a significant shortage of qualified data scientists with 54% of the most rigorous data science positions requiring a degree higher than a bachelor’s.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis MicroMasters® program in Statistics and Data Science (SDS) was developed by MITx and the \u003ca href=\"https://idss.mit.edu/academics/micromasters-program-in-statistics-and-data-science-sds/\"\u003eMIT Institute for Data, Systems, and Society (IDSS)\u003c/a\u003e. It is a multidisciplinary approach comprised of four separate tracks with four online courses each and a virtually proctored exam. Each track focuses on a combination of methods-centered courses and domain analysis courses to provide you with foundational knowledge and hands-on training. All learners complete the Probability and Machine Learning courses, two other courses determined by the chosen track, and the Capstone Exam.\r\n\r\n\u003cp\u003eData scientists bring value to organizations across industries because they are able to solve complex challenges with data and drive important decision-making processes. Not only is there a huge demand, but there is a significant shortage of qualified data scientists with 54% of the most rigorous data science positions requiring a degree higher than a bachelor’s.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis MicroMasters® program in Statistics and Data Science (SDS) was developed by MITx and the MIT Institute for Data, Systems, and Society (IDSS). It is a multidisciplinary approach comprised of four separate tracks with four online courses each and a virtually proctored exam. Each track focuses on three methods-centered courses and a domain analysis course to provide you with foundational knowledge and hands-on training in a discipline of your choice. All learners complete the Probability and Machine Learning courses. The track you choose determines your third methods course and a final domain analysis course.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eGeneral Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you to become an informed and effective practitioner of data science who adds value to your organization across industries.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-general-track\"\u003eYou are currently exploring the General track\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eMethods Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you with in-depth knowledge of data science and time series analysis and will enable you to conduct rigorous analysis, inform decision-making processes, and contribute to evidence-based practices across industries.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-methods-track\"\u003eExplore the Methods track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eSocial Sciences Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you to extract meaningful insights from social, cultural, economic, and policy-related data and equip you to tackle complex real-world problems and contribute to cutting-edge advancements in AI and data-driven solutions within all social sciences.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-social-sciences-track\"\u003eExplore the Social Sciences track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eTime Series and Social Sciences Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will equip you to analyze the impact of interventions on time series data, preparing you for roles in economics, public policy, and social sciences where understanding temporal dynamics is crucial for informed decision-making and policy formulation.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-time-series-and-social-sciences-track\"\u003eExplore the Time Series and Social Sciences track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eAll tracks are taught by MIT faculty and administered by IDSS at a similar pace and level of rigor as an on-campus course at MIT. The program is designed for learners who want to acquire sophisticated and rigorous training in data science without leaving their day job but without compromising quality. There is no application process, but college-level calculus and comfort with mathematical reasoning and Python programming are highly recommended if you want to excel.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"36b:T4f1,\u003cp\u003eData is at the heart of our digital economy and data science has been ranked as the hottest profession of the 21st century. Whether you are new to the job market or already in the workforce and looking to upskill yourself, this five course Data Science with Python Professional Certificate program is aimed at preparing you for a career in data science and machine learning. No prior computer programming experience required!\u003c/p\u003e\r\n\r\n\u003cp\u003eYou will start by learning Python, the most popular language for data science. You will then develop skills for data analysis and data visualization and also get a practical introduction in machine learning. Finally, you will apply and demonstrate your knowledge of data science and machine learning with a capstone project involving a real life business problem.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program is taught by experts and focused on hands-on learning and job readiness. As such you will work with real datasets and will be given no-charge access to tools like Jupyter notebooks in the IBM Cloud. You will utilize popular Python toolkits and libraries such as pandas, numpy, matplotlib, seaborn, folium, scipy, scikitlearn, and more.\u003c/p\u003e\r\n\r\n\u003cp\u003eStart developing data and analytical skills today and launch your career in data science!\u003c/p\u003e36c:T13b9,"])</script><script>self.__next_f.push([1,"\u003cp\u003eData scientists bring value to organizations across industries because they are able to solve complex challenges with data and drive important decision-making processes. Not only is there a huge demand, but there is a significant shortage of qualified data scientists with 54% of the most rigorous data science positions requiring a degree higher than a bachelor’s.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis MicroMasters® program in Statistics and Data Science (SDS) was developed by MITx and the \u003ca href=\"https://idss.mit.edu/academics/micromasters-program-in-statistics-and-data-science-sds/\"\u003eMIT Institute for Data, Systems, and Society (IDSS)\u003c/a\u003e. It is a multidisciplinary approach comprised of four separate tracks with four online courses each and a virtually proctored exam. Each track focuses on a combination of methods-centered courses and domain analysis courses to provide you with foundational knowledge and hands-on training. All learners complete the Probability and Machine Learning courses, two other courses determined by the chosen track, and the Capstone Exam.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eGeneral Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you to become an informed and effective practitioner of data science who adds value to your organization across industries.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-general-track\"\u003eExplore the General track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eMethods Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you with in-depth knowledge of data science and time series analysis and will enable you to conduct rigorous analysis, inform decision-making processes, and contribute to evidence-based practices across industries.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-methods-track\"\u003eYou are currently exploring the Methods track\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eSocial Sciences Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you to extract meaningful insights from social, cultural, economic, and policy-related data and equip you to tackle complex real-world problems and contribute to cutting-edge advancements in AI and data-driven solutions within all social sciences.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-social-sciences-track\"\u003eExplore the Social Sciences track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eTime Series and Social Sciences Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will equip you to analyze the impact of interventions on time series data, preparing you for roles in economics, public policy, and social sciences where understanding temporal dynamics is crucial for informed decision-making and policy formulation.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-time-series-and-social-sciences-track\"\u003eExplore the Time Series and Social Sciences track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eAll tracks are taught by MIT faculty and administered by IDSS at a similar pace and level of rigor as an on-campu\u003cp\u003eTo earn the MicroMasters program certificate in Statistics and Data Science, learners must complete and successfully earn a certificate in the four required courses and pass a virtually-proctored capstone exam.\u003c/p\u003e\r\n\r\n\u003cp\u003eMicroMasters programs are designed to offer learners a pathway to an advanced degree and can count as credit toward completing a Master’s degree program. Learners who successfully earn this MicroMasters program certificate may apply for admission to several Master’s programs, and if accepted, the MicroMasters program certificate will count towards the degree.\u003c/p\u003e\r\n\r\n\u003cp\u003eLearners who successfully complete this MicroMasters program certificate have the opportunity to apply to the MIT Doctoral Program in Social and Engineering Systems (SES) offered through the MIT Institute for Data, Systems, and Society (IDSS).\u003c/p\u003e \r\n\r\n\u003cp\u003eLearners can use their MicroMasters program certificate to demonstrate their preparation in Statistics and Data Science fundamentals to the SES Admissions Committee. Learners admitted to SES can expect that their MicroMasters coursework will be recognized with credit for corresponding SES core classes, and for satisfying the SES Information, Systems, and Decision Science requirements. More information on the MIT SES Doctoral Program can be found \u003ca href=\"https://idss.mit.edu/academics/ses_doc/\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\r\n\r\n\u003cp\u003eIn addition, learners who successfully earn the MicroMasters program certificate in Statistics and Data Science are now eligible to earn credit at a number of universities across the globe to fast track their pursuit of a full Master’s degree. A list of pathways to graduate programs can be found \u003ca href=\"https://micromasters.mit.edu/ds/pathways-graduate-programs/\"\u003ehere\u003c/a\u003e.\u003c/p\u003es course at MIT. The program is designed for learners who want to acquire sophisticated and rigorous training in data science without leaving their day job but without compromising quality. There is no application process, but college-level calculus and comfort with mathematical reasoning and Python programming are highly recommended if you want to excel.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"36d:Tafb,"])</script><script>self.__next_f.push([1,"\u003cp\u003eData scientists bring value to organizations across industries because they are able to solve complex challenges with data and drive important decision-making processes. Not only is there a huge demand, but there is a significant shortage of qualified data scientists with 54% of the most rigorous data science positions requiring a degree higher than a bachelor’s.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis MicroMasters® program in Statistics and Data Science (SDS) was developed by MITx and the \u003ca href=\"https://idss.mit.edu/academics/micromasters-program-in-statistics-and-data-science-sds/\"\u003eMIT Institute for Data, Systems, and Society (IDSS)\u003c/a\u003e. It is a multidisciplinary approach comprised of four separate tracks with four online courses each and a virtually proctored exam. Each track focuses on a combination of methods-centered courses and domain analysis courses to provide you with foundational knowledge and hands-on training. All learners complete the Probability and Machine Learning courses, two other courses determined by the chosen track, and the Capstone Exam.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eGeneral Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you to become an informed and effective practitioner of data science who adds value to your organization across industries.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-general-track\"\u003eExplore the General track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eMethods Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you with in-depth knowledge of data science and time series analysis and will enable you to conduct rigorous analysis, inform decision-making processes, and contribute to evidence-based practices across industries.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-methods-track\"\u003eExplore the Methods track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eSocial Sciences Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you to extract meaningful insights from social, cultural, economic, and policy-related data and equip you to tackle complex real-world problems and contribute to cutting-edge advancements in AI and data-driven solutions within all social sciences.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-social-sciences-track\"\u003eYou are currently exploring the Social Sciences track\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eTime Series and Social Sciences Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will equip you to analyze the impact of interventions on time series data, preparing you for roles in economics, public policy, and social sciences where understanding temporal dynamics is crucial for informed decision-making and policy formulation.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-time-series-and-social-sciences-track\"\u003eExplore the Time Series and Social Sciences track here\u003c/a\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"36e:T414,Master the foundations of data science, data analysis, time series with interventions, and machine learning.,Analyze big data and make data-driven predictions through probabilistic modeling and statistical inference; identify and deploy appropriate modeling and methodologies in order to extract meaningful information for decision making.,Develop and build machine learning algorithms to extract meaningful information from seemingly unstructured data; learn popular unsupervised learning methods, including clustering methodologies and supervised methods such as deep neural networks.,Understand the interplay between statistics and computation for the analysis of real data.,Learn the methods for harnessing and analyzing data to answer questions of cultural, social, economic, and policy interest, and then assess that knowledge.,Finishing this track of the MicroMasters program will prepare you for job titles such as: Data Scientist, Data Analyst, Business Intelligence Analyst, Systems Analyst, Data Engineer in Social Sciences contexts.36f:Tcf0,"])</script><script>self.__next_f.push([1,"\u003cp\u003eData scientists bring value to organizations across industries because they are able to solve complex challenges with data and drive important decision-making processes. Not only is there a huge demand, but there is a significant shortage of qualified data scientists with 54% of the most rigorous data science positions requiring a degree higher than a bachelor’s.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis MicroMasters® program in Statistics and Data Science (SDS) was developed by MITx and the \u003ca href=\"https://idss.mit.edu/academics/micromasters-program-in-statistics-and-data-science-sds/\"\u003eMIT Institute for Data, Systems, and Society (IDSS)\u003c/a\u003e. It is a multidisciplinary approach comprised of four separate tracks with four online courses each and a virtually proctored exam. Each track focuses on a combination of methods-centered courses and domain analysis courses to provide you with foundational knowledge and hands-on training. All learners complete the Probability and Machine Learning courses, two other courses determined by the chosen track, and the Capstone Exam.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eGeneral Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you to become an informed and effective practitioner of data science who adds value to your organization across industries.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-general-track\"\u003eYou are currently exploring the General track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eMethods Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you with in-depth knowledge of data science and time series analysis and will enable you to conduct rigorous analysis, inform decision-making processes, and contribute to evidence-based practices across industries.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-methods-track\"\u003eExplore the Methods track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eSocial Sciences Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you to extract meaningful insights from social, cultural, economic, and policy-related data and equip you to tackle complex real-world problems and contribute to cutting-edge advancements in AI and data-driven solutions within all social sciences.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-social-sciences-track\"\u003eExplore the Social Sciences track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eTime Series and Social Sciences Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will equip you to analyze the impact of interventions on time series data, preparing you for roles in economics, public policy, and social sciences where understanding temporal dynamics is crucial for informed decision-making and policy formulation.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-time-series-and-social-sciences-track\"\u003eYou are currently exploring the Time Series and Social Sciences track\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eAll tracks are taught by MIT faculty and administered by IDSS at a similar pace and level of rigor as an on-campus course at MIT. The program is designed for learners who want to acquire sophisticated and rigorous training in data science without leaving their day job but without compromising quality. There is no application process, but college-level calculus and comfort with mathematical reasoning and Python programming are highly recommended if you want to excel.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"370:T4ee,\u003cp\u003eTechnological advances have transformed fields that rely on data by providing a wealth of information ready to be analyzed. From working with single genes to comparing entire genomes, biomedical research groups around the world are producing more data than they can handle and the ability to interpret this information is a key skill for any practitioner. The skills necessary to work with these massive datasets are in high demand, and this series will help you learn those skills.\u003c/p\u003e\r\n\r\n\u003cp\u003eUsing the open-source R programming language, you’ll gain a nuanced understanding of the tools required to work with complex life sciences and genomics data. You’ll learn the mathematical concepts — and the data analytics techniques — that you need to drive data-driven research. From a strong foundation in statistics to specialized R programming skills, this series will lead you through the data analytics landscape step-by-step.\u003c/p\u003e\r\n\r\n\u003cp\u003eTaught by Rafael Irizarry from the Harvard T.H. Chan School of Public Health, these courses will enable new discoveries and will help you improve individual and population health. If you’re working in the life sciences and want to learn how to analyze data, enroll now to take your research to the next level.\u003c/p\u003e371:T542,\u003cp\u003eData science and machine learning skills continue to be in highest demand across industries, and the need for data practitioners is booming. Upon completing this Professional Certificate program, you will be armed with the basics to jump start your career in data science and machine learning.\u003c/p\u003e\r\n\r\n\u003cp\u003eIt is a myth that to become a data scientist you need a Ph.D. This Professional Certificate is suitable for anyone who has some computer skills and a passion for self-learning. No prior computer science or programming knowledge is necessary. Anyone with some computer skills and a passion for self-learning can succeed as we start small and build up to more complex problems and topics.\u003c/p\u003e\r\n \r\n\u003cp\u003eWhen you are ready you can build up to more complex topics in o"])</script><script>self.__next_f.push([1,"ur full 9-course Data Science Professional Certificate program which covers a wide array of data science topics including: open source tools and libraries, methodologies, Python, databases, SQL, data visualization, data analysis, machine learning, and a capstone project.\u003c/p\u003e\r\n \r\n\u003cp\u003eWith the tremendous need for data science and data analyst professionals in the market today, this program will kick-start your path in data science and arm you with the fundamentals of Data Science so that you have the confidence to take the plunge and start your data science career today.\u003c/p\u003e372:T4af,\u003cp\u003eExcel in Data Science, one of the hottest fields in tech today. Learn how to gain new insights from big data by asking the right questions, manipulating data sets and visualizing your findings in compelling ways. \u003c/p\u003e \r\n\r\n\u003cp\u003eIn this MicroMasters program, you will develop a well-rounded understanding of the mathematical and computational tools that form the basis of data science and how to use those tools to make data-driven business recommendations. \u003c/p\u003e \r\n\r\n\u003cp\u003eThis MicroMasters program encompasses two sides of data science learning: the mathematical and the applied. \u003c/p\u003e \r\n\r\n\u003cp\u003eMathematical courses cover probability, statistics, and machine learning. The applied courses cover the use of specific toolkit and languages such as Python, Numpy, Matplotlib, pandas and Scipy, the Jupyter notebook environment and Apache Spark to delve into real world data.\u003c/p\u003e \r\n\r\n\u003cp\u003eYou will learn how to collect, clean and analyse big data using popular open source software will allow you to perform large-scale data analysis and present your findings in a convincing, visual way. When combined with expertise in a particular type of business, it will make you a highly desirable employee.\u003c/p\u003e373:T638,\u003cp\u003eDemand for data analysis skills are projected to grow in the U.S. 21% over the next 10 years, over four times the rate of the overall labor market. Fields like Data Science, Data Analytics, and Statistics are expected to grow up to 34%. According to "])</script><script>self.__next_f.push([1,"the World Economic Forum, emerging global demand for data analytics skills across occupations is contributing to a “race for talent,” with more jobs available than qualified candidates. According to Burning Glass Technologies research, hybrid, more complex roles which combine field-centric skills with data analysis competencies are up to 40% higher paying than their single-focus counterparts, are high-growth, and immune to the threat of automation.\u003c/p\u003e \r\n\r\n\u003cp\u003eThis Professional Certificate program prepares students, working professionals, and decision makers to become data literate in both their professional and personal lives. In today’s data-driven world, data literacy will transform you into a “data citizen,” allowing you to communicate and make decisions based upon facts with confidence. You will emerge as a champion for a data literate culture.\u003c/p\u003e \r\n\r\n\u003cp\u003eYou will gain an understanding of how using data visualization, big data, data collection, and analytical tools to better understand business challenges and inform the decision-making process.\u003c/p\u003e \r\n\r\n\u003cp\u003eThis program is valuable for students and professionals who want to go beyond data analysis software proficiency to develop the ability to read, write, and communicate using data in context, including an understanding of data sources and constructs.\u003c/p\u003e374:T6ec,\u003cp\u003eData Science and Data Analytics skills are in high demand and R is the programming language of choice for many data professionals. This Applied Data Science with R program emphasizes a hands-on approach to developing job-ready skills for analyzing and visualizing data using R.\u003c/p\u003e \r\n\r\n\u003cp\u003eYou will start the program by learning the fundamentals of R language, including common data types and structures, and utilize it for basic programming and data manipulation tasks.\u003c/p\u003e\r\n\r\n\u003cp\u003eAs you progress in the program, you will learn about relational database concepts and gain a foundational knowledge of the SQL language. You will access and analyze data in databases using R and SQL through Jupyter "])</script><script>self.__next_f.push([1,"notebooks.\u003c/p\u003e\r\n\r\n\u003cp\u003eYou will learn various data analysis techniques – from cleaning and refining data to developing, evaluating, and tuning , data science models. You will also learn how to tell a compelling story with data by creating graphs, visualizations, dashboards and interactive data applications.\u003c/p\u003e\r\n\r\n\u003cp\u003eIn each course you will complete hands-on labs and projects to help you gain practical experience with data manipulation, analysis and visualization using a variety of datasets. You will work with tools like R Studio, Jupyter Notebooks, Watson Studio and related R libraries for data science, including dplyr, Tidyverse, Tidymodels, R Shiny, ggplot2, Leaflet, and rvest.\u003c/p\u003e\r\n\r\n\u003cp\u003eBy the end of the program you will be able to apply the data science skills and techniques that you have accumulated and show case those skills in the R Data Science Capstone Project. involving a real-world dataset, and inspired by a real business challenge. This project will culminate in a presentation for reporting the results of data analysis with stakeholders.\u003c/p\u003e375:T456,\u003cp\u003eIn this course, part of our \u003ca href=\"https://www.edx.org/professional-certificate/harvardx-data-science\"\u003eProfessional Certificate Program in Data Science\u003c/a\u003e,you will learn valuable concepts in probability theory. The motivation for this course is the circumstances surrounding the financial crisis of 2007-2008. Part of what caused this financial crisis was that the risk of some securities sold by financial institutions was underestimated. To begin to understand this very complicated event, we need to understand the basics of probability. \u003c/p\u003e\n\u003cp\u003eWe will introduce important concepts such as random variables, independence, Monte Carlo simulations, expected values, standard errors, and the Central Limit Theorem. These statistical concepts are fundamental to conducting statistical tests on data and understanding whether the data you are analyzing is likely occurring due to an experimental method or to chance. \u003c/p\u003e\n\u003cp\u003eProbability theory is the mathematica"])</script><script>self.__next_f.push([1,"l foundation of statistical inference which is indispensable for analyzing data affected by chance, and thus essential for data scientists.\u003c/p\u003e376:T532,\u003cp\u003e\u003cem\u003ePlease Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMuch of the world's data lives in databases. SQL (or Structured Query Language) is a powerful programming language that is used for communicating with and extracting various data types from databases. A working knowledge of databases and SQL is necessary to advance as a data scientist or a machine learning specialist. The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. It is also intended to get you started with performing SQL access in a data science environment.\u003c/p\u003e\n\u003cp\u003eThe emphasis in this course is on hands-on, practical learning. As such, you will work with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs, you will practice building and running SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and Python.\u003c/p\u003e\n\u003cp\u003eNo prior knowledge of databases, SQL, Python, or programming is required.\u003c/p\u003e377:T417,\u003cp\u003e\u003cem\u003ePlease Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn this course, you'll learn about Data Science tools like Jupyter Notebooks, RStudio IDE, and Watson Studio. You will learn what each tool is used for, what programming languages they can execute, their features and limitations and how data scientists use these t"])</script><script>self.__next_f.push([1,"ools today.\u003c/p\u003e\n\u003cp\u003eWith the tools hosted in the cloud, you will be able to test each tool and follow instructions to run simple code in Python or R. To complete the course, you will create a final project with a Jupyter Notebook on IBM Watson Studio on Cloud and demonstrate your proficiency in preparing a notebook, writing Markdown, and sharing your work with your peers.\u003c/p\u003e\n\u003cp\u003eThis hands-on course will get you up and running with some of the latest and greatest data science tools.\u003c/p\u003e378:T5d7,\u003cp\u003eArtificial Intelligence (AI) is often touted as a key technology spurring the Fourth Industrial Revolution in which the physical, digital and biological worlds are being fused together in a way that will have a tremendous impact on our global culture and economy. The unprecedented amount of data we create every day fuels this new paradigm of AI. This new world of opportunities also brings with it concerns about security, user privacy, data misuse, surveillance, data ownership, and more. People distrust the use of artificial intelligence and institutions that rely on it without building accountability and transparency. It is the responsibility of business and technology leaders and data scientists to change that: add transparency, develop standards and share best practices to drive AI adoption with trust.\u003c/p\u003e\n\u003cp\u003eBusiness leaders and data professionals today need AI frameworks and methods to achieve optimal results while also being good technology and business stewards. Though companies and institutions are adopting AI principles and the language of ethics, trust and responsibility has entered emerging technologies, AI and data science, there is still confusion about when and why it’s needed. This course introduces some of the principles and frameworks that puts ethics and responsibility into practice in the data analytics profession, and offers practical approaches to technical, business and leadership dilemmas and challenges posed by work in AI and Data Science.\u003c/p\u003e379:T46f,\u003cp\u003eIn the first half of this course, we'll in"])</script><script>self.__next_f.push([1,"vestigate DNA replication, and ask the question, where in the genome does DNA replication begin? You will learn how to answer this question for many bacteria using straightforward algorithms to look for hidden messages in the genome. \u003c/p\u003e\r\n\u003cp\u003eIn the second half of the course, we'll examine a different biological question, and ask which DNA patterns play the role of molecular clocks. The cells in your body manage to maintain a circadian rhythm, but how is this achieved on the level of DNA? Once again, we will see that by knowing which hidden messages to look for, we can start to understand the amazingly complex language of DNA. Perhaps surprisingly, we will apply randomized algorithms to solve problems. \u003c/p\u003e\r\n\u003cp\u003eFinally, you will get your hands dirty and apply existing software tools to find recurring biological motifs within genes that are responsible for helping Mycobacterium tuberculosis go \"dormant\" within a host for many years before causing an active infection. \u003c/p\u003e\r\n\u003cp\u003eThis course begins a series of classes illustrating the power of computing in modern biology.\u003c/p\u003e37a:T7b0,\u003cp\u003eAs a pilot course and cognitive course for data science, this course is dedicated to popularizing the basic knowledge, core concepts and thinking models related to data mining and big data for students through a vivid teaching model, from engineering technology, legal norms, and application practice. Describe the beautiful blueprint of data science from different angles. This course is suitable for college students from various backgrounds who are interested in the fascinating field of data science. Existing online data science courses mainly focus on purely technical content such as learning specific algorithms. In contrast, data science is an application-oriented, highly interdisciplinary field that requires systematic knowledge from multiple domains. In addition to algorithmic learning, students also need to recognize the challenges people may face in the real world and the relationship between data and human society. The purpose"])</script><script>self.__next_f.push([1," of this course is to comprehensively understand the key issues in the big data era, improve data awareness, and help students lay a solid foundation for subsequent data science courses.\u003c/p\u003e\n\u003cp\u003eThis is an introductory course suitable for university students with diverse backgrounds interested in getting into the fascinating world of data science. Existing online data science courses mainly focus on learning specific algorithms and other purely technical contents. By contrast, data science is an application-oriented, highly interdisciplinary domain, which requires systematic knowledge from a variety of sources. In addition to algorithm learning, students also need to appreciate the challenges that people may face in the real world as well as the relationship between data and human society. The purpose of this course is to provide a comprehensive understanding of the key issues in the era of big data and promote data awareness to help students lay a solid foundation for subsequent data science courses.\u003c/p\u003e37b:T6e4,\u003cp\u003e\u003cem\u003ePor favor ten en cuenta: Los estudiantes que completen con éxito este curso de IBM ahora pueden obtener una insignia digital de habilidades: una credencial detallada, verificable y digital que comprueba los conocimientos y habilidades que has adquirido en este curso. Inscríbete para obtener más información, completa el curso y reclama tu insignia digital.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eComienza tu aprendizaje de Python para la ciencia de datos, así como programación en general con esta introducción a Python. Este curso de Python para principiantes te llevará rápidamente de cero a programar en Python en cuestión de horas y te dará una idea de cómo comenzar a trabajar con datos en Python.\u003c/p\u003e\n\u003cp\u003eUna vez completado, podrás escribir tus propios scripts de Python y realizar análisis básicos de datos prácticos utilizando nuestro entorno de laboratorio basado en Jupyter. Si quieres aprender Python desde cero, este curso es para ti.\u003c/p\u003e\n\u003cp\u003ePuedes comenzar a crear tus propios proyectos de ciencia de datos y co"])</script><script>self.__next_f.push([1,"laborar con otros científicos de datos utilizando IBM Watson Studio. Cuando te registres, recibirás acceso gratuito a Watson Studio. Comienza ahora y aprovecha esta plataforma, aprenderás los conceptos básicos de programación, aprendizaje automático y visualización de datos con este curso introductorio.\u003cspan lang=\"ES-TRAD\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eTen en cuenta que los foros de discusión de este curso están abiertos para que los estudiantes publiquen y se comuniquen entre sí. Sin embargo, los foros ya no serán supervisados por el equipo de IBM. Las preguntas técnicas relacionadas con tu experiencia en el curso deben dirigirse al equipo de soporte de edX a través de la información de contacto proporcionada en el curso. Gracias.\u003c/p\u003e37c:T45c,\u003cp\u003eData Science techniques are very powerful predictive tools for all types of organizations. Recent advancements in data collection, data science libraries and more powerful computers have put advanced data science within reach of all size organizations.\u003c/p\u003e \r\n\r\n\u003cp\u003eThis series of courses uses easy to learn, state of the art, free tools of Python, Scikit-Learn and Tableau to perform advanced data science. Most examples use real dataset so the skills that you are learning produce real results.\u003c/p\u003e \r\n\r\n\u003cp\u003eCourses cover numerous useful topics. Data preprocessing shows students how to easily normalize data and how domain space reduction can improve results. Supervised Learning algorithms like K-Nearest Neighbor, Regression, Decision Tree and Random Forest are covered. Unsupervised Learning algorithms like K-means, DBSCAN and Hierarchical clustering are also covered. Tableau is used to show students how to visualize data.\u003c/p\u003e \r\n\r\n\u003cp\u003eThese courses do require basic programming skills, except for the Data Visualization course. The Data Visualization course has no prerequisite skills requirements.\u003c/p\u003e37d:T7e7,\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIf you have specific questions about this course, please contact us at\u003ca href=\"mailto:sds-mm@mit.edu\"\u003e sds-mm@mit.edu\u003c/a\u003e.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMachine lea"])</script><script>self.__next_f.push([1,"rning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk.\u003c/p\u003e\n\u003cp\u003eAs a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control.\u003c/p\u003e\n\u003cp\u003eIn this course, students will learn about principles and algorithms for turning training data into effective automated predictions. We will cover:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRepresentation, over-fitting, regularization, generalization, VC dimension;\u003c/li\u003e\n\u003cli\u003eClustering, classification, recommender problems, probabilistic modeling, reinforcement learning;\u003c/li\u003e\n\u003cli\u003eOn-line algorithms, support vector machines, and neural networks/deep learning.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eStudents will implement and experiment with the algorithms in several Python projects designed for different practical applications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis course is part of the\u003ca href=\"https://www.edx.org/micromasters/mitx-statistics-and-data-science\"\u003e MITx MicroMasters Program in Statistics and Data Science\u003c/a\u003e. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit \u003ca href=\"https://micromasters.mit.edu/ds/\"\u003ehttps://micromasters.mit.edu/ds/\u003c/a\u003e.\u003c/strong\u003e\u003c/p\u003e37e:T68d,\u003cp\u003eThis is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic r"])</script><script>self.__next_f.push([1,"egression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; neural networks and deep learning; survival models; multiple testing. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).\u003c/p\u003e\n\u003cp\u003eThis is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data science. Computing in this course is done in Python. There are lectures devoted to Python, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chatper. We also offer the separate and original version of this course called \u003ca href=\"https://www.edx.org/learn/statistics/stanford-university-statistical-learning\" rel=\"noopener\" target=\"_blank\"\u003eStatistical Learning with R\u003c/a\u003e – the chapter lectures are the same, but the lab lectures and computing are done using R.\u003c/p\u003e\n\u003cp\u003eThe lectures cover all the material in An Introduction to Statistical Learning, with Applications in Python by James, Witten, Hastie, Tibshirani, and Taylor (Springer, 2023. The pdf for this book is available for free on the \u003ca href=\"https://www.statlearning.com/\"\u003ebook website\u003c/a\u003e.\u003c/p\u003e37f:T4ac,\u003cp\u003eBuilding an online brand and outreach strategy is paramount to any social media planning and strategy. The vast amount of data, customer insights, platforms and networks are re-imagined each day, demanding the marketer to be a savvy predictor. This course will prepare you for a comprehensive approach to online advertising, detailing various analysis approaches and selecting the right networks and the right messages. You will explore relevant and trending concepts such as: social media ads, active users, advertising platforms, advertising s"])</script><script>self.__next_f.push([1,"trategy, advertising, hashtags, influencers, landing page, seo, ad spend, advertising options, email marketing, google adwords, lead generation, video ads, click-through rate, and content marketing. Be prepared to plan and allocate resources to the advertising that creates an impact. \u003c/p\u003e\n\u003cp\u003eThis online course is designed for online media strategists, marketing analysts and managers, marketing brand and strategy experts and digital marketing leaders.\u003c/p\u003e\n\u003cp\u003eThis course is part of Maryland Smith’s Digital Marketing Professional Certificate. For more information, \u003ca href=\"https://www.edx.org/digital-marketing-professional\"\u003esee here\u003c/a\u003e.\u003c/p\u003e380:T5fd,\u003cp\u003eBusinesses today have access to an increasingly large amount of detailed customer data, and this influx of “big data” is only going to continue. Combined with a detailed history of marketing actions, there is a newfound potential for deriving actionable insights, but you need the tools to do so. Using real-world applications from various industries, this course will help you understand the tools and strategies used to make data-driven decisions that you can put to use in your own company or business.\u003c/p\u003e\n\u003cp\u003eThis valuable data may include in-store and online customer transactions, customer surveys, web analytics, as well as prices and advertising. You’ll also learn how to assess critical managerial problems, develop relevant hypotheses, analyze data and, most importantly, draw inferences to create convincing narratives which yield actionable results. Artificial intelligence and machine learning will be explored as tools to deepen analytical skills and acumen and hone decision making.\u003c/p\u003e\n\u003cp\u003eThis comprehensive exploration into digital marketing analytics tools and techniques is critical knowledge for any marketing influencers, digital marketing analysts and product and brand decision makers within small and medium businesses as well as larger organizations with international reach.\u003c/p\u003e\n\u003cp\u003eThis course is part of Maryland Smith’s Digital Marketing Professional C"])</script><script>self.__next_f.push([1,"ertificate. For more information, \u003ca href=\"https://www.edx.org/certificates/professional-certificate/usmx-digital-marketing\"\u003esee here\u003c/a\u003e.\u003c/p\u003e381:T68d,\u003cp\u003eThis is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; neural networks and deep learning; survival models; multiple testing. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).\u003c/p\u003e\n\u003cp\u003eThis is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data science. Computing is done in R. There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter. We also offer a separate version of the course called \u003ca href=\"https://www.edx.org/learn/data-analysis-statistics/stanford-university-statistical-learning-with-python\" rel=\"noopener\" target=\"_blank\"\u003eStatistical Learning with Python\u003c/a\u003e – the chapter lectures are the same, but the lab lectures and computing are done using Python.\u003c/p\u003e\n\u003cp\u003eThe lectures cover all the material in An Introduction to Statistical Learning, with Applications in R (second addition) by James, Witten, Hastie and Tibshirani (Springer, 2021). The pdf for this book is available for free on the \u003ca href=\"https://www.statlearning.com/\"\u003ebook website\u003c/a\u003e.\u003c/p\u003e382:T756,\u003cp\u003eDo you want to prepare for medical school, study a STEM field, become a research scientist, or transition to a career in the booming biotechnology industry? Or maybe you just want to understand the chemical r"])</script><script>self.__next_f.push([1,"eactions that govern life itself. Join Professor Yaffe, an MIT professor and practicing surgeon, as he guides you through the science that inspires countless doctors, researchers, and students alike.\u003c/p\u003e\n\u003cp\u003eWe developed 7.05x Biochemistry with an emphasis on:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDeveloping your scientific thinking skills including articulating hypotheses, performing thought experiments, interpreting data, and designing experiments.\u003c/li\u003e\n\u003cli\u003eUsing data based on real scientific experiments and highlighting the scientific process.\u003c/li\u003e\n\u003cli\u003eAsserting that biology is an active field that changes daily through examples of MIT (and other current) research, not static information in a textbook.\u003c/li\u003e\n\u003cli\u003eVisualizing real molecular structures with PyMOL to better understand function and mechanism.\u003c/li\u003e\n\u003cli\u003eAppreciating the quantitative aspects of biochemistry and practicing this quantitation with MATLAB.\u003c/li\u003e\n\u003cli\u003eTranslating topics in biochemistry to diseases and medicine.\u003c/li\u003e\n\u003cli\u003eConveying the authentic MIT \u003ca href=\"http://hacks.mit.edu/Hacks/by_year/1991/fire_hydrant/\"\u003efirehose\u003c/a\u003e experience.\u003c/li\u003e\n\u003cli\u003eImplementing the science of learning in the course design.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe offer a thorough and robust means of certifying edX learners in their mastery of the MITx biochemistry content, through the MITx 7.05x Biochemistry Competency Exam. This challenging option is available \u003cstrong\u003eonly\u003c/strong\u003e to those who register for the verified-certificate track, and successful completion of this exam is the majority of the assessment grade that counts toward a certificate. \u003cstrong\u003eThe Competency Exam will be open during the final week of the course.\u003c/strong\u003e\u003c/p\u003e383:T59a,\u003cp\u003eCausal diagrams have revolutionized the way in which researchers ask: What is the causal effect of X on Y? They have become a key tool for researchers who study the effects of treatments, exposures, and policies. By summarizing and communicating assumptions about the causal structure of a problem, causal diagrams have helped clarify apparent paradoxes, describe commo"])</script><script>self.__next_f.push([1,"n biases, and identify adjustment variables. As a result, a sound understanding of causal diagrams is becoming increasingly important in many scientific disciplines.\u003c/p\u003e\n\u003cp\u003eThe first part of this course is comprised of seven lessons that introduce causal diagrams and its applications to causal inference. The first lesson introduces causal DAGs, a type of causal diagrams, and the rules that govern them. The second, third, and fourth lessons use causal DAGs to represent common forms of bias. The fifth lesson uses causal DAGs to represent time-varying treatments and treatment-confounder feedback, as well as the bias of conventional statistical methods for confounding adjustment. The sixth lesson introduces SWIGs, another type of causal diagrams. The seventh lesson guides learners in constructing causal diagrams.\u003c/p\u003e\n\u003cp\u003eThe second part of the course presents a series of case studies that highlight the practical applications of causal diagrams to real-world questions from the health and social sciences.\u003c/p\u003e\n\u003cp\u003eProfessor Photo Credit: Anders Ahlbom\u003c/p\u003e384:T9cc,"])</script><script>self.__next_f.push([1,"\u003cp\u003eNote: Learners who successfully complete this MathWorks course can earn a Digital Credential — a visual representation of a verified achievement that can be issued, accessed, and displayed online. Enroll to learn more, complete the course, and claim your badge!\u003c/p\u003e\n\u003cp\u003eExpand your data analysis and modeling skills in MATLAB, a programming and numeric computing platform used to analyze data, develop algorithms, and create models. Millions of engineers and scientists worldwide use MATLAB to study and build advanced applications in machine learning, deep learning, signal processing, communications, image processing, and control systems. They are shaping the future by modeling rockets that may someday take you into space, developing autonomous vehicles to travel safely and efficiently, and designing wave farms that harness the power of ocean waves to generate clean energy.\u003c/p\u003e\n\u003cp\u003eIn this course, you'll use MATLAB to examine real-world problems and answer questions like:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eHow far does a blue whale swim each day?\u003c/li\u003e\n\u003cli\u003eWhat is the favorite topping in a pizza shop?\u003c/li\u003e\n\u003cli\u003eWhat is the ride quality of a car suspension?\u003c/li\u003e\n\u003cli\u003eHow does the magnitude of an earthquake impact the strength of a tsunami?\u003c/li\u003e\n\u003cli\u003eWhat is the most expensive failure in a factory?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eMATLAB makes it easy to see results quickly, so there are no pre-requisites for the course. Whether you're auditing or a verified learner, you will have free access to MATLAB for the duration of the course. You will learn how to process, analyze, and visualize data collected nearly everywhere in today's digital workplace. You'll use powerful templates and auto-generated code to start experimenting immediately and quickly process similar data sets. And you'll gain the essential programming skills needed to perform these exciting tasks.\u003c/p\u003e\n\u003cp\u003eThroughout the course, you'll have ample opportunities to practice your newly acquired skills – through auto-graded assignments, practice quizzes, interactive readings, and projects. By the end of the course, you'll be ready to analyze your own data sets and impress colleagues with word clouds, geographic plots, animations, and more.\u003c/p\u003e\n\u003cp\u003eAdditionally, this course will give you the skills you need to prepare for the \u003ca href=\"https://www.mathworks.com/certification\"\u003eMathWorks Certified MATLAB Associate exam\u003c/a\u003e. Certification verifies valuable transferable skills, sets you apart in the job market, and can help accelerate professional growth.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"385:T8c0,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cstrong\u003eAbout the Database Series of Courses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\"Databases\" was one of Stanford's three inaugural massive open online courses in the fall of 2011. It has been offered in synchronous and then in self-paced versions on a variety of platforms continuously since 2011. The material is now being offered as a set of five self-paced courses, which can be taken in a variety of ways to learn about different aspects of databases. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRelational Databases and SQL\u003c/em\u003e is the most popular course in the Databases series. It is applicable to learners seeking to gain a strong understanding of relational databases, and to master SQL, the long-accepted standard query language for relational database systems. Additional courses focus on advanced concepts in relational databases and SQL, formal foundations and database design methodologies, and semistructured data.\u003c/p\u003e\n\u003cp\u003eAll of the courses are based around video lectures and demos. Many of them include quizzes between video segments to check understanding, in-depth standalone quizzes, and/or a variety of automatically-checked interactive exercises. Each course also includes an unmoderated discussion forum and pointers to readings and resources. The courses are described briefly below. Taught by Professor Jennifer Widom, the overall curriculum draws from Stanford's popular longstanding Databases course.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy Learn About Databases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDatabases are incredibly prevalent -- they underlie technology used by most people every day if not every hour. Databases reside behind a huge number of websites; they're a crucial component of telecommunications systems, banking systems, video games, and just about any other software system or electronic device that maintains some amount of persistent information. In addition to persistence, database systems provide a number of other properties that make them exceptionally useful and convenient: reliability, efficiency, scalability, concurrency control, data abstractions, and high-level query languages. Databases are so ubiquitous and important that computer science graduates frequently cite their database class as the one most useful to them in their industry or graduate-school careers.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"386:T4f2,\u003cp\u003eThis course is one of five self-paced courses on the topic of Databases, originating as one of Stanford's three inaugural massive open online courses released in the fall of 2011. The original \"Databases\" courses are now all available on edx.org.\u003c/p\u003e\n\u003cp\u003eThis course covers underlying principles and design considerations related to databases; it can be taken either before or after taking other courses in the Databases series.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe Relational Algebra section of this course teaches the algebraic query language that provides the formal foundations of SQL.\u003c/li\u003e\n\u003cli\u003eThe Relational Design Theory section of the course provides comprehensive coverage of dependency theory and normal forms in relational databases, a well-accepted theoretical framework for developing good relational database schemas.\u003c/li\u003e\n\u003cli\u003eThe Unified Modeling Language section of this course introduces the data-modeling component of UML, and describes how UML diagrams are translated to relational database schemas.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe introductory videos in this course are the same as the introductory videos in \u003cem\u003eDatabases: Relational Databases and SQL\u003c/em\u003e ; they are included for the benefit of learners who have not taken \u003cem\u003eDatabases: Relational Databases and SQL\u003c/em\u003e.\u003c/p\u003e387:T805,"])</script><script>self.__next_f.push([1,"\u003cp\u003eStanford's online offering in Databases is now available as a set of five self-paced courses:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDatabases: Relational Databases and SQL\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIntroduction to the relational model and concepts in relational databases and relational database management systems\u003c/li\u003e\n\u003cli\u003eComprehensive coverage of SQL, the long-accepted standard query language for relational database management systems\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDatabases: Advanced Topics in SQL (prerequisite: Relational Databases and SQL)\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreating indexes for increased query performance\u003c/li\u003e\n\u003cli\u003eUsing transactions for concurrency control and failure recovery\u003c/li\u003e\n\u003cli\u003eDatabase constraints: key, referential integrity, and \"check\" constraints\u003c/li\u003e\n\u003cli\u003eDatabase triggers\u003c/li\u003e\n\u003cli\u003eHow views are created, used, and updated in relational databases\u003c/li\u003e\n\u003cli\u003eAuthorization in relational databases\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDatabases: OLAP and Recursion\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eStar schemas, the data cube concept, and On-Line Analytical Processing (OLAP) features in relational databases including the Cube and Rollup operators\u003c/li\u003e\n\u003cli\u003eThe SQL standard for queries over recursively-defined relations\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDatabases: Modeling and Theory\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelational algebra – the algebraic query language that provides the formal foundations of SQL\u003c/li\u003e\n\u003cli\u003eDependency theory and normal forms in relational databases as the basis of schema design\u003c/li\u003e\n\u003cli\u003eThe data-modeling component of the Unified Modeling Language (UML), how UML diagrams are translated to relations\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDatabases: Semistructured Data\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe XML model for semistructured and self-describing data, including DTDs and some features of XML Schema\u003c/li\u003e\n\u003cli\u003eThe JSON model for human-readable structured or semistructured data\u003c/li\u003e\n\u003cli\u003eThe XPath language for processing XML data, and many features of the more advanced XQuery language\u003c/li\u003e\n\u003cli\u003eAn introduction to the XSLT rule-based language for querying and transforming XML data\u003c/li\u003e\n\u003c/ul\u003e"])</script><script>self.__next_f.push([1,"388:T76f,\u003cp\u003eWant to know how to avoid bad decisions with data?\u003c/p\u003e\n\u003cp\u003eMaking good decisions with data can give you a distinct competitive advantage in business. This statistics and data analysis course will help you understand the fundamental concepts of sound statistical thinking that can be applied in surprisingly wide contexts, sometimes even before there is any data! Key concepts like understanding variation, perceiving relative risk of alternative decisions, and pinpointing sources of variation will be highlighted.\u003c/p\u003e\n\u003cp\u003eThese big picture ideas have motivated the development of quantitative models, but in most traditional statistics courses, these concepts get lost behind a wall of little techniques and computations. In this course we keep the focus on the ideas that really matter, and we illustrate them with lively, practical, accessible examples.\u003c/p\u003e\n\u003cp\u003eWe will explore questions like: How are traditional statistical methods still relevant in modern analytics applications? How can we avoid common fallacies and misconceptions when approaching quantitative problems? How do we apply statistical methods in predictive applications? How do we gain a better understanding of customer engagement through analytics?\u003c/p\u003e\n\u003cp\u003eThis course will be is relevant for anyone eager to have a framework for good decision-making. It will be good preparation for students with a bachelor's degree contemplating graduate study in a business field.\u003c/p\u003e\n\u003cp\u003eOpportunities in analytics are abundant at the moment. Specific techniques or software packages may be helpful in landing first jobs, but those techniques and packages may soon be replaced by something newer and trendier. Understanding the ways in which quantitative models really work, however, is a management level skill that is unlikely to go out of style.\u003c/p\u003e\n\u003cp\u003eThis course is part of the Business Principles and Entrepreneurial Thought XSeries.\u003c/p\u003e389:T6e1,\u003cp\u003eThis is the first cell biology course in a three-part series. Building upon the concepts from biochemistry, genetics, and mo"])</script><script>self.__next_f.push([1,"lecular biology from our \u003ca href=\"http://bit.ly/700xBio\"\u003e7.00x Introductory Biology\u003c/a\u003e and \u003ca href=\"http://bit.ly/705xBiochem\"\u003e7.05x Biochemistry\u003c/a\u003e MOOCs, these cell biology courses transition to a comprehensive discussion of biology at an experimental level. How do we know what we know about cells at a molecular level and how can we use that knowledge to design experiments to test hypotheses in cell biology?\u003c/p\u003e\n\u003cp\u003eProfessors Rebecca Lamason and Iain Cheeseman guide you through a learning experience where you will discover experiments that answered big questions and find out what is still on the horizon. You will embark on a lively journey through cellular transport and cellular signaling mechanisms and regulation and learn how to apply key concepts and themes of this dynamic experimental science to understand the fundamental workings of cells.\u003c/p\u003e\n\u003cp\u003eWe developed the 7.06x Cell Biology series with an emphasis on:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDeveloping your scientific thinking skills including articulating hypotheses, performing thought experiments, interpreting data, and designing experiments.\u003c/li\u003e\n\u003cli\u003eUsing data based on real scientific experiments and highlighting the scientific process in assessments.\u003c/li\u003e\n\u003cli\u003eAsserting that biology is an active field that changes daily through examples of research and relevance to medicine, not static information in a textbook.\u003c/li\u003e\n\u003cli\u003eUniting themes and principles that inform how scientists conduct and interpret research. \u003c/li\u003e\n\u003cli\u003eExploring foundational experiments that defined modern cell biology. \u003c/li\u003e\n\u003cli\u003eImplementing the science of learning in the course design.\u003c/li\u003e\n\u003c/ul\u003e38a:T8b8,"])</script><script>self.__next_f.push([1,"\u003cp\u003eFrom the printing press to the typewriter, there is a long history of scholars adapting to new technologies. In the last forty or fifty years, the most significant advance has been the digitization of books. We now have whole libraries—centuries of history, literature, and philosophy—available instantaneously. This new access is a wonderful benefit, but it can also be overwhelming. If you have hundreds of thousands of books available to you in an instant, where do you even start? With a bit of elementary code, you can study all of these books at once, and derive new sorts of insights.\u003c/p\u003e\n\u003cp\u003eComputation is changing the very nature of how we do research in the humanities. Tools from data science can help you to explore the record of human culture in ways that just wouldn’t have been possible before. You’re more likely to reach out to others, to work across disciplines, and to assemble teams. Whether you're a student wanting to expand your skillset, a librarian supporting new modes of research, or a journalist who has just received a massive cache of leaked e-mails, this course will show you how to draw insights from thousands of documents at once. You will learn how, with a few simple lines of code, to make use of the metadata—the information about our objects of study—to zero in on what matters most, and visualize your results so that you can understand them at a glance.\u003c/p\u003e\n\u003cp\u003eIn this course, you’ll work on building parts of a search engine, one tailor-made to the needs of academic research. Along the way, you'll learn the fundamentals of text analysis: a set of techniques for manipulating the written word that stand at the core of the digital humanities.\u003c/p\u003e\n\u003cp\u003eBy the end of the course, you will be able to apply what you learn to what interests you most, be it contemporary speeches, journalism, caselaw, and even art objects. This course will analyze pieces of 18th-century literature, showing you how these methods can be applied to philosophical works, religious texts, political and historical records – material from across the spectrum of humanistic inquiry.\u003c/p\u003e\n\u003cp\u003eCombine your traditional research skills with data science to find answers you never might have expected.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"38b:T782,\u003cp dir=\"ltr\"\u003e\u003cspan style=\"font-size: 10.5pt; font-family: Roboto,sans-serif; color: #000000; background-color: #ffffff; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"\u003eThis third course in the Data Science and Economics series is based on a Connector Course taught at UC Berkeley as a Connector between the field of Economics and the popular Introduction to Data Science Course. \u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan style=\"font-size: 10.5pt; font-family: Roboto,sans-serif; color: #000000; background-color: #ffffff; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"\u003e\n\nIn this course we cover some advanced topics from upper division courses, but in an introductory way, with applied applications and datasets. \u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan style=\"font-size: 10.5pt; font-family: Roboto,sans-serif; color: #000000; background-color: #ffffff; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"\u003e\n\nThis course is of interest to the growing number of students interested in the overlap between Economics and Data Science. The course has some more advanced programming challenges, including the Lorenz Curve and Gini Coefficient, statsmodels package for econometrics, and using a finance API. \u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan style=\"font-size: 10.5pt; font-family: Roboto,sans-serif; color: #000000; background-color: #ffffff; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"\u003e\n\nEach of the applications follows a unique applied dataset to illustrate the concepts that are learned in intermediate economics courses. Concepts of applied data analysis are illustrated in some advanced fields.\u003c/span\u003e\u003c/p\u003e38c:T66a,\u003cp\u003eThis is the final cell biology course in a three-part series. Building upon the concepts from biochemistr"])</script><script>self.__next_f.push([1,"y, genetics, and molecular biology from our \u003ca href=\"http://bit.ly/700xBio\"\u003e7.00x Introductory Biology\u003c/a\u003e and \u003ca href=\"http://bit.ly/705xBiochem\"\u003e7.05x Biochemistry\u003c/a\u003e MOOCs, these cell biology courses transition to a comprehensive discussion of biology at an experimental level. How do we know what we know about cells at a molecular level and how can we use that knowledge to design experiments to test hypotheses in cell biology?\u003c/p\u003e\n\u003cp\u003eHow do you go from a single cell to trillions of cells working together? And what happens when this amazing collective is confronted with pathogens? Professors Rebecca Lamason and Sebastian Lourido will challenge you to apply your foundational knowledge of cell biology to the next level. You will explore how cells function to maintain organism health and the consequences if these processes fail.\u003c/p\u003e\n\u003cp\u003eWe developed the 7.06x Cell Biology series with an emphasis on:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDeveloping your scientific thinking skills including articulating hypotheses, performing thought experiments, interpreting data, and designing experiments.\u003c/li\u003e\n\u003cli\u003eUsing data based on real scientific experiments and highlighting the scientific process in assessments.\u003c/li\u003e\n\u003cli\u003eAsserting that biology is an active field that changes daily through examples of research and relevance to medicine, not static information in a textbook.\u003c/li\u003e\n\u003cli\u003eUniting themes and principles that inform how scientists conduct and interpret research.\u003c/li\u003e\n\u003cli\u003eImplementing the science of learning in the course design.\u003c/li\u003e\n\u003c/ul\u003e38d:T6a3,\u003cp\u003eThis is the second cell biology course in a three-part series. Building upon the concepts from biochemistry, genetics, and molecular biology from our \u003ca href=\"https://www.edx.org/course/introduction-to-biology-the-secret-of-life-3\"\u003e7.00x Introductory Biology\u003c/a\u003e and \u003ca href=\"https://www.edx.org/course/biochemistry-biomolecules-methods-and-mechanisms\"\u003e7.05x Biochemistry\u003c/a\u003e MOOCs, these cell biology courses transition to a comprehensive discussion of biology at an experimental level. How do we know"])</script><script>self.__next_f.push([1," what we know about cells at a molecular level and how can we use that knowledge to design experiments to test hypotheses in cell biology?\u003c/p\u003e\n\u003cp\u003eDo you think you know how cells grow and divide? Professor Iain Cheeseman will challenge you to see the cytoskeleton in new and beautiful ways. You will explore these structural elements of cells with an expanded toolkit to better understand the dynamic processes that generate incredible amounts of force and regulate function throughout the cell cycle.\u003c/p\u003e\n\u003cp\u003eWe developed the 7.06x Cell Biology series with an emphasis on:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDeveloping your scientific thinking skills including articulating hypotheses, performing thought experiments, interpreting data, and designing experiments.\u003c/li\u003e\n\u003cli\u003eUsing data based on real scientific experiments and highlighting the scientific process in assessments.\u003c/li\u003e\n\u003cli\u003eAsserting that biology is an active field that changes daily through examples of research and relevance to medicine, not static information in a textbook.\u003c/li\u003e\n\u003cli\u003eUniting themes and principles that inform how scientists conduct and interpret research.\u003c/li\u003e\n\u003cli\u003eImplementing the science of learning in the course design.\u003c/li\u003e\n\u003c/ul\u003e38e:T551,\u003cp\u003e\u003cspan lang=\"EN\"\u003eBasics of Bayesian Data Analysis Using R is part one of the Bayesian Data Analysis in R professional certificate. \u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eBayesian approach is becoming increasingly popular in all fields of data analysis, including but not limited to epidemiology, ecology, economics, and political sciences. It also plays an increasingly important role in data mining and deep learning. Let this course be your first step into Bayesian statistics.\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eHere, you will find a practical introduction to applied Bayesian data analysis with the emphasis on formulating and answering real life questions. You will learn how to combine the data generating mechanism, likelihood, with prior distribution using Bayes’ Theorem to produce the posterior distribution. You will investigate the und"])</script><script>self.__next_f.push([1,"erlying theory and fundamental concepts by way of simple and clear practical examples, including a case of linear regression.\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eYou will be introduced to the Gibbs sampler – the simplest version of the powerful Markov Chain Monte Carlo (MCMC) algorithm. And you will see how the popular R-software can be used in this context, and encounter some Bayesian R packages .\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eA facility in basic algebra and calculus as well as programming in R is recommended.\u003c/p\u003e38f:T527,\u003cp\u003e\u003cspan lang=\"EN\"\u003e•\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003cspan lang=\"EN\"\u003e Bayes’ Theorem. 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You will identify the key components of the data analytics ecosystem including the different types of data analytics: descriptive, diagnostic, predictive, and prescriptive. You’ll also learn about the different data skill sets and data hires that are needed according to an organization’s size, goals, and data maturity.\u003c/p\u003e\n\u003cp\u003eNo matter your level of technical expertise, you will come away from this course equipped with a step-by-step process of how to create your own data analytics and business strategy. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLearn from data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIdentify key components of the data analytics ecosystem and understand how data connects - well, everything. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlan with data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEffectively diagnose the pits and peaks of your workflows, and how to optimize them through data. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThrive through data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCome away equipped with a step-by-step process of how to create and apply a data-driven business strategy.\u003c/p\u003e391:T696,\u003cp\u003eBy the end of the course, you will be able to…\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eMake data work for you.\u003c/strong\u003e Understand the challenges and opportunities of an effective data analytics ecosystem in businesses of all sizes.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eBecome data literate.\u003c/strong\u003e Identify the purpose and value of the different types of data analytics, including their principles, benefits and challenges.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eGain a competitive edge.\u003c/strong\u003e Identify the factors involved in evaluating data analytics projects, and learn how to use data to gain competitive advantage.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eExpert knowledge\u003cbr /\u003e\n\u003c/strong\u003e Interviews with world-renowned data experts help you understand how to use data analytics to make decisions and improve your business. Digest relevant statistics and facts that widen your understanding of the topic and industry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHollywood storytelling\u003cbr /\u003e\n\u003c/strong\u003e Helios used to be the market leader in bikes, but for some reason the wheels aren’t turning quite like they used to. Joi"])</script><script>self.__next_f.push([1,"n COO Caroline and data consultant Joel as they lead the company through a data transformation, bringing an outdated business model up to speed. Sit in on strategy meetings as the team overcome challenges and make choices that will determine the company’s fate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e‍Visual learning\u003cbr /\u003e\n\u003c/strong\u003e A visual step-by-step approach walks you through the entire process of becoming data-led. Beautiful infographics and engaging gifs explain the most complicated data analytics topics and theories, guiding you through the learning journey.\u003c/p\u003e\n\u003cp\u003eCreated by BoxPlay, an Emmy award-winning company on a mission to democratize career success.\u003c/p\u003e392:T526,\u003cp\u003eWhat is Predictive Analytics? These methods lie behind the most transformative technologies of the last decade, that go under the more general name Artificial Intelligence or AI. In this course, the focus is on the skills that will allow you to fit a model to data, and measure how well it performs. \u003c/p\u003e\n\u003cp\u003eThese skills also go under the names \"machine learning\" and \"data science,\" the latter being a broader term than machine learning or predictive analytics but narrower than AI. This course is part of the Machine Learning Operations (MLOps) Program. We will be doing enough data science so that you get hands-on familiarity with understanding a dataset, fitting a model to it, and generating predictions. As you get further into the program, you will learn how to fit that model into a machine learning pipeline.\u003c/p\u003e\n\u003cp\u003eYou will get hands-on experience with the top techniques in supervised learning: linear and logistic regression modeling, decision trees, neural networks, ensembles, and much more.\u003c/p\u003e\n\u003cp\u003eBut most importantly, by the end of this course, you will know\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhat a predictive model can (and cannot) do, and how its data is structured\u003c/li\u003e\n\u003cli\u003eHow to predict a numerical output, or a class (category)\u003c/li\u003e\n\u003cli\u003eHow to measure the out-of-sample (future)performance of a model\u003c/li\u003e\n\u003c/ul\u003e393:T513,\u003cp\u003eThe 'Statistical and Probabilistic Foundations of AI'"])</script><script>self.__next_f.push([1," course provides an accessible overview of the mathematics and statistics behind fundamental concepts of machine learning, data science, and artificial intelligence. \u003c/p\u003e\n\u003cp\u003eIt covers descriptive and exploratory data analysis and a brief introduction to inferential statistics. Starting with summary statistics, it focuses on visualising data and the resulting key characteristics. This includes box plots, histograms, kernel density estimates, and regression. In addition, the course provides the principles of probability necessary to understand the methods used in inferential statistics and machine learning at an introductory level. Starting with the basic concepts of probability and elementary stochastic models, the course also covers more advanced topics of probability theory. These include multivariate distributions, generating functions, limit theorems, and a brief introduction to stochastic simulation. \u003c/p\u003e\n\u003cp\u003eFinally, a brief introduction to inferential statistics is given. Parametric and non-parametric inferential approaches are discussed. Point and interval estimation and hypothesis testing are also covered.\u003c/p\u003e\n\u003cp\u003eThe presentation is rounded off with many examples and data that are analysed and visualised using R.\u003c/p\u003e394:T4e1,\u003cp\u003eThe Big Data Capstone Project will allow you to apply the techniques and theory you have gained from the four courses in this Big Data MicroMasters program to a medium-scale data science project.\u003c/p\u003e\n\u003cp\u003eWorking with organisations and stakeholders of your choice on a real-world dataset, you will further develop your data science skills and knowledge.\u003c/p\u003e\n\u003cp\u003eThis project will give you the opportunity to deepen your learning by giving you valuable experience in evaluating, selecting and applying relevant data science techniques, principles and theory to a data science problem.\u003c/p\u003e\n\u003cp\u003eThis project will see you plan and execute a reasonably substantial project and demonstrate autonomy, initiative and accountability.\u003c/p\u003e\n\u003cp\u003eYou’ll deepen your learning of social and ethical concerns in r"])</script><script>self.__next_f.push([1,"elation to data science, including an analysis of ethical concerns and ethical frameworks in relation to data selection and data management.\u003c/p\u003e\n\u003cp\u003eBy communicating the knowledge, skills and ideas you have gained to other learners through online collaborative technologies, you will learn valuable communication skills, important for any career. You’ll also deliver a written presentation of your project design, plan, methodologies, and outcomes.\u003c/p\u003e395:T4a7,\u003cp\u003eIn this course, you will begin learning about more advanced multivariate statistical methods that are regularly used in healthcare data analysis. You will also practice applying these statistical methods to examples from the healthcare industry. The topics covered in this course will prepare you for interpreting data and making data-informed decisions in real-world healthcare settings. While the course focuses on application and the use of these statistical methods, there is some discussion of the mathematical underpinning, relevant formulae, and assumptions necessary for understanding the application of statistical methods. \u003c/p\u003e\n\u003cp\u003eThis self-paced course is comprised of written content, video content, step-by-step follow-along activities, and assessments to reinforce your learning (Assessments available to Verified Track learners only). \u003c/p\u003e\n\u003cp\u003eThe course is comprised of 4 modules that you should complete in order, as each subsequent module builds on the previous one. \u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eModule 1: Non-Linear Trends \u003c/li\u003e\n\u003cli\u003eModule 2: Interacting Variables and Finding Outliers \u003c/li\u003e\n\u003cli\u003eModule 3: Logistic Regression \u003c/li\u003e\n\u003cli\u003eModule 4: Logistic Regression Variants\u003c/li\u003e\n\u003c/ul\u003e396:T70f,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eAre you interested in the economic effects of migration? Do you want to take a historical perspective on current debates? \u003c/span\u003eAnd which policies foster integration? These questions do not only figure prominently in today’s, often heated policy debates on immigration. They were already relevant in the past. In this course, we will review the evidence on hist"])</script><script>self.__next_f.push([1,"orical migrant flows, relating past to current debates. \u003c/p\u003e\n\u003cp\u003eStudents will learn key facts and empirical evidence on the economic determinants and labour market consequences of migration from a historical perspective. Topics include the skill composition of migrants; the integration of migrants and their children; and the effect of immigration on wages and employment of native workers. \u003c/p\u003e\n\u003cp\u003eThe course will focus on immigration to Europe and North America since the mid-19th century. We will study each topic in the context of one major migration episode in history. Episodes include the Age of Mass Migration from Europe to the US; forced displacement after World War II; and the mass emigration of Cubans during the Mariel Boatlift of 1980. \u003c/p\u003e\n\u003cp\u003eThis MOOC consists of six chapters. Each chapter will guide you through the material with a combination of videos, figures and interactive quizzes. You will also be introduced to the work of other experts in this field and can deepen your knowledge by reading articles and peer-reviewed papers. In addition, you will have the chance to do your own data analysis—for example, when exploring the relationship between settlement location and reefugee integration. The course is self-paced. However, we advise you to finish one chapter per week. Moreover, you have the option to receive a certificate after successfully completing the course and a final exam.\u003c/p\u003e397:T469,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eOne of the most commonly used tools in data science, pandas is a Python library used to load, process, and analyze datasets using SQL-like queries. Pandas offers several advantages, such as data representation, simpler lines of code, and the ability to handle large sets of data. A number of academic and commercial domains, including finance, economics, statistics, web analytics, and other entities, use pandas as part of their data analytics toolkit.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIn this hands-on guided project, you will learn to perform preliminary data analysis and credit risk analysis on a credit card c"])</script><script>self.__next_f.push([1,"lient dataset using the Python library pandas. You will learn how to import required libraries, explore datasets, analyze data, and visualize the dataset. By the end of this project, you will have learned the fundamentals of data analysis using pandas and developed job-ready skills.\u003c/p\u003e\n\u003cp\u003eYou will be provided with access to a Cloud based-IDE which has all of the required software, including Python pandas, pre-installed. All you need is a recent version of a modern web browser to complete this project.\u003c/p\u003e398:T820,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThe world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic modeling and the related field of statistical inference are the keys to analyzing data and making scientifically sound predictions.\u003c/p\u003e\n\u003cp\u003eProbabilistic models use the language of mathematics. But instead of relying on the traditional \"theorem-proof\" format, we develop the material in an intuitive -- but still rigorous and mathematically-precise -- manner. Furthermore, while the applications are multiple and evident, we emphasize the basic concepts and methodologies that are universally applicable.\u003c/p\u003e\n\u003cp\u003eThe course covers all of the basic probability concepts, including:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003emultiple discrete or continuous random variables, expectations, and conditional distributions\u003c/li\u003e\n\u003cli\u003elaws of large numbers\u003c/li\u003e\n\u003cli\u003ethe main tools of Bayesian inference methods\u003c/li\u003e\n\u003cli\u003ean introduction to random processes (Poisson processes and Markov chains)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe contents of this courseare heavily based upon the corresponding MIT class -- \u003cem\u003eIntroduction to Probability\u003c/em\u003e -- a course that has been offered and continuously refined over more than 50 years. It is a challenging class but will enable you to apply the tools of probability theory to real-world applications or to your research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis course is part of the\u003ca href=\"https://micromasters.mit.edu/ds/\"\u003eMITx MicroMasters Program in Statistics and Data Science\u003c/a\u003e. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit \u003ca href=\"https://micromasters.mit.edu/ds/\"\u003ehttps://micromasters.mit.edu/ds/\u003c/a\u003e.\u003c/strong\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"399:T6d7,\u003cp\u003eStatistics is the science of turning data into insights and ultimately decisions. Behind recent advances in machine learning, data science and artificial intelligence are fundamental statistical principles. The purpose of this class is to develop and understand these core ideas on firm mathematical grounds starting from the construction of estimators and tests, as well as an analysis of their asymptotic performance.\u003c/p\u003e\n\u003cp\u003eAfter developing basic tools to handle parametric models, we will explore how to answer more advanced questions, such as the following:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eHow suitable is a given model for a particular dataset?\u003c/li\u003e\n\u003cli\u003eHow to select variables in linear regression?\u003c/li\u003e\n\u003cli\u003eHow to model nonlinear phenomena?\u003c/li\u003e\n\u003cli\u003eHow to visualize high-dimensional data?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTaking this class will allow you to expand your statistical knowledge to not only include a list of methods, but also the mathematical principles that link them together, equipping you with the tools you need to develop new ones.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis course is part of the\u003ca href=\"https://micromasters.mit.edu/ds/\"\u003eMITx MicroMasters Program in Statistics and Data Science\u003c/a\u003e. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit \u003ca href=\"https://micromasters.mit.edu/ds/\"\u003ehttps://micromasters.mit.edu/ds/\u003c/a\u003e.\u003c/strong\u003e\u003c/p\u003e39a:Tb12,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cem\u003eIf you have specific questions about this course, please contact us at\u003ca href=\"mailto:sds-mm@mit.edu\" rel=\"noopener\" target=\"_blank\"\u003esds-mm@mit.edu\u003c/a\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA time series is a time-stamped set of noisy observations from an underlying process that evolves over time. These observations are dependent on each other in a particular, unknown, fashion. Examples of such series include stock values, value of a currency with respect to the dollar, mean housing prices, the number of Covid-19 infections, or the pitch angle of an airplane during flights. Modeling such processes for the purpose of prediction or intervention is a fundamental problem in statistical learning.\u003c/p\u003e\n\u003cp\u003eThis graduate-level course that will address three lines of development:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLearning Structured Models\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e In this module, we focus on learning the underlying stochastic dynamic model that generates the data. We discuss how algorithms depend on the underlying class of models adopted for this learning. We address the accuracy and reliability of our learned models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction:\u003c/strong\u003e In this module, we make no assumptions on how the data is generated and focus on predicting the next outcome of the process based on past observations. In this context, we analyze \u003cem\u003eMatrix and Tensor Completion Methods\u003c/em\u003e in providing such predictions and we analyze the accuracy of these prediction in the presence of noise, missing data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptimal Intervention and Reinforcement Learning (RL):\u003c/strong\u003e A key ingredient of RL is a simulator that can estimate the value of a reward for a given intervention. In this module course, we build on techniques from RL as well as the first two parts to show how new intervention/control can be derived with better outcomes.\u003c/p\u003e\n\u003cp\u003eThis course will consist of three hands-on projects, in which learners will apply knowledge gained in lectures, build models and implement algorithms to solve problems posed on real time series data sets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis course is part of the\u003ca href=\"https://micromasters.mit.edu/ds/\" rel=\"noopener\" target=\"_blank\"\u003eMITx MicroMasters Program in Statistics and Data Science\u003c/a\u003e. \u003c/strong\u003e\u003cstrong\u003eMaster the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit\u003ca href=\"https://micromasters.mit.edu/ds/\" rel=\"noopener\" target=\"_blank\"\u003ehttps://micromasters.mit.edu/ds/\u003c/a\u003e.\u003cbr /\u003e\n\u003c/strong\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"39b:T605,\u003cp\u003eOur capacity to collect and store data has exponentially increased, but deriving information from data from a scientific perspective requires a foundational knowledge of probability.\u003c/p\u003e\r\n\u003cp\u003eAre you interested in a career in the emerging data science field, or as an actuarial scientist? Or want better to understand statistical theory and mathematical modeling?\u003c/p\u003e\r\n\u003cp\u003eIn this statistics and data analysis course, we will provide an introduction to mathematical probability to help meet your career goals in the exciting new areas becoming known as information science.\u003c/p\u003e\r\n\u003cp\u003eIn this course, we will first introduce basic probability concepts and rules, including Bayes theorem, probability mass functions and CDFs, joint distributions and expected values.\u003c/p\u003e\r\n\u003cp\u003eThen we will discuss a few important probability distribution models with discrete random variables, including Bernoulli and Binomial distributions, Geometric distribution, Negative Binomial distribution, Poisson distribution, Hypergeometric distribution and discrete uniform distribution.\u003c/p\u003e\r\n\u003cp\u003eTo continue learning about probability, enroll in \u003ca href=\"https://prod-edx-mktg-edit.edx.org/course/probability-distribution-models-purduex-416-2x#!\"\u003eProbability: Distribution Models \u0026amp; Continuous Random Variables\u003c/a\u003e, which covers continuous distribution models, central limit theorem and more.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe Center for Science of Information, a National Science Foundation Center, supports learners by offering free educational resources in information science.\u003c/p\u003e39c:T412,\u003cp\u003eIn this statistics and data analysis course, you will learn about continuous random variables and some of the most frequently used probability distribution models including, exponential distribution, Gamma distribution, Beta distribution, and most importantly, normal distribution.\u003c/p\u003e\r\n\u003cp\u003eYou will learn how these distributions can be connected with the Normal distribution by Central limit theorem (CLT). We will discuss Markov and Chebyshev inequalities, order statistics, moment gen"])</script><script>self.__next_f.push([1,"erating functions and transformation of random variables.\u003c/p\u003e\r\n\u003cp\u003eThis course along with the recommended pre-requisite,\u003ca href=\"https://www.edx.org/course/probability-basic-concepts-discrete-purduex-416-1x\"\u003eProbability: Basic Concepts \u0026amp; Discrete Random Variables\u003c/a\u003e,will you give the skills and knowledge to progress towards an exciting career in information and data science.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe Center for Science of Information, a National Science Foundation Center, supports learners by offering free educational resources in information science.\u003c/p\u003e39d:T4d1,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eOne of the most commonly used tools in data science, pandas is a Python library used to load, process, and analyze datasets using SQL-like queries. Pandas offers several advantages, such as data representation, simpler lines of code, and the ability to handle large sets of data. A number of academic and commercial domains, including finance, economics, statistics, web analytics, and other entities, use pandas as part of their data analytics toolkit.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIn this hands-on guided project, you will learn to perform preliminary data analysis and credit risk analysis on a credit card client dataset using the Python library pandas. You will learn how to import required libraries, explore datasets, analyze data, and visualize the dataset. By the end of this project, you will have learned the fundamentals of data analysis using pandas and developed job-ready skills.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eYou will be provided with access to a Cloud based-IDE which has all of the required software, including Python pandas, pre-installed. All you need is a recent version of a modern web browser to complete this project.\u003c/p\u003e39e:Tc38,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIntroducing DATA 88EX, a comprehensive certificate program designed to bridge the gap between Economics and Data Science using Python Jupyter notebooks. Through a series of hands-on exercises and coding applications, students will delve into key concepts spanning Introductory Economics, Microeconomic Theory, Econometrics, Development Economics, Environmental Economics, and Public Economics. This program offers a unique pathway for learners to apply Python programming and data science principles within the realm of economics.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe certificate is structured to motivate the basics of econometrics from a data science perspective, showcasing how coding can illustrate and reinforce economic concepts. Upon successful completion, learners will gain proficiency in reasoning about and solving simple equations used in microeconomics through coding, as well as programmatically creating and interpreting graphs of these equations. Moreover, they will develop a solid understanding of fundamental economic principles.\u003c/p\u003e\r\n\r\n\u003cp\u003eComprising three courses, DATA 88EX covers a spectrum of topics essential for aspiring professionals at the intersection of Data Science and Economics. From Fundamentals of Economics including demand, supply, and public economics, to Advanced Concepts encompassing production, macroeconomic policy, utility, inequality, and development, to Real-World Applications exploring game theory, econometrics, environmental economics, and finance, this program caters to diverse interests and career goals. Targeted learners include students and professionals interested in careers as economists, business analysts, market researchers, investment analysts, and data analysts seeking to enhance their Python skills within an economic context. Prerequisite knowledge includes completion of the Data 8/8X course or familiarity with basic Python programming and the ability to consult documentation for open-source Python libraries like datascience and NumPy. Embark on your journey to mastering the fusion of Economics and Data Science with DATA 88EX.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe idea for the certificate is to take students through a series of exercises to motivate and illustrate key concepts in Economics with examples in Python Jupyter notebooks. The classes will cover concepts from Introductory Economics, Microeconomic Theory, Econometrics, Development Economics, Environmental Economics and Public Economics. The courses will give students a pathway to apply python programming and data science concepts within the discipline of economics.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe certificate aims to motivate the basics of econometrics from a data science perspective, illustrate topics in economics through coding applications , and demonstrate how to construct understanding of concepts in economics by developing and coding examples.\u003c/p\u003e\r\n\r\n\u003cp\u003eAfter successfully passing the certificate, students would be able to reason about and solve simple equations used in microeconomics through coding , programmatically create and interpret graphs of simple equations used in microeconomics, and understand basic concepts in economics.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"39f:Ta33,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIn today's work environment, it is vital for professionals to interpret the large amount of quantitative information to which they have access, more and more we have to interpret large volumes of data (big data), either by analyzing financial reports, case studies or presenting data effectively. This Professional Certification program, offered by EGADE Business School, the number one school in Mexico and Latin America in Finance and Administration, will give you the opportunity to successfully develop these skills that are highly required by companies in today's job market.\u003c/p\u003e\r\n \r\n\u003cp\u003eWith the help of experts, you will use interactive exercises to learn the fundamentals of mathematics, statististical analysiscs, data science and finance, all applied to business.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe topics covered in this Professional Certification program will allow you to obtain theoretical and practical training at the master's or postgraduate level to better understand the functions of business management; additionally, it will prepare you to continue your studies successfully by deciding to pursue a Master's Degree in Finance or an MBA at EGADE.\u003c/p\u003e\r\n\r\n\u003cp\u003eYou will need prior experience using Microsoft Excel.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program will provide you with the key concepts you need to make successful managerial decision makings in your company, both in the short term and in the long term. For example, to make financial decisions, monetary decisions or investment decisions, it is necessary to know the interest rate, opportunity costs, master financial models, know the capital budget and working capital, the value of money, risk management.analyze risk. In general, this program will give you the quantitative skills and problem solving skills you need professionally to be successful in real world making business decisions.\u003c/p\u003e\r\n\r\n\u003cp\u003eQuantitative skills like financial modeling give you a framework for creating new tools to compare investment options and determine which will yield the best return based on a set of inputs and assumptions.Corporate finance can help the company in the creation of value, to analyze its financial resources / equity, to know the real assets that the company has, or to select the best investment projects based on the current value of money, and to know the analysis used in the processes.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe program will also cover data science, which helps in making successful operational decisions and managerial decisions based on data analysis, for example, taking into account the information provided by the data you can determine the efficient use of resources.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3a0:T494,Bayes’ Theorem. Differences between classical (frequentist) and Bayesian inference.,Posterior inference: summarizing posterior distributions, credible intervals, posterior probabilities, posterior predictive distributions and data visualization.,Gamma-poisson, beta-binomial and normal conjugate models for data analysis.,Bayesian regression analysis and analysis of variance (ANOVA).,Use of simulations for posterior inference. Simple applications of Markov chain-Monte Carlo (MCMC) methods and their implementation in R.,Bayesian cluster analysis.,Model diagnostics and comparison.,Make sure to answer the actual research question rather than “apply methods to the data”,Using latent (unobserved) variables and dealing with missing data.,Multivariate analysis within the context of mixed effects linear regression models. Structure, assumptions, diagnostics and interpretation. Posterior inference and model selection.,Why Monte Carlo integration works and how to implement your own MCMC Metropolis-Hastings algorithm in R.,Bayesian model averaging in the context of change-point problem. Pinpointing the time of change and obtaining uncertainty estimates for it.3a1:T4eb,\u003cp\u003eAn introduction to programming using a language called Python. Learn how to read and write code as well as how to test and \"debug\" it. Designed for students with or without prior programming experience who'd like to learn Python specifically. Learn about functions, arguments, and return values (oh my!); variables and types; conditionals and Boolean expressions; and loops. Learn how to handle exceptions, find and fix bugs, and write unit tests; use third-party libraries; validate and extract data with regular expressions; model real-world entities with classes, objects, methods, and properties; and read and write files. Hands-on opportunities for lots of practice. Exercises inspired by real-world programming problems. No software required except for a web browser, or you can write code on your own PC or Mac. \u003c/p\u003e\n\u003cp\u003eWhereas \u003ca href=\"https://www"])</script><script>self.__next_f.push([1,".edx.org/course/introduction-computer-science-harvardx-cs50x\"\u003eCS50x\u003c/a\u003e itself focuses on computer science more generally as well as programming with C, Python, SQL, and JavaScript, this course, aka CS50P, is entirely focused on programming with Python. You can take CS50P before CS50x, during CS50x, or after CS50x. But for an introduction to computer science itself, you should still take CS50x!\u003c/p\u003e3a2:T6da,\u003cp\u003e\u003cspan lang=\"EN-CA\"\u003eMuch of the world's data lives in databases. SQL (or Structured Query Language) is a powerful programming language that is used for communicating with and manipulating data in databases. A working knowledge of databases and SQL is necessary for anyone who wants to start a career in Data Engineering, Data Analytics or Data Science. The purpose of this course is to introduce relational database (RDBMS) concepts and help you learn and apply foundational and intermediate knowledge of the SQL language.\u003c/span\u003e\u003cspan lang=\"EN-CA\"\u003e\u003c/span\u003e\u003cspan lang=\"EN-CA\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eYou will start with performing basic Create, Read, Update and Delete (CRUD) operations using CREATE, SELECT, INSERT, UPDATE and DELETE statements. You will then learn to filter, order, sort, and aggregate data. You will also work with functions, perform sub-selects and nested queries, as well as access multiple tables in the database.\u003cspan lang=\"EN-CA\"\u003e\u003c/span\u003e\u003cspan lang=\"EN-CA\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe emphasis in this course is on hands-on, practical learning. As such, you will work with real database systems, use real tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs, you will practice building and running SQL queries. At the end of the course you will apply and demonstrate your skills with a final project.\u003cspan lang=\"EN-CA\"\u003e\u003c/span\u003e\u003cspan lang=\"EN-CA\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe SQL skills you learn in this course will be applicable to a variety of RDBMSes such as MySQL, PostgreSQL, IBM Db2, Oracle, SQL Server and others.\u003cspan lang=\"EN-CA\"\u003e\u003c/span\u003e\u003cspan lang=\"EN-CA\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e"])</script><script>self.__next_f.push([1,"No prior knowledge of databases, SQL or programming is required, however some basic data literacy is beneficial.\u003c/p\u003e3a3:T5df,\u003cp\u003eEl futuro pertenece a la ciencia de datos y a quienes la entiendan. Al igual que el petróleo y el gas impulsaron las economías de los siglos XX y XXI, los datos impulsan cada vez mas la innovación y la economía global a medida que avanzamos hacia una nueva era denominada la revolución digital.\u003c/p\u003e\n\u003cp\u003eLas empresas que están cambiando a una mentalidad de datos van a obtener primero una enorme ventaja competitiva.\u003c/p\u003e\n\u003cp\u003eEsto se ha convertido en una verdad universal: las empresas modernas se están inundando de big data. El año pasado, McKinsey estimó que las iniciativas de Big Data en el sistema de salud de los Estados Unidos, \"podrían representar de $300 mil millones a $450 mil millones en gastos de salud reducidos o del 12 al 17 por ciento de la línea de base de $2.6 billones en costos de salud de los Estados Unidos\". Por otro lado, sin embargo se estima que los datos erróneos le cuestan a los Estados Unidos apróximadamente $3.1 trillones de dólares al año.\u003c/p\u003e\n\u003cp\u003eAunque la ciencia de datos representará ventajas puntuales para las empresas como mitigar el riesgo y/o el fraude; entregar al cliente productos relevantes y tener experiencias personalizadas al mismo, es sin embargo, importante notar que la ciencia de datos podrá agregar un valor a cuaquier negocio, siempre y cuando pueda usar bien sus datos. El análisis de la información y la inteligencia de negocios ayudan a las organizaciones a tomar decisiones inteligentes basadas en el análisis de datos.\u003c/p\u003e3a4:Tb7a,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThe success of machine learning, and in particular deep learning in image recognition and natural language processing applications, has created high expectations and their use has rapidly spread to many different areas. The financial sector is no exception and the last six years have seen an increase in these types of models in financial, banking and insurance contexts. Data science and advanced analytics teams in the financial and insurance community are implementing these models regularly and have found a place for them in their toolbox.\u003c/p\u003e\n\u003cp\u003eIn this course, we will first present a review of some of the applications of machine learning and deep learning. We will then illustrate their use in financial applications through concrete examples that we have seen have sparked interest in the industry. Our examples will illustrate how we can add value through ad hoc construction of architectures rather than a simple exercise of replacing classical models with more complex ones, such as multi-layer networks.\u003c/p\u003e\n\u003cp\u003eWe will see\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNeural network architectures on graphs to integrate new information dimensions in financial markets and bitcoin transactions\u003c/li\u003e\n\u003cli\u003ePortfolio design using reinforcement learning and\u003c/li\u003e\n\u003cli\u003eNatural Language Processing and information extraction methods from financial disclosures in the in an ESG and sustainable finance context\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis course was developed by IVADO and Fin-ML as part of a workshop that takes place yearly in Montréal, since 2018. You will be accompanied throughout and given concrete examples by six international experts from both Academia and Industry.\u003c/p\u003e\n\u003cp\u003eThe course is primarily intended for industry professionals and academics with intermediate knowledge of mathematics and programming (ideally Python). Graduate students in data science and quantitative finance (mainly those who are not yet familiar with machine learning and deep learning) may find this content instructive and compelling. The content of this course will also be of great use to whomever uses or is interested in AI, in any other way. Previous experience in the financial industry is not necessary to follow this course.\u003c/p\u003e\n\u003cp\u003eThis course is brought to you by IVADO, Fin-ML and Université de Montréal.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u003ca href=\"https://ivado.ca/\"\u003eIVADO\u003c/a\u003e\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e is a Québec-wide collaborative institute in the field of digital intelligence.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u003ca href=\"https://fin-ml.ca/fr/\"\u003eFin-ML\u003c/a\u003e\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e is a nationwide network of researchers working at the intersection of data science, quantitative finance, and business analytics.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u003ca href=\"https://catalogue.edulib.org/en/organizations/umontreal/\"\u003eUniversité de Montréal\u003c/a\u003e\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e is one of the world’s leading research universities.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e"])</script><script>self.__next_f.push([1,"3a5:T684,\u003cp\u003eThe demand for gen AI is forecast to grow over 46% annually by 2030 (Source: Statista). AI engineers and developers, data scientists, machine learning engineers, and other AI professionals with gen AI skills are highly sought-after. This course builds in-demand skills in large language model (LLM) architecture and data preparation employers are looking for. \u003c/p\u003e\n\u003cp\u003eDuring the course, you’ll learn about real-world applications using generative AI. You’ll gain insights into gen AI architectures and models, such as recurrent neural networks (RNNs), transformers, generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models. You’ll use different training approaches for each model. Plus, you’ll explore LLMs such as generative pre-trained transformers (GPT) and bidirectional encoder representations from transformers (BERT). \u003c/p\u003e\n\u003cp\u003eAdditionally, you’ll gain a detailed understanding of the tokenization process, tokenization methods, and the use of tokenizers for word-based, character-based, and subword-based tokenization. You’ll get hands-on experience using data loaders for training generative AI models, using PyTorch libraries, and generative AI libraries in Hugging Face. Plus, you’ll implement tokenization and create an NLP data loader. \u003c/p\u003e\n\u003cp\u003eIf you’re looking to master gen AI LLM architecture and data preparation, ENROLL TODAY and get ready to power up your resume with skills employers need! \u003c/p\u003e\n\u003cp\u003ePrerequisites: To enroll for this course, a basic knowledge of Python and PyTorch and an awareness of machine learning and neural networks would be an advantage, though not strictly required.\u003c/p\u003e3a6:T5c2,\u003cp\u003eThis is the second of three courses in the Machine Learning Operations Program using Azure Machine Learning.\u003c/p\u003e\n\u003cp\u003eData Science, AI, and Machine Learning projects can deliver an amazing return on investment. But, in practice, most projects that look great in the lab (and would work if implemented!) never see the light of day. They could save or make the or"])</script><script>self.__next_f.push([1,"ganization millions of dollars but never make it all the way into production. What’s going on? It turns out that making decisions in a whole new way is a big challenge to implement--for many technical, business andhuman-naturereasons. After decades of experience though, our team has learned how to turn this around and actually get working models into production the great majority of the time. A key part of deployment is excellence in data engineering, and is why we developed this course: \u003cstrong\u003eMLOps1 (Azure): Deploying AI \u0026amp; ML Models in Production using Microsoft Azure Machine Learning.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYou will get hands on experience with topics like data pipelines, data and model “versioning”, model storage, data artifacts, and more.\u003c/p\u003e\n\u003cp\u003eMost importantly, by the end of this course, you will know...\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhat data engineers need to know to work effectively with data scientists\u003c/li\u003e\n\u003cli\u003eHow to embed a predictive model in a pipeline that takes in data and outputs predictions automatically\u003c/li\u003e\n\u003cli\u003eHow to moniter the model’s performance and follow best practices\u003c/li\u003e\n\u003c/ul\u003e3a7:T5af,\u003cp\u003eGet ready to dive into the exciting world of Python programming! This comprehensive course is designed to provide you with a deep understanding of fundamental Python techniques, including data structures, control statements, and functions. You'll also explore advanced concepts such as iterators, file handling, and exceptions, giving you a well-rounded foundation in Python programming.\u003c/p\u003e\n\u003cp\u003eBut that's not all! You'll also get hands-on experience with powerful libraries like Pandas, NumPy, and MatPlotLib, which are essential for success in Data Science and Machine Learning. These libraries will enable you to manipulate and visualize data like a pro, making your insights more impactful and your work more efficient.\u003c/p\u003e\n\u003cp\u003eThroughout the course, you'll complete weekly programming exercises, giving you the opportunity to apply and practice what you've learned. This hands-on experience will help you build confidenc"])</script><script>self.__next_f.push([1,"e in your programming skills and enable you to execute programming solutions with ease.\u003c/p\u003e\n\u003cp\u003eBy the end of the course, you'll be able to critically evaluate and interpret the results of your code, making you a valuable asset in any data-driven field. Whether you're looking to start a career in Data Science, Machine Learning, or simply want to expand your programming skills, this course is the perfect starting point. So, are you ready to master Python programming and unlock a world of opportunities? Let's get started!\u003c/p\u003e3a8:T5a7,\u003cp\u003eThis is the second of three courses in the Machine Learning Operations Program using Amazon Web Services (AWS).\u003c/p\u003e\n\u003cp\u003eData Science, AI, and Machine Learning projects can deliver an amazing return on investment. But, in practice, most projects that look great in the lab (and would work if implemented!) never see the light of day. They could save or make the organization millions of dollars but never make it all the way into production. What’s going on? It turns out that making decisions in a whole new way is a big challenge to implement--for many technical, business and human-nature reasons. After decades of experience though, our team has learned how to turn this around and actually get working models into production the great majority of the time. A key part of deployment is excellence in data engineering, and is why we developed this course: MLOps1(AWS): Deploying AI \u0026amp; ML Models in Production.\u003c/p\u003e\n\u003cp\u003eYou will get hands-on experience with topics like data pipelines, data and model “versioning”, model storage, data artifacts, and more.\u003c/p\u003e\n\u003cp\u003eMost importantly, by the end of this course, you will know...\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eWhat data engineers need to know to work effectively with data scientists\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHow to embed a predictive model in a pipeline that takes in data and outputs predictions automatically\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHow to monitor the model’s performance and follow best practices\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e3a9:T482,\u003cp\u003e\u003cspan lang=\"EN\"\u003eMachine learning has changed the gam"])</script><script>self.__next_f.push([1,"e for sports predictions. Popular Python libraries like LIME and SHAP are used to interpret and explain models. Even if you are not a soccer fan or working in the sports industry, machine learning skills are in demand in many industries. The skills needed to import and use data to create predictive models are both practical and valuable.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIn this hands-on guided project, you’ll develop practical Python, pandas, numpy, sklearn, seaborn, matplotlib, seaborn, LIME, and SHAP skills to process data using the 2022 World Cup teams’ data. Then, you’ll train a model to predict the outcome of the group stages.\u003c/p\u003e\n\u003cp\u003eAfter completing this project, you will have practical experience working with Python machine-learning tools.\u003c/p\u003e\n\u003cp\u003eGet started fast. This hands-on guided project uses a browser-accessible development environment with the technologies and libraries you need, preinstalled—including the Python IDE—saving you setup time and complications. Also, note that this platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.\u003c/p\u003e3aa:T5a0,\u003cp\u003eThis is the second of three courses in the Machine Learning Operations Program using Google Cloud Platform (GCP).\u003c/p\u003e\n\u003cp\u003eData Science, AI, and Machine Learning projects can deliver an amazing return on investment. But, in practice, most projects that look great in the lab (and would work if implemented!) never see the light of day. They could save or make the organization millions of dollars but never make it all the way into production. What’s going on? It turns out that making decisions in a whole new way is a big challenge to implement--for many technical, business and human-nature reasons. After decades of experience though, our team has learned how to turn this around and actually get working models into production the great majority of the time. A key part of deployment is excellence in data engineering, and is why we developed this course: \u003cstrong\u003eMLOps1 (GCP): Deploying AI \u0026amp; ML Models in Production\u003c/strong\u003e.\u003c/"])</script><script>self.__next_f.push([1,"p\u003e\n\u003cp\u003eYou will get hands-on experience with topics like data pipelines, data and model “versioning”, model storage, data artifacts, and more.\u003c/p\u003e\n\u003cp\u003eMost importantly, by the end of this course, you will know...\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhat data engineers need to know to work effectively with data scientists\u003c/li\u003e\n\u003cli\u003eHow to embed a predictive model in a pipeline that takes in data and outputs predictions automatically\u003c/li\u003e\n\u003cli\u003eHow to monitor the model’s performance and follow best practices\u003c/li\u003e\n\u003c/ul\u003e3ab:T4f5,\u003cp\u003e\u003cspan lang=\"EN\"\u003eMachine learning has changed the game for sports predictions. Popular Python libraries like LIME and SHAP are used to interpret and explain models. Even if you are not a soccer fan or working in the sports industry, machine learning skills are in demand in many industries. The skills needed to import and use data to create predictive models are both practical and valuable.\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIn this hands-on guided project, you’ll develop practical Python, pandas, numpy, sklearn, seaborn, matplotlib, seaborn, LIME, and SHAP skills to process data using the 2022 World Cup teams’ data. Then, you’ll train a model to predict the outcome of the group stages.\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eAfter completing this project, you will have practical experience working with Python machine-learning tools.\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eGet started fast. This hands-on guided project uses a browser-accessible development environment with the technologies and libraries you need, preinstalled—including the Python IDE—saving you setup time and complications. Also, note that this platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari.\u003c/p\u003e3ac:T839,"])</script><script>self.__next_f.push([1,"\u003cp\u003eMachine Learning Operations (MLOps) lies at the core of the AI Engineering function. In Statistics.com’s MLOps with Azure program you will learn to combine data engineering and data science skills to deploy machine learning models.\u003c/p\u003e\r\n\r\n\u003cp\u003eMost of the work in deploying AI models does not lie in developing models. Rather, it lies in developing, monitoring and maintaining an automated, self-monitoring data pipeline through a model and into actions. The common practice of tossing a project back and forth between pure data scientists and pure data engineers leads to delay and errors. This has created a need for AI engineers who have knowledge of each function. Mastering machine learning deployment skills on the Microsoft Azure platform is a sure path to career success.\u003c/p\u003e\r\n \r\n\u003cp\u003eIn this course, you will learn how to work with data scientists to deploy machine learning models that can learn from data, and generate predictions, recommendations or decisions. This process usually is automated and that is where MLOps and AI engineering skills are needed.\u003c/p\u003e\r\n\r\n\u003cp\u003eYou will focus on developing the skills needed to create a Microsoft Azure pipeline that:\r\n\u003cul\u003e\r\n\u003cli\u003eIngests data to train a predictive model\u003c/li\u003e\r\n\u003cli\u003eFeeds the model as it operates\u003c/li\u003e\r\n\u003cli\u003eScores the data on an ongoing basis\u003c/li\u003e\r\n\u003cli\u003eOutputs an action\u003c/li\u003e\r\n\u003cli\u003eIntegrates into business applications\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eAdditionally, you will learn to develop the pipeline so that it will continuously monitor several points of operation, including the incoming data (for data drift) and the decision outputs (for anomalies).\r\nStatistics.com is the training platform of Elder Research (elderresearch.com), an internationally recognized data analytics consulting firm that, since 1995, has consulted for hundreds of leading businesses in data strategy, data science, and data engineering. Elder Research leverages the wisdom gained by solving a wide variety of real-world problems to infuse their education programs on the Statistics.com platform with the most cutting-edge training that can be applied day one.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3ad:T831,"])</script><script>self.__next_f.push([1,"\u003cp\u003eMachine Learning Operations (MLOps) lies at the core of the AI Engineering function. In Statistics.com’s MLOps with AWS program you will learn to combine data engineering and data science skills to deploy machine learning models.\u003c/p\u003e\r\n\r\n\u003cp\u003eMost of the work in deploying AI models does not lie in developing models. Rather, it lies in developing, monitoring and maintaining an automated, self-monitoring data pipeline through a model and into actions. The common practice of tossing a project back and forth between pure data scientists and pure data engineers leads to delay and errors. This has created a need for AI engineers who have knowledge of each function. Mastering machine learning deployment skills on the Amazon Web Services platform is a sure path to career success.\u003c/p\u003e\r\n \r\n\u003cp\u003eIn this course, you will learn how to work with data scientists to deploy machine learning models that can learn from data, and generate predictions, recommendations or decisions. This process usually is automated and that is where MLOps and AI engineering skills are needed.\u003c/p\u003e\r\n\r\n\u003cp\u003eYou will focus on developing the skills needed to create an AWS pipeline that:\r\n\u003cul\u003e\r\n\u003cli\u003eIngests data to train a predictive model\u003c/li\u003e\r\n\u003cli\u003eFeeds the model as it operates\u003c/li\u003e\r\n\u003cli\u003eScores the data on an ongoing basis\u003c/li\u003e\r\n\u003cli\u003eOutputs an action\u003c/li\u003e\r\n\u003cli\u003eIntegrates into business applications\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eAdditionally, you will learn to develop the pipeline so that it will continuously monitor several points of operation, including the incoming data (for data drift) and the decision outputs (for anomalies).\r\nStatistics.com is the training platform of Elder Research (elderresearch.com), an internationally recognized data analytics consulting firm that, since 1995, has consulted for hundreds of leading businesses in data strategy, data science, and data engineering. Elder Research leverages the wisdom gained by solving a wide variety of real-world problems to infuse their education programs on the Statistics.com platform with the most cutting-edge training that can be applied day one.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3ae:T828,"])</script><script>self.__next_f.push([1,"\u003cp\u003eMachine Learning Operations (MLOps) lies at the core of the AI Engineering function. In Statistics.com’s MLOps with GCP program you will learn to combine data engineering and data science skills to deploy machine learning models.\u003c/p\u003e\r\n\r\n\u003cp\u003eMost of the work in deploying AI models does not lie in developing models. Rather, it lies in developing, monitoring and maintaining an automated, self-monitoring data pipeline through a model and into actions. The common practice of tossing a project back and forth between pure data scientists and pure data engineers leads to delay and errors. This has created a need for AI engineers who have knowledge of each function. Mastering machine learning deployment skills on the Google Cloud platform is a sure path to career success.\u003c/p\u003e\r\n \r\n\u003cp\u003eIn this course, you will learn how to work with data scientists to deploy machine learning models that can learn from data, and generate predictions, recommendations or decisions. This process usually is automated and that is where MLOps and AI engineering skills are needed.\u003c/p\u003e\r\n \r\n\u003cp\u003eYou will focus on developing the skills needed to create a GCP pipeline that:\r\n\u003cul\u003e\r\n\u003cli\u003eIngests data to train a predictive model\u003c/li\u003e\r\n\u003cli\u003eFeeds the model as it operates\u003c/li\u003e\r\n\u003cli\u003eScores the data on an ongoing basis\u003c/li\u003e\r\n\u003cli\u003eOutputs an action\u003c/li\u003e\r\n\u003cli\u003eIntegrates into business applications\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eAdditionally, you will learn to develop the pipeline so that it will continuously monitor several points of operation, including the incoming data (for data drift) and the decision outputs (for anomalies).\r\nStatistics.com is the training platform of Elder Research (elderresearch.com), an internationally recognized data analytics consulting firm that, since 1995, has consulted for hundreds of leading businesses in data strategy, data science, and data engineering. Elder Research leverages the wisdom gained by solving a wide variety of real-world problems to infuse their education programs on the Statistics.com platform with the most cutting-edge training that can be applied day one.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3af:Tb4d,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis course teaches the R programming language in the context of statistical data and statistical analysis in the life sciences.\u003c/p\u003e\n\u003cp\u003eWe will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R code. We provide R programming examples in a way that will help make the connection between concepts and implementation. Problem sets requiring R programming will be used to test understanding and ability to implement basic data analyses. We will use visualization techniques to explore new data sets and determine the most appropriate approach. We will describe robust statistical techniques as alternatives when data do not fit assumptions required by the standard approaches. By using R scripts to analyze data, you will learn the basics of conducting reproducible research.\u003c/p\u003e\n\u003cp\u003eGiven the diversity in educational background of our students we have divided the course materials into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. We start with simple calculations and descriptive statistics. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.\u003c/p\u003e\n\u003cp\u003eThese courses make up two Professional Certificates and are self-paced:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis for Life Sciences:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistics-and-r\"\u003ePH525.1x: Statistics and R for the Life Sciences\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-linear-models-and-matrix-algebra\"\u003ePH525.2x: Introduction to Linear Models and Matrix Algebra\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistical-inference-and-modeling-for-high-throug\"\u003ePH525.3x: Statistical Inference and Modeling for High-throughput Experiments\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/high-dimensional-data-analysis\"\u003ePH525.4x: High-Dimensional Data Analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eGenomics Data Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-bioconductor-annotation-and-analys\"\u003ePH525.5x: Introduction to Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/case-studies-in-functional-genomics\"\u003ePH525.6x: Case Studies in Functional Genomics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/advanced-bioconductor\"\u003ePH525.7x: Advanced Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis class was supported in part by NIH grant R25GM114818.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3b0:T8c0,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cstrong\u003eAbout the Database Series of Courses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\"Databases\" was one of Stanford's three inaugural massive open online courses in the fall of 2011. It has been offered in synchronous and then in self-paced versions on a variety of platforms continuously since 2011. The material is now being offered as a set of five self-paced courses, which can be taken in a variety of ways to learn about different aspects of databases. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRelational Databases and SQL\u003c/em\u003e is the most popular course in the Databases series. It is applicable to learners seeking to gain a strong understanding of relational databases, and to master SQL, the long-accepted standard query language for relational database systems. Additional courses focus on advanced concepts in relational databases and SQL, formal foundations and database design methodologies, and semistructured data.\u003c/p\u003e\n\u003cp\u003eAll of the courses are based around video lectures and demos. Many of them include quizzes between video segments to check understanding, in-depth standalone quizzes, and/or a variety of automatically-checked interactive exercises. Each course also includes an unmoderated discussion forum and pointers to readings and resources. The courses are described briefly below. Taught by Professor Jennifer Widom, the overall curriculum draws from Stanford's popular longstanding Databases course.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy Learn About Databases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDatabases are incredibly prevalent -- they underlie technology used by most people every day if not every hour. Databases reside behind a huge number of websites; they're a crucial component of telecommunications systems, banking systems, video games, and just about any other software system or electronic device that maintains some amount of persistent information. In addition to persistence, database systems provide a number of other properties that make them exceptionally useful and convenient: reliability, efficiency, scalability, concurrency control, data abstractions, and high-level query languages. Databases are so ubiquitous and important that computer science graduates frequently cite their database class as the one most useful to them in their industry or graduate-school careers.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3b1:T805,"])</script><script>self.__next_f.push([1,"\u003cp\u003eStanford's online offering in Databases is now available as a set of five self-paced courses:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDatabases: Relational Databases and SQL\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIntroduction to the relational model and concepts in relational databases and relational database management systems\u003c/li\u003e\n\u003cli\u003eComprehensive coverage of SQL, the long-accepted standard query language for relational database management systems\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDatabases: Advanced Topics in SQL (prerequisite: Relational Databases and SQL)\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreating indexes for increased query performance\u003c/li\u003e\n\u003cli\u003eUsing transactions for concurrency control and failure recovery\u003c/li\u003e\n\u003cli\u003eDatabase constraints: key, referential integrity, and \"check\" constraints\u003c/li\u003e\n\u003cli\u003eDatabase triggers\u003c/li\u003e\n\u003cli\u003eHow views are created, used, and updated in relational databases\u003c/li\u003e\n\u003cli\u003eAuthorization in relational databases\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDatabases: OLAP and Recursion\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eStar schemas, the data cube concept, and On-Line Analytical Processing (OLAP) features in relational databases including the Cube and Rollup operators\u003c/li\u003e\n\u003cli\u003eThe SQL standard for queries over recursively-defined relations\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDatabases: Modeling and Theory\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelational algebra – the algebraic query language that provides the formal foundations of SQL\u003c/li\u003e\n\u003cli\u003eDependency theory and normal forms in relational databases as the basis of schema design\u003c/li\u003e\n\u003cli\u003eThe data-modeling component of the Unified Modeling Language (UML), how UML diagrams are translated to relations\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDatabases: Semistructured Data\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe XML model for semistructured and self-describing data, including DTDs and some features of XML Schema\u003c/li\u003e\n\u003cli\u003eThe JSON model for human-readable structured or semistructured data\u003c/li\u003e\n\u003cli\u003eThe XPath language for processing XML data, and many features of the more advanced XQuery language\u003c/li\u003e\n\u003cli\u003eAn introduction to the XSLT rule-based language for querying and transforming XML data\u003c/li\u003e\n\u003c/ul\u003e"])</script><script>self.__next_f.push([1,"3b2:T56a,\u003cp\u003eWhat’s living in your food? Many of the foods that we consume daily owe their distinct characteristics and flavors to microbes, specifically through a biochemical process of fermentation (using bacteria, fungi, and other microorganisms to produce diverse foods). Gourmands and everyday consumers can quickly name some of the most popular fermented foods we consume—beer, yogurt, pickles—but, what about that coffee you drank this morning, or the chocolate bar you are saving for later? \u003c/p\u003e\n\u003cp\u003eThrough hands-on, at-home exercises, you will experiment with your food to grow your own microbial environments to make mead, sourdough, tempeh, and more—and discover the important role science plays in food fermentation. In Food Fermentation: The Science of Cooking with Microbes, you will explore the history of food and beverage fermentations and how it changes and enhances flavors, aromas, and tastes. You will engage with your peers in kitchen science, discussing how and why fermentation does or does not happen and what conditions you should consider to create the right growth opportunities. \u003c/p\u003e\n\u003cp\u003eFrom chemistry to microbiology to your dinner plate, this course will analyze the role of microbes in production, preservation, and enhancement of diverse foods across a variety of culinary traditions.\u003c/p\u003e\n\u003cp\u003eIgnore the old adage. Are you ready to play with your food?\u003c/p\u003e3b3:T8c0,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cstrong\u003eAbout the Database Series of Courses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\"Databases\" was one of Stanford's three inaugural massive open online courses in the fall of 2011. It has been offered in synchronous and then in self-paced versions on a variety of platforms continuously since 2011. The material is now being offered as a set of five self-paced courses, which can be taken in a variety of ways to learn about different aspects of databases. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRelational Databases and SQL\u003c/em\u003e is the most popular course in the Databases series. It is applicable to learners seeking to gain a strong understanding of relational databases, and to master SQL, the long-accepted standard query language for relational database systems. Additional courses focus on advanced concepts in relational databases and SQL, formal foundations and database design methodologies, and semistructured data.\u003c/p\u003e\n\u003cp\u003eAll of the courses are based around video lectures and demos. Many of them include quizzes between video segments to check understanding, in-depth standalone quizzes, and/or a variety of automatically-checked interactive exercises. Each course also includes an unmoderated discussion forum and pointers to readings and resources. The courses are described briefly below. Taught by Professor Jennifer Widom, the overall curriculum draws from Stanford's popular longstanding Databases course.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy Learn About Databases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDatabases are incredibly prevalent -- they underlie technology used by most people every day if not every hour. Databases reside behind a huge number of websites; they're a crucial component of telecommunications systems, banking systems, video games, and just about any other software system or electronic device that maintains some amount of persistent information. In addition to persistence, database systems provide a number of other properties that make them exceptionally useful and convenient: reliability, efficiency, scalability, concurrency control, data abstractions, and high-level query languages. Databases are so ubiquitous and important that computer science graduates frequently cite their database class as the one most useful to them in their industry or graduate-school careers.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3b4:T523,\u003cp\u003eThis course is one of five self-paced courses on the topic of Databases, originating as one of Stanford's three inaugural massive open online courses released in the fall of 2011. The original \"Databases\" courses are now all available on edx.org.\u003c/p\u003e\n\u003cp\u003eThis course is broad and practical, covering indexes, transactions, constraints, triggers, views, and authorization, all in the context of relational database systems and the SQL language. This course builds on concepts introduced in \u003cem\u003eDatabases: Relational Databases and SQL\u003c/em\u003e and is recommended for learners seeking to advance their understanding and use of relational databases.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe Indexes and Transactions section of this course covers two important features of database systems from the application-builder's perspective: indexing for increased performance, and transactions for concurrency control and failure recovery.\u003c/li\u003e\n\u003cli\u003eThe Constraints and Triggers section of this course explains key, referential integrity, and \"check\" constraints, followed by comprehensive coverage of database triggers.\u003c/li\u003e\n\u003cli\u003eThe Views and Authorization section of this course provides extensive coverage of how database views can be created, used, and updated, and introduces standard techniques for authorization in relational databases.\u003c/li\u003e\n\u003c/ul\u003e3b5:T805,"])</script><script>self.__next_f.push([1,"\u003cp\u003eStanford's online offering in Databases is now available as a set of five self-paced courses:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDatabases: Relational Databases and SQL\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIntroduction to the relational model and concepts in relational databases and relational database management systems\u003c/li\u003e\n\u003cli\u003eComprehensive coverage of SQL, the long-accepted standard query language for relational database management systems\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDatabases: Advanced Topics in SQL (prerequisite: Relational Databases and SQL)\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreating indexes for increased query performance\u003c/li\u003e\n\u003cli\u003eUsing transactions for concurrency control and failure recovery\u003c/li\u003e\n\u003cli\u003eDatabase constraints: key, referential integrity, and \"check\" constraints\u003c/li\u003e\n\u003cli\u003eDatabase triggers\u003c/li\u003e\n\u003cli\u003eHow views are created, used, and updated in relational databases\u003c/li\u003e\n\u003cli\u003eAuthorization in relational databases\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDatabases: OLAP and Recursion\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eStar schemas, the data cube concept, and On-Line Analytical Processing (OLAP) features in relational databases including the Cube and Rollup operators\u003c/li\u003e\n\u003cli\u003eThe SQL standard for queries over recursively-defined relations\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDatabases: Modeling and Theory\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelational algebra – the algebraic query language that provides the formal foundations of SQL\u003c/li\u003e\n\u003cli\u003eDependency theory and normal forms in relational databases as the basis of schema design\u003c/li\u003e\n\u003cli\u003eThe data-modeling component of the Unified Modeling Language (UML), how UML diagrams are translated to relations\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDatabases: Semistructured Data\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe XML model for semistructured and self-describing data, including DTDs and some features of XML Schema\u003c/li\u003e\n\u003cli\u003eThe JSON model for human-readable structured or semistructured data\u003c/li\u003e\n\u003cli\u003eThe XPath language for processing XML data, and many features of the more advanced XQuery language\u003c/li\u003e\n\u003cli\u003eAn introduction to the XSLT rule-based language for querying and transforming XML data\u003c/li\u003e\n\u003c/ul\u003e"])</script><script>self.__next_f.push([1,"3b6:T996,"])</script><script>self.__next_f.push([1,"\u003cp\u003eMatrix Algebra underlies many of the current tools for experimental design and the analysis of high-dimensional data. In this introductory online course in data analysis, we will use matrix algebra to represent the linear models that commonly used to model differences between experimental units. We perform statistical inference on these differences. Throughout the course we will use the R programming language to perform matrix operations.\u003c/p\u003e\n\u003cp\u003eGiven the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. You will need to know some basic stats for this course. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.\u003c/p\u003e\n\u003cp\u003eThese courses make up two Professional Certificates and are self-paced:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis for Life Sciences:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistics-and-r\"\u003ePH525.1x: Statistics and R for the Life Sciences\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-linear-models-and-matrix-algebra\"\u003ePH525.2x: Introduction to Linear Models and Matrix Algebra\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistical-inference-and-modeling-for-high-throug\"\u003ePH525.3x: Statistical Inference and Modeling for High-throughput Experiments\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/high-dimensional-data-analysis\"\u003ePH525.4x: High-Dimensional Data Analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eGenomics Data Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-bioconductor-annotation-and-analys\"\u003ePH525.5x: Introduction to Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/case-studies-in-functional-genomics\"\u003ePH525.6x: Case Studies in Functional Genomics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/advanced-bioconductor\"\u003ePH525.7x: Advanced Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis class was supported in part by NIH grant R25GM114818.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3b7:T59f,\u003cp\u003eWelcome to Machine learning with Python for finance professionals, provided by ACCA (Association of Chartered Certified Accountants), the global body for professional accountants. This course is part of the \u003ca href=\"https://www.edx.org/professional-certificate/acca-fintech-for-finance-and-business-leaders?source=aw\u0026awc=6798_1622218261_f2da42a5bdfd5fea3402ba5a568d9b86\u0026utm_source=aw\u0026utm_medium=affiliate_partner\u0026utm_content=text-link\u0026utm_term=301045_https%3A%2F%2Fwww.class-central.com%2F\"\u003eFinTech for finance and business leaders professional certificate program\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eThis course will provide a view of what lies under the surface of a machine learning output, help to better interrogate a model, and partner with data scientists and others in an organisation to drive adoption and use of machine learning. Digital finance knowledge and skills are essential components of the technology transformation as business becomes increasingly customer focused. And having the skills to understand how these technologies are deployed and integrated into a customer centric business strategy is essential. With 16 Jupyter Notebooks available, alongside corresponding solution notebooks, and bonus exercises you will quickly become skilled in specific time-saving Machine Learning tools\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan lang=\"EN-US\"\u003eAccess to all end of module quizzes\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"EN-US\"\u003eAccess to the final assessment\u003c/span\u003e\u003c/li\u003e\n\u003c/ul\u003e3b8:T4af,\u003cul\u003e\n\u003cli\u003e\u003cspan lang=\"EN-GB\"\u003eAn introduction to Python starting from initial setup and explaining foundational concepts like data types, variables, mathematical operators, flow control, and functions \u003c/span\u003e\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"EN-GB\"\u003eUsing Python for data analysis including how to load data from different sources, drill down and segment, create pivot table style aggregations and explore data visualisation libraries. \u003c/span\u003e\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"EN-GB\"\u003eAutomating Excel workflows using Python to write macros that can be run at the click of a button using the full power of the Python ec"])</script><script>self.__next_f.push([1,"o-system; and to create template reports that update live with the latest data. \u003c/span\u003e\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"EN-GB\"\u003eHow to better interrogate a model, and partner with data scientists and others in an organisation to drive adoption and use of Machine Learning \u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eUnderstand \u003cspan lang=\"EN-GB\"\u003ethe basic workings of a machine learning model and its relationship to data science, Big Data and Artificial Intelligence. \u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eApply to real-world machine learning examples to meet practical objectives such as evaluating and improving the model, and error detection/correction.\u003c/li\u003e\n\u003c/ul\u003e3b9:T422,\u003cp\u003eIn this course, you will learn how to organize your data within the Microsoft Office Excel software tool. Once organized, we will discuss data cleaning. You will learn how to identify outliers and anomalies in the data, and how to identify and change data-types. Together we will develop a data analysis plan, after which we will apply analysis methods and tools, including exploratory analysis, evaluation of results, and comparison with other findings.\u003c/p\u003e\n\u003cp\u003eIn this robust Excel course, you will gain a solid foundation in using advanced Excel functions such as pivot tables and vlookup to organize and analyze data sets. You will be able to create an Excel chart in a variety of chart types including scatter plot, pie charts, and more. We’ll discuss various techniques such as descriptive statistics, and review the variety of Excel add-ins available to use this powerful tool to organize, analyze, and transform your data into actionable insights. All course activities are designed and demonstrated using Windows OS and Microsoft Excel 2016.\u003c/p\u003e3ba:T43b,\u003cp\u003eUse Tableau to explore data and discover insights to innovate data-driven decision-making. \u003c/p\u003e\n\u003cp\u003eEmployer demand for Tableau skills will grow 35% over the next 10 years. Whether you are in a data-centric role or just need to add data skills to take your job or career to the next level, this course will provide the foundation and skills to get started using "])</script><script>self.__next_f.push([1,"one of today’s most impactful data visualization tools. \u003c/p\u003e\n\u003cp\u003eYou will learn the basics of data representation and data visualization, and then explore the various elements of graphical representation. You will practice creating Tableau representations using your data sources to visually communicate insights. \u003c/p\u003e\n\u003cp\u003eTogether we will discuss examples and cases of visual representations, assessing accuracy and identifying any misrepresentations, and ultimately evaluate decisions and solutions based on data visualizations. \u003c/p\u003e\n\u003cp\u003eYou will gain foundational skills for using tableau desktop’s drag and drop functions. You will learn how to create commonly used data representations to help visualize data.\u003c/p\u003e3bb:Tab6,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIn this course you’ll learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values and exploratory data analysis. We then introduce statistical modeling and how it is applied to high-throughput data. In particular, we will discuss parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation. We provide several examples of how these concepts are applied in next generation sequencing and microarray data. Finally, we will discuss hierarchical models and empirical bayes along with some examples of how these are used in practice. We provide R programming examples in a way that will help make the connection between concepts and implementation.\u003c/p\u003e\n\u003cp\u003eGiven the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.\u003c/p\u003e\n\u003cp\u003eThese courses make up two Professional Certificates and are self-paced:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis for Life Sciences:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistics-and-r\"\u003ePH525.1x: Statistics and R for the Life Sciences\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-linear-models-and-matrix-algebra\"\u003ePH525.2x: Introduction to Linear Models and Matrix Algebra\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistical-inference-and-modeling-for-high-throug\"\u003ePH525.3x: Statistical Inference and Modeling for High-throughput Experiments\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/high-dimensional-data-analysis\"\u003ePH525.4x: High-Dimensional Data Analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eGenomics Data Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-bioconductor-annotation-and-analys\"\u003ePH525.5x: Introduction to Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/case-studies-in-functional-genomics\"\u003ePH525.6x: Case Studies in Functional Genomics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/advanced-bioconductor\"\u003ePH525.7x: Advanced Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis class was supported in part by NIH grant R25GM114818.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3bc:T762,\u003cp\u003eHow can we eradicate malaria, a disease that has caused more deaths than the two world wars combined? Can modern data science help us in eliminating this mosquito-borne disease? How do the biology, ecology, and epidemiology of the malaria-causing parasites \u003cem\u003ePlasmodium falciparum\u003c/em\u003e and \u003cem\u003ePlasmodium vivax\u003c/em\u003e differ? Explore the scientific and technological underpinnings of malaria, as well as the historical, political, social, and economic contexts in which control, elimination, and eradication efforts unfold.\u003c/p\u003e\n\u003cp\u003eThrough foundational lectures, case studies, SimState scenarios, and interviews with experts, MalariaX provides learners with a toolbox of knowledge and analytical skills to understand one of the deadliest diseases ever known. By exploring multidisciplinary aspects of malaria, this course demonstrates how understanding social and economic factors is crucial to developing a successful integrated approach to national and local malaria eradication efforts. Learners will be guided through the analysis of real-world data and its effective use in the development of context-specific interventions to achieve sustainable and equitable impact against malaria.\u003c/p\u003e\n\u003cp\u003eMalariaX contains new findings, examines recent innovations in prevention and treatment, and explores the knowledge gap for tackling malaria. This course also explores how data is used to model malaria transmission dynamics, evaluate the different strategies used to eliminate this disease, and inform decision-making to achieve a malaria-free world.\u003c/p\u003e\n\u003cp\u003eThe self-paced nature of the course allows learners to access essential malaria knowledge on their own schedule. Learners with an interest in gaining new technical knowledge, global health expertise, and decision-making skills, including those already working in the field of global health and malaria are suited for this course.\u003c/p\u003e3bd:T817,"])</script><script>self.__next_f.push([1,"\u003cp\u003eDario, a worker in an NGO in Colombia and a participant in the course, points out that he applied the knowledge from the course to create DATASIMUS, a portal for urban mobility data that compares over 400 variables from transportation systems in the region. Discover more testimonials about the impact of this course below.\u003c/p\u003e\n\u003cp\u003eThanks to this course, through videos and real-life cases, you will strengthen your abilities to use, understand, and interpret data. In this way, you will learn to define research problems, identify methodologies for data collection and analysis, interpret graphs, and acquire the foundations to support the decision making process in public management with verifiable data.\u003c/p\u003e\n\u003cp\u003eThis course is \"self-paced\" so you can enroll at any time, even if the course has been open for a while.\u003c/p\u003e\n\u003cp\u003eIf you choose the \u003cstrong\u003eAudit Track\u003c/strong\u003e , you can complete the course for free, but you won't have access to graded activities, and you won't be able to obtain a certificate upon course completion.\u003c/p\u003e\n\u003cp\u003eIf you opt for the \u003cstrong\u003eVerified Track\u003c/strong\u003e , you can access the course unlimitedly and complete the graded assessments until the closing date by making a payment of USD 25. By doing so, if you pass the course, in addition to the verified certificate, you will receive a \u003cem\u003e\u003cstrong\u003e\u003ca href=\"https://credencialesbid.openbadgepassport.org/app/badge/info/35511\" rel=\"noopener\" target=\"_blank\"\u003edigital badge\u003c/a\u003e\u003c/strong\u003e\u003c/em\u003e that allows you to transform how you share your academic and professional achievements, for example, on social media.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDo you know about edX financial aid to apply for the verified certificate and digital badge?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEdX offers financial assistance to students who have difficulties in making the payment. Enroll in the course and fill out this \u003ca href=\"https://courses.edx.org/financial-assistance/apply/\" rel=\"noopener\" target=\"_blank\"\u003e\u003cem\u003e\u003cstrong\u003efinancial aid application\u003c/strong\u003e\u003c/em\u003e\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003eSee more information in the Frequently Asked Questions section below.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3be:T4a8,\u003cp\u003eIn this course on AI technologies for digital marketing, learners will explore the fundamental principles and applications of predictive analytics, real-time optimization, natural language processing (NLP), and neural networks. Learners will understand the technologies that allow them to make informed decisions, optimize marketing campaigns dynamically, personalize customer interactions, and perform segmentation in digital marketing.\u003c/p\u003e\n\u003cp\u003eFrom market research to copywriting, email personalization, and social media scheduling, learners will explore cutting-edge methods powered by AI. The course emphasizes strategic selection and integration of AI tools to maximize impact in digital marketing. By this course's end, participants will be equipped to implement innovative AI-driven solutions across marketing functions, driving superior customer engagement and conversion rates.\u003c/p\u003e\n\u003cp\u003eWhether you're a seasoned marketer or new to the field, this course provides valuable insights and practical techniques to succeed in today's competitive landscape. Join us and unlock the full potential of AI for driving impactful marketing campaigns and achieving superior business outcomes.\u003c/p\u003e3bf:T8c0,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cstrong\u003eAbout the Database Series of Courses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\"Databases\" was one of Stanford's three inaugural massive open online courses in the fall of 2011. It has been offered in synchronous and then in self-paced versions on a variety of platforms continuously since 2011. The material is now being offered as a set of five self-paced courses, which can be taken in a variety of ways to learn about different aspects of databases. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRelational Databases and SQL\u003c/em\u003e is the most popular course in the Databases series. It is applicable to learners seeking to gain a strong understanding of relational databases, and to master SQL, the long-accepted standard query language for relational database systems. Additional courses focus on advanced concepts in relational databases and SQL, formal foundations and database design methodologies, and semistructured data.\u003c/p\u003e\n\u003cp\u003eAll of the courses are based around video lectures and demos. Many of them include quizzes between video segments to check understanding, in-depth standalone quizzes, and/or a variety of automatically-checked interactive exercises. Each course also includes an unmoderated discussion forum and pointers to readings and resources. The courses are described briefly below. Taught by Professor Jennifer Widom, the overall curriculum draws from Stanford's popular longstanding Databases course.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy Learn About Databases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDatabases are incredibly prevalent -- they underlie technology used by most people every day if not every hour. Databases reside behind a huge number of websites; they're a crucial component of telecommunications systems, banking systems, video games, and just about any other software system or electronic device that maintains some amount of persistent information. In addition to persistence, database systems provide a number of other properties that make them exceptionally useful and convenient: reliability, efficiency, scalability, concurrency control, data abstractions, and high-level query languages. Databases are so ubiquitous and important that computer science graduates frequently cite their database class as the one most useful to them in their industry or graduate-school careers.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3c0:T4a1,\u003cp\u003eThis course is one of five self-paced courses on the topic of Databases, originating as one of Stanford's three inaugural massive open online courses released in the fall of 2011. The original \"Databases\" courses are now all available on edx.org.\u003c/p\u003e\n\u003cp\u003ePart of the Databases series, this is a standalone course; learners seeking to develop an understanding of topics in this course do not need to take other Databases courses. This course covers the JSON and XML standards for semistructured data, along with query languages and schema declaration features for XML.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe XML Data section of this course introduces the XML model for semistructured and self-describing data, including DTDs and some features of XML Schema.\u003c/li\u003e\n\u003cli\u003eThe JSON Data section of this course introduces the JSON model for human-readable structured or semistructured data.\u003c/li\u003e\n\u003cli\u003eThe XPath and XQuery section of this course covers the XPath language for processing XML data, along with many features of the more advanced XQuery language.\u003c/li\u003e\n\u003cli\u003eThe XSLT section of this course provides a general introduction to the XSLT rule-based language for querying and transforming XML data.\u003c/li\u003e\n\u003c/ul\u003e3c1:T805,"])</script><script>self.__next_f.push([1,"\u003cp\u003eStanford's online offering in Databases is now available as a set of five self-paced courses:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDatabases: Relational Databases and SQL\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIntroduction to the relational model and concepts in relational databases and relational database management systems\u003c/li\u003e\n\u003cli\u003eComprehensive coverage of SQL, the long-accepted standard query language for relational database management systems\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDatabases: Advanced Topics in SQL (prerequisite: Relational Databases and SQL)\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreating indexes for increased query performance\u003c/li\u003e\n\u003cli\u003eUsing transactions for concurrency control and failure recovery\u003c/li\u003e\n\u003cli\u003eDatabase constraints: key, referential integrity, and \"check\" constraints\u003c/li\u003e\n\u003cli\u003eDatabase triggers\u003c/li\u003e\n\u003cli\u003eHow views are created, used, and updated in relational databases\u003c/li\u003e\n\u003cli\u003eAuthorization in relational databases\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDatabases: OLAP and Recursion\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eStar schemas, the data cube concept, and On-Line Analytical Processing (OLAP) features in relational databases including the Cube and Rollup operators\u003c/li\u003e\n\u003cli\u003eThe SQL standard for queries over recursively-defined relations\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDatabases: Modeling and Theory\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRelational algebra – the algebraic query language that provides the formal foundations of SQL\u003c/li\u003e\n\u003cli\u003eDependency theory and normal forms in relational databases as the basis of schema design\u003c/li\u003e\n\u003cli\u003eThe data-modeling component of the Unified Modeling Language (UML), how UML diagrams are translated to relations\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDatabases: Semistructured Data\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe XML model for semistructured and self-describing data, including DTDs and some features of XML Schema\u003c/li\u003e\n\u003cli\u003eThe JSON model for human-readable structured or semistructured data\u003c/li\u003e\n\u003cli\u003eThe XPath language for processing XML data, and many features of the more advanced XQuery language\u003c/li\u003e\n\u003cli\u003eAn introduction to the XSLT rule-based language for querying and transforming XML data\u003c/li\u003e\n\u003c/ul\u003e"])</script><script>self.__next_f.push([1,"3c2:T5eb,\u003cp\u003eThe R programming language is purpose-built for data analysis. R is the key that opens the door between the problems you want to solve with data and the answers you need to meet your objectives. This course starts with a question, and then walks you through the process of answering it through data. You will first learn important techniques for preparing (or wrangling) your data for analysis. Then you will learn how to gain a better understanding of your data through exploratory data analysis, helping you to summarize your data and identify relevant relationships between variables that can lead to insights. ****\u003c/p\u003e\n\u003cp\u003eOnce your data is ready to analyze, you will learn how to develop your model, evaluate it and tune its performance. By following this process, you can be sure that your data analysis performs to the standards that you have set, so that you can have confidence in the results. ****\u003c/p\u003e\n\u003cp\u003eBy playing the role of a data analyst who is analyzing airline departure and arrival data to predict flight delays, you will build hands-on experience delivering insights using data. Using an Airline Reporting Carrier On-Time Performance Dataset, you will practice reading data files, preprocessing data, creating models, improving models, and evaluating them to ultimately choose the best one to use. \u003c/p\u003e\n\u003cp\u003eNote: The prerequisite for this course is basic R programming skills. For example, ensure that you have completed a course like Introduction to R Programming for Data Science from IBM.\u003c/p\u003e3c3:T580,\u003cp\u003eBig data is transforming the health care industry relative to improving quality of care and reducing costs--key objectives for most organizations. Employers are desperately searching for professionals who have the ability to extract, analyze, and interpret data from patient health records, insurance claims, financial records, and more to tell a compelling and actionable story using health care data analytics. \u003c/p\u003e\n\u003cp\u003eThe course begins with a study of key components of the U.S. health care system as they rel"])</script><script>self.__next_f.push([1,"ate to data and analytics. While we will be looking through a U.S. lens, the topics will be familiar to global learners, who will be invited to compare/contrast with their country's system. \u003c/p\u003e\n\u003cp\u003eWith that essential industry context, we'll explore the role of health informatics and health information technology in evidence-based medicine, population health, clinical process improvement, and consumer health. \u003c/p\u003e\n\u003cp\u003eUsing that as a foundation, we'll outline the components of a successful data analytics program in health care, establishing a \"virtuous cycle\" of data quality and standardization required for clinical improvement and innovation. \u003c/p\u003e\n\u003cp\u003eThe course culminates in a study of how visualizations harness data to tell a powerful, actionable story. We'll build an awareness of visualization tools and their features, as well as gain familiarity with various analytic tools.\u003c/p\u003e3c4:T9f2,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIn our daily lives, many of us ask questions of ourselves and about others. Some of these questions, born of simple curiosity, resemble elements of qualitative research. For instance, what will I wear today—and what motives, both overt and hidden to the world, will guide those choices? Or, why does my neighbor seem particularly downtrodden today when he’s normally happy? Or, for what reasons is my favorite charitable organization having difficulty recruiting new members this year, especially when the factors surrounding recruitment haven’t changed? These, and other questions, may arise from our observations and our experiences of the world in which we live. As you will learn in this course, these questions become qualitative research projects when the right methods and analytical processes are learned and applied.\u003c/p\u003e\n\u003cp\u003eThrough qualitative research, we are able to explore—through methods like interviews and observations—various facets of individuals’ lived experiences. Qualitative research in psychology arises from historical and cross-disciplinary foundations, as well as from dynamic philosophical underpinnings. We will focus on the past and present of qualitative inquiry, with grounding in contemporary examples across a variety of psychology fields. Through tools, illustrations, and self-assessments, you will learn some of the basics of qualitative research while examining your own research predilections and experiences. We hope you will emerge with an understanding of, and appreciation for, how qualitative inquiry adds depth, character, and nuance to our understanding of humans’ individual and collective experiences.\u003c/p\u003e\n\u003cp\u003eQualitative research usually involves naturalistic inquiry. This means that we explore human experiences of the real world as they’re living it, or as it was lived. Qualitative inquiry invites inquiry into everyday living experiences. Through an array of qualitative traditions and their associated methods, we can investigate beliefs, biases, behaviors, routines, roles, cultures, and other facets of the human experience. Here, you will learn about the foundations of qualitative inquiry—and how it differs from quantitative research, yet in complementary ways. Relatedly, you will gain practice in common qualitative traditions—like phenomenology and ethnography—and some of the methods they use, such as interviewing. We hope you will gain the competence, confidence, and “know-how” to begin planning your own qualitative study of a topic in psychology.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3c5:T623,\u003cp\u003eDecisions made by humans are rarely made by data alone. Human decision-makers have cognitive biases, are affected by emotions, and make conceptual leaps beyond what the data may suggest. The best way to educate and persuade decision-makers is through stories. Stories built by good data analysis.\u003c/p\u003e\n\u003cp\u003eIn this course, you will learn how to create stories based on data - data storytelling - by using the ABT (“And, But, Then”) Narrative Framework. We will start by using powerful data science techniques to explore and refine your data. We shape a compelling narrative out of the data findings using brain-friendly storytelling techniques.\u003c/p\u003e\n\u003cp\u003eOnce you have your stories, you will learn to create insightful data visualizations and build a persuasive presentation for business professionals - both in-person or online.\u003c/p\u003e\n\u003cp\u003eThe final week of the course deals with Customer Training. Customers want the latest and greatest digital products full of features. But customers don't want to spend too much time learning those features. Customer training helps the digital organization be more strategic by assisting the customer in engaging more with the product. \u003c/p\u003e\n\u003cp\u003eUsing data storytelling skills, you will craft stories to help customers understand how your organization’s products and services will make their lives better. You will learn how to use the data analytics and machine learning in your digital organization to create effective customer training, so your products and services get the traction you need for digital transformation success.\u003c/p\u003e3c6:T86d,"])</script><script>self.__next_f.push([1,"\u003cp\u003eExplore one of the most dynamic fields with one of the best mining courses available online. Designed for learners from top universities, professionals in the mining industry, and individuals looking to upskill, this course covers the essentials of mining engineering, from exploration to innovation, in a flexible and accessible format. This mining engineering program is suitable for those interested in natural resources, sustainability, and related fields such as civil engineering and environmental engineering.\u003c/p\u003e\n\u003cp\u003eWelcome to Mining Engineering!\u003c/p\u003e\n\u003cp\u003eOur online course, \"Mining Engineering Principles: Exploration to Innovation,\" is a comprehensive exploration of the fundamental concepts and principles that underpin this dynamic field. Whether you’re a student preparing for a bachelor’s degree in mining engineering or a professional seeking professional development, this course equips you with essential skills and insights.\u003c/p\u003e\n\u003cp\u003eWith no prerequisites required, learners will embark on a journey to understand the need for sustainable practices in the mining sector while exploring the impact of mining on the value chain and broader sustainability goals. The program includes electives and hands-on experience to enrich the learning process.\u003c/p\u003e\n\u003cp\u003eCourse Goals\u003c/p\u003e\n\u003cp\u003eThis course aims to provide learners with a strong foundation in mining engineering, covering topics crucial to the mining business and related sectors such as electrical engineering, robotics, and data analytics. The course leverages instructor videos, expert interviews, interactive modules, and case studies to enhance engagement.\u003c/p\u003e\n\u003cp\u003eTopics include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhy mining is essential and its role in achieving sustainable development goals.\u003c/li\u003e\n\u003cli\u003eContributions of the mining industry to the U.S. economy.\u003c/li\u003e\n\u003cli\u003eThe fundamentals of mineral resource identification and evaluation.\u003c/li\u003e\n\u003cli\u003eExploration of modern mining technologies like IoT, augmented reality, and data science.\u003c/li\u003e\n\u003cli\u003eSustainability and health and safety practices in mining operations.\u003c/li\u003e\n\u003cli\u003eKey engineering problems and innovative solutions in the mining sector.\u003c/li\u003e\n\u003c/ul\u003e"])</script><script>self.__next_f.push([1,"3c7:T8c0,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cstrong\u003eAbout the Database Series of Courses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\"Databases\" was one of Stanford's three inaugural massive open online courses in the fall of 2011. It has been offered in synchronous and then in self-paced versions on a variety of platforms continuously since 2011. The material is now being offered as a set of five self-paced courses, which can be taken in a variety of ways to learn about different aspects of databases. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRelational Databases and SQL\u003c/em\u003e is the most popular course in the Databases series. It is applicable to learners seeking to gain a strong understanding of relational databases, and to master SQL, the long-accepted standard query language for relational database systems. Additional courses focus on advanced concepts in relational databases and SQL, formal foundations and database design methodologies, and semistructured data.\u003c/p\u003e\n\u003cp\u003eAll of the courses are based around video lectures and demos. Many of them include quizzes between video segments to check understanding, in-depth standalone quizzes, and/or a variety of automatically-checked interactive exercises. Each course also includes an unmoderated discussion forum and pointers to readings and resources. The courses are described briefly below. Taught by Professor Jennifer Widom, the overall curriculum draws from Stanford's popular longstanding Databases course.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhy Learn About Databases\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDatabases are incredibly prevalent -- they underlie technology used by most people every day if not every hour. Databases reside behind a huge number of websites; they're a crucial component of telecommunications systems, banking systems, video games, and just about any other software system or electronic device that maintains some amount of persistent information. 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Process mining can be applied in logistics, finance, production, sales, and healthcare. This hands-on course explains the key concepts and techniques in process mining. You will learn about automated process discovery, conformance checking, performance analysis, and applications of machine learning to event data. The theoretical concepts learned are tool and "])</script><script>self.__next_f.push([1,"application-independent. However, to be able to apply these concepts, the course provides several data sets and access to the Celonis process mining software. After taking this compact course, participants understand current trends in process management and automation, know the key process discovery and conformance checking algorithms, and can apply these to real-life data sets using the Celonis software. 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This practical experience will equip you with the skills to adapt analysis to your unique datasets and specific farming applications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUpgrade to the verified track\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLooking to further develop your practical skills and get feedback on your data processing choices? Then upgrading to the Verified Track may be just the right choice for you! You will gain access to materials on future sensing approaches and graded assignments evaluating your data processing choices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFor whom?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAre you an avid agricultural enthusiast or a professional looking to enhance your expertise? Then this course provides you a gateway to tap into the opportunities of UAV-based sensing. Enroll today and position yourself at the forefront of the future of farming!\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3cc:T466,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eIn this course, one will explore the foundational concepts of machine learning in banking, dive into data analysis techniques tailored for financial data, and learn to apply supervised and unsupervised learning methods to real-world banking and finance challenges. Discover how Natural Language Processing (NLP) is changing the way banks interact with customers and gain essential skills in time series analysis and forecasting for financial markets.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe course also covers model evaluation, interpretability, and ethical considerations in AI, ensuring you're well-equipped to navigate the unique challenges of the banking industry. 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Enroll now to unlock the potential of machine learning and become a data-driven decision-maker in the world of finance.\u003c/p\u003e3cd:T6df,\u003cp\u003eThis self-paced social sciences course is about trying to understand ‘methods’ and the ways researchers go about trying to find things out about people, societies and cultures. \u003c/p\u003e\n\u003cp\u003eIn this course, you will explore: \u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe kinds of methods researchers tend to adopt\u003c/li\u003e\n\u003cli\u003eThe contexts in which certain research methods are used\u003c/li\u003e\n\u003cli\u003eThe benefits, drawbacks and ethical implications of research\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis course isn’t a practical or technical guide to doing research. 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The development of a rigorous business plan is used to help you make that assessment.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eParticipants with prior experience in the medical field will learn how novel data science applications can improve healthcare, create societal value and how to spot entrepreneurial opportunities.\u003c/p\u003e\n\u003cp\u003eParticipants with experience in data science or mathematics will learn about medical approaches to data and why healthcare is an exciting area to apply and develop data analytics.\u003c/p\u003e\n\u003cp\u003eParticipants interested in launching their startup will learn how big data solutions in health care can provide a solid basis to build great ventures.\u003c/p\u003e\n\u003cp\u003eWhatever your motivation to enrol in this course, we care about your project and your success - that’s why we will guide you through all parts of this learning journey step by step!\u003c/p\u003e\n\u003cp\u003eEnter now to see how you can engage in data driven innovation and make an impact on improving care, outcomes and the quality of life.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3cf:T7e9,\u003cp\u003eIn this hands-on course, you will learn how to use R Shiny to create data-driven web applications. By the end of the course, you will have created an interactive web application that highlights the biodiversity of America's National Parks. Your application will feature an interactive map, biodiversity calculator, trail journal and species images. Using R Shiny, you will expand your data analysis and visualization skills while developing a way to share and distribute your findings in an application. If you are a beginner level data professional, a student, a researcher, an academic marketing analyst, business and data analyst, or financial analyst, this course is for you.\u003c/p\u003e\n\u003cp\u003eThis four week course will give you a foundation for making and deploying Shiny applications. 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Well-respected AP instructors from around the USA will lead you through video, assessment questions, and interactive activities.\u003c/p\u003e\n\u003cp\u003eEach module breaks these tricky topics into bite-sized pieces - with short instructional videos, on-screen simulations, interactive graphs, and practice problems written by many of the same people who write and grade your AP® Physics 1 \u0026amp; 2 exams.\u003c/p\u003e\n\u003cp\u003eTopics include:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eAcceleration\u003c/li\u003e\n\u003cli\u003eForce Diagrams\u003c/li\u003e\n\u003cli\u003eFree Fall and Projectile Motion\u003c/li\u003e\n\u003cli\u003eMomentum\u003c/li\u003e\n\u003cli\u003eRotational Motion\u003c/li\u003e\n\u003cli\u003eAngular Momentum\u003c/li\u003e\n\u003cli\u003eStanding Waves\u003c/li\u003e\n\u003cli\u003eConservation of Charge \u0026amp; Energy in Circuits\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cem\u003e* Advanced Placement® and AP® are trademarks registered and/or owned by the College Board, which was not involved in the production of, and does not endorse, these offerings. Stand-alone units cover the most challenging concepts in the newly redesigned AP® Physics 1 curricula (based on College Board data from 2011–2013 AP® Physics B exams).\u003c/em\u003e\u003c/p\u003e3d1:T478,\u003cp\u003eThis course covers 9 challenging topics in AP® Physics 2. Well-respected AP instructors from around the USA will lead you through video, assessment questions, and interactive activities.\u003c/p\u003e\n\u003cp\u003eEach module breaks these tricky topics into bite-sized pieces—with short instructional videos, on-screen simulations, interactive graphs, and practice problems written by many of the same people who write and grade your AP® Physics 2 exam.\u003c/p\u003e\n\u003cp\u003eTopics include:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eElectrostatic Fields\u003c/li\u003e\n\u003cli\u003eGravitational and Electric Potentials\u003c/li\u003e\n\u003cli\u003eElectromagnetic Induction\u003c/li\u003e\n\u003cli\u003eCapacitance\u003c/li\u003e\n\u003cli\u003eThermodynamics\u003c/li\u003e\n\u003cli\u003ePressure, Force \u0026amp; Flow in Fluids\u003c/li\u003e\n\u003cli\u003eMirrors \u0026amp; Lenses\u003c/li\u003e\n\u003cli\u003eDiffraction \u0026amp; Interference\u003c/li\u003e\n\u003cli\u003eAtomic Transitions\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cem\u003e* Advanced Placement® and AP® are trademarks registered and/or owned by the College Board, which was not involved in the production of, and does not endor"])</script><script>self.__next_f.push([1,"se, these offerings. Stand-alone units cover the most challenging concepts in the newly redesigned AP® Physics 2 curricula (based on College Board data from 2011–2013 AP® Physics B exams).\u003c/em\u003e\u003c/p\u003e3d2:T635,\u003cp\u003e本课程完整覆盖数据挖掘领域的各项核心技术，包括数据预处理、分类、聚类、回归、关联、推荐、集成学习、进化计算等。强调在知识的广度、深度和趣味性之间寻找最佳平衡点，在生动幽默中讲述数据挖掘的核心思想、关键技术以及一些在其它相关课程和教科书中少有涉及的重要知识点。本课程适合对大数据和数据科学感兴趣的各专业学生以及工程技术人员学习，不追求纯粹的理论推导，而是把理论与实践有机结合，让学生学到活的知识、有用的知识和真正属于自己的知识，特别是数据分析领域的研究方法和思维方式。 \u003c/p\u003e\n\u003cp\u003eDespite the large volume of data mining papers and tutorials available on the web, aspiring data scientists find it surprisingly difficult to locate an overview that blends clarity, technical depth and breadth with enough amusement to make big data analytics engaging. This course does just that.\u003c/p\u003e\n\u003cp\u003eEach module starts with an interesting real-world example that gives rise to the specific research question of interest.\u003c/p\u003e\n\u003cp\u003eStudents are then presented with a general idea of how to tackle this problem along with some intuitive and straightforward approaches.\u003c/p\u003e\n\u003cp\u003eFinally, a number of representative algorithms are introduced along with concrete examples that show how they function in practice.\u003c/p\u003e\n\u003cp\u003eWhile theoretical analysis sometimes overcomplicates things for students, here it’s applied to help them better understand the key features of the techniques.\u003c/p\u003e3d3:T721,\u003cp\u003eMachine Learning is the basis for the most exciting careers in data analysis today. 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In this framework, we possess an output or response that we wish to predict based on a set of inputs. We will discuss several fundamental methods for performing this task and algorithms for their optimization. Our approach will be more practically motivated, meaning we will fully develop a mathematical understanding of the respective algorithms, but we will only briefly touch on abstract learning theory.\u003c/p\u003e\n\u003cp\u003eIn the second half of the course we shift to unsupervised learning techniques. In these problems the end goal less clear-cut than predicting an output based on a corresponding input. We will cover three fundamental problems of unsupervised learning: data clustering, matrix factorization, and sequential models for order-dependent data. Some applications of these models include object recommendation and topic modeling.\u003c/p\u003e3d4:T6cc,\u003cp\u003eThe gen AI market is projected to grow at an impressive 46% CAGR through 2030 (Statista). The demand for tech professionals skilled in generative AI engineering is skyrocketing!\r\nThis Generative AI Engineering Professional Certificate gives aspiring generative AI engineers, AI developers, data scientists, machine learning engineers, and AI researchers the critical skills in generative AI, large language models (LLMs), and natural language processi"])</script><script>self.__next_f.push([1,"ng (NLP) that employers are looking for.\u003c/p\u003e\r\n\r\n\u003cp\u003eBy utilizing transformers and LLMs, gen AI engineers create AI systems, applications and agents that generate and process new data—such as images, text, audio, and video.\u003c/p\u003e\r\n\r\n\u003cp\u003eDuring this program, you'll explore AI, generative AI, and prompt engineering, as well as data analysis, machine learning, and deep learning with Python. You'll gain hands-on experience with libraries like SciPy and scikit-learn, and develop applications using frameworks and models like BERT, GPT, and LLaMA. You'll also learn how to build LLM-based NLP applications using tools like Hugging Face Transformers, PyTorch, RAG, and LangChain, while diving deep into tokenization, language models, and transformer methods.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe program offers ample practical experience through hands-on labs and projects you can showcase in interviews. Additionally, you’ll complete a substantial guided project where you’ll build your own real-world generative AI application.\u003c/p\u003e\r\n\r\n\u003cp\u003eIf you're eager to set yourself apart with the highly sought-after generative AI skills that employers are actively looking for, ENROLL TODAY and advance your career in less than 6 months!\r\nPrerequisites: Just basic computer literacy... No prior experience required.\u003c/p\u003e3d5:T8c8,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis Professional Certificate program is intended for anyone who is seeking to develop the job-ready skills, tools, and portfolio for an entry-level data analyst or data scientist position. No prior knowledge of R, or programming is required to get you started!\u003c/p\u003e\r\n\r\n\u003cp\u003eIn this Data Analytics and Visualization with Excel and R Professional Certificate Program, you will dive into the role of a data analyst or data scientist and develop the essential skills you need work with a range of data sources and apply powerful tools, including Excel, Cognos Analytics, and the R programming language (including: ggplot2, Leaflet and R Shiny), towards becoming a data driven practitioner, and gaining a competitive edge in the job market.\u003c/p\u003e\r\n \r\n\u003cp\u003eBy the end of this program, you will be able to explain the data analyst and data scientist roles. Skills you will developer and tools you will be exposed to in this program include:\r\n\u003cul\u003e\r\n\u003cli\u003eExcel spreadsheets to create charts and plots.\u003c/li\u003e\r\n\u003cli\u003eCognos Analytics to create interactive dashboards.\u003c/li\u003e\r\n\u003cli\u003eRelational databases and query data using SQL statements.\u003c/li\u003e \r\n\u003cli\u003eR programming language to complete the entire data analysis process - including data preparation, statistical analysis, data visualization, predictive modeling, and creating interactive data applications.\u003c/li\u003e \r\n\u003cli\u003eVarious methods to communicate your data findings and learn to prepare a report for stakeholders.\r\nThis program is suitable for anyone with a passion for learning and does not require any prior data analysis, statistics, or programming experience.\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eApplied Learning Project\u003c/b\u003e\u003cbr\u003e\r\nThroughout this Professional Certificate, you will also complete hands-on labs and projects to help you gain practical experience with Excel, Cognos Analytics, SQL, and the R programing language and related libraries for data science, including Tidyverse, Tidymodels, R Shiny, ggplot2, Leaflet, and rvest.\u003c/p\u003e\r\n\r\n\u003cp\u003eIn the final course in this Professional Certificate, you will complete a capstone project that applies what you have learned to a challenge that requires data collection, analysis, basic hypothesis testing, visualization, and modelling to be performed on real-world datasets.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3d6:T5c2,\u003cp\u003eData engineering is a growth tech segment, with considerable demand for skilled data engineers. Data engineering makes quality data available for business operations, business intelligence and data-driven decision making.\u003c/p\u003e \r\n\r\n\u003cp\u003eThis six-course Professional Certificate from IBM is an excellent base for those interested in a career in data engineering. Through these data engineering courses, you will learn the core principles and get to practice your new skills with hands-on labs. You will learn about the data engineering ecosystem, data integration pipelines, data repositories, Business Intelligence and Reporting tools. You will understand Data repositories, such as relational and non-relational databases, data warehouses, data marts, data lakes, and big data stores, as well as how to store and process this data.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe certificate starts with an introductory course, then progresses through how Python is used by Data Scientists, in Artificial Intelligence and Development, and gives you the opportunity to create a Python project to put these skills into practice. The certificate then covers relational databases and SQL.\u003c/p\u003e\r\n \r\n\u003cp\u003eThis Professional Certificate does not require any prior programming or data science skills. These online learning tools will provide you with practical skills and experience in collating data from data sources for factual analysis and providing organizations with the basis for data-driven decision making.\u003c/p\u003e3d7:T5a5,\u003cp\u003e Digital marketing is fast becoming the dominant tool in marketers’ toolboxes. While “out of home” marketing and mass communication continue to make-up a large part of a company’s marketing budget, the digital components of these budgets are growing rapidly. This is in part due to the fact that digital marketing presents the opportunity to track the ROI on every dollar spent. As such, it is critical that experts in digital marketing build both sound marketing and creativity skills, as well as significant skills in data analysis.\u003c/p\u003e \r\n\r\n\u003c"])</script><script>self.__next_f.push([1,"p\u003eThis program will prepare you for the future of marketing and arm you with the most critical tools and skills to drive value at your company. You will learn the key marketing skills most in-demand today, including: omni-channel marketing, marketing analytics, social media strategy, online advertising, and AI approaches to customer value analysis.\u003c/p\u003e\r\n\r\n\u003cp\u003eDesigned by world-renowned marketing professors at Maryland Smith, home to some of the strongest marketing researchers and teachers in the world, this Professional Certificate in Digital Marketing from the University of Maryland develops the digital marketing acumen you need to take advantage of the explosive growth and rapidly evolving marketing industry. Today’s marketers face a “digital skills gap,” and when you earn a Professional Certificate in Digital Marketing from Maryland Smith, you will be future ready.\u003c/p\u003e3d8:T5af,\u003cp\u003eData Warehouse Engineers and Business Analysts are in high demand as organizations become increasingly dependent on data to support their operations.\u003c/p\u003e\r\n\r\n\u003cp\u003eData warehousing has transformed the way organizations perform business analysis and make strategic decisions. Massive amounts of data from multiple sources can be easily accessed using SQL and formatted for analysis, reporting and Business Intelligence for organizations to gain deeper business insights.\u003c/p\u003e \r\n \r\n\u003cp\u003eThe Data Warehouse Engineer Professional Certificate provides you the skills and knowledge to design, deploy and manage Enterprise Data Warehouses (EDW) and utilize Business Intelligence tools to analyze and extract insights using reports and dashboards.\u003c/p\u003e \r\n \r\n\u003cp\u003eUpon completing this program, you’ll gain practical experience to work with Relational Database Management Systems (RDBMS), query data using SQL statements, utilize Linux/UNIX shell scripts to automate repetitive tasks, and build data pipelines using Apache Airflow and Kafka to Extract, Transform and Load (ETL) data. You’ll also acquire the skills to build and operationalize Data Warehouses and"])</script><script>self.__next_f.push([1," conduct data analysis.\u003c/p\u003e\r\n \r\n\u003cp\u003eWithin each course you’ll practice your skills with numerous hands-on labs and multiple projects to add to your portfolio for launching your career.\u003c/p\u003e\r\n \r\n\u003cp\u003eTo get started, all you need is basic computer literacy and the desire to learn and practice new skills.\u003c/p\u003e3d9:Tac0,"])</script><script>self.__next_f.push([1,"\u003cp\u003eProduct management is one of the fastest growing and most lucrative jobs available today. Companies have awoken to the desperate need for product managers to create products that customers love, that integrate design, functionality, and business solutions. This is a one of kind product management certification that covers the skill sets for the entire product lifecycle.\u003c/p\u003e\r\n\r\n\u003cp\u003eWe start with the fundamentals of product management and why this role is so coveted as a launch pad for future CEOs and startup founders. By exploring the roles and responsibilities of being a product manager, we’ll identify its relationship to stakeholders external and internal, including customers, engineering, legal, finance, marketing, and customer service.\u003c/p\u003e\r\n\r\n\u003cp\u003eAs a product manager you have to own the results of your product’s success by tackling risks early and setting up your teams for success. This is why we immediately move on to discussing product-market fit, with a detailed examination of target customers, customer’s underserved needs, value propositions, feature sets, and user experiences.\u003c/p\u003e\r\n\r\n\u003cp\u003eDesigning the customer and user experience is essential to creating great products today. Gone is the old paradigm of “form follows function” model of design. The process must be iterative and follow the best product design and development processes. This essential product management course explains key design thinking principles around personas, story mapping, and prototyping. Product managers need to know and appreciate product designer tools and processes. By combining these principles with good scrum processes you’ll learn to create great products that don’t sacrifice design for functionality or feasibility.\u003c/p\u003e \r\n\r\n\u003cp\u003eAgile systems engineering ensures great products truly excel over time in all three areas of design, functionality, and technical feasibility. Agile systems engineering uses tools such as rapid prototyping, open set architectures, and platform design. By using an agile methodology not just for business and design, you'll innovate and collaborate better, manage costly technical debt, and validate your product ideas with data science and innovation metrics.\u003c/p\u003e \r\n\r\n\u003cp\u003eFinally, you cannot lead products without core skills in leadership and influence. Product managers are masters at internal sales, team building, delegation, and empowerment. They also know that great product management careers are not built alone. From envisioning a product strategy to correctly portraying the product roadmap, the process must be inclusive, interactive, and motivating. This course will enable you to align product teams, product designs, and product development for speed and innovation at scale.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3da:T6d6,\u003cp\u003eThe world runs on data more than ever before. Regardless of your profession, data administration, analysis and visualization have become critical skills that are in high demand. Whether you need to make business projections, store inventory, conduct research or gather market intelligence, the ability to make sense of data is increasingly essential.\u003c/p\u003e \r\n\r\n\u003cp\u003eEveryone recognises Microsoft Excel, yet few people use it to its full potential. It’s ubiquity and ability to effectively manage large amounts of data make it an ideal data analysis tool that offers something for everyone. From the fundamentals to advanced use cases, Excel can be your ally when it comes to streamlining tasks and delivering valuable, professional data insights. In this program, even beginners can work their way up to extracting actionable intelligence from raw data, by using Excel’s powerful tools for data manipulation.\u003c/p\u003e \r\n\r\n\u003cp\u003eStarting with the fundamentals, and progressing to advanced data management and analysis, this hands-on Professional Certificate program will equip you with the tools and methodology to master Excel skills. Learn to carry out regular tasks faster than ever before, create models and templates with multiple scenarios, and design professional visualizations and build reports and analysis from raw data extraction.\u003c/p\u003e\r\n \r\n\u003cp\u003eUsing data effectively goes beyond simply understanding a tool’s functionality. Drawing on real-life examples, this program will also help you integrate data analysis into your everyday thinking; to recognise how and when Excel can be used to problem solve and provide valuable insight. Developing this data-centric mindset will give you a truly competitive edge that is valued across industries.\u003c/p\u003e3db:T4e1,\u003cp\u003eLiteracy in business financials, regardless of your industry, is essential for growing your career in management. Whether you are working within an organization or have your own business, the decisions you make affect the health of the business. You must interpret and forecast"])</script><script>self.__next_f.push([1," the impact of your decisions to ensure you are making the best recommendation for the firm. Through the four pillars of financial decision-making: Accounting, Finance, Data Analysis and Economics, you can become a successful entrepreneur or intrapreneur. \u003c/p\u003e\r\n\r\n\u003cp\u003eThis program will cover these four pillars in-depth - with each course devoted to exploring a particular topic and how it can inform the decisions you face in your career. If you’re looking to discover and practice the fundamental skills and methodology used in business today, this Professional Certificate program is for you. Some of the topics you will cover in this program include: funding, valuation, key performance metrics, financial forecasting, data analysis, and models of consumer choice and demand. \u003c/p\u003e\r\n\r\n\u003cp\u003eThis program is best suited for professionals who are looking to grow their career into a managerial role where they will need to make financial and analytical decisions. \u003c/p\u003e3dc:T814,"])</script><script>self.__next_f.push([1,"\u003cp\u003eGain a solid foundation in essential quantitative and analytical skills to succeed as a business professional and prepare yourself to pursue an MBA.\u003c/p\u003e\r\n\r\n\u003cp\u003eIn this Professional Certificate program, you will learn key quantitative and analytical skills needed to succeed as a business professional. In the era of big data, it is vital for business professionals to understand what the numbers say, whether it be analyzing financial reports or presenting data in an effective way.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe subjects covered in this program not only provide foundational business skills but also prepare you to further your business studies and pursue an MBA, where you can build upon these skills and gain theoretical and practical training at a graduate-level to better understand general business management functions.\u003c/p\u003e \r\n\r\n\u003cp\u003eEach course offers a personalised learning plan that highlights the areas you need to focus on to give you a competitive edge within your company or in an MBA program.\u003c/p\u003e\r\n\r\n\u003cp\u003eGuided by experts in the field, you will use real world examples, interactive exercises and the support of your peers to learn the fundamentals of: \r\n\u003cul\u003e\u003cli\u003eAccounting\u003c/li\u003e\r\n\u003cli\u003eFinance\u003c/li\u003e\r\n\u003cli\u003eMaths\u003c/li\u003e\r\n\u003cli\u003eData Analysis\u003c/li\u003e\u003c/ul\u003e\r\n\u003c/p\u003e\r\n\r\n\u003cp\u003eThis Professional Certificate program is targeted at:\r\n\u003cul\u003e\u003cli\u003eProfessionals preparing to or already working in a business environment who want to enhance their quantitative business skills. \u003c/li\u003e\r\n\u003cli\u003eLearners who are ready to embark on or considering an MBA program and want to identify and remove any gaps in their quantitative and analytical skills to ensure they are prepared to succeed in the program. \u003c/li\u003e\u003c/ul\u003e\r\n\u003c/p\u003e\r\n\r\n\u003cp\u003eThe “PreMBA Essentials for Professionals” Professional Certificate program will prepare you for success in an MBA at an institution such as Imperial Business School by helping you gain the fundamental skills you need to advance your studies. Imperial College Business School offers a world-class Global Online MBA program and we invite you to visit their website to learn more. \u003c/p\u003e"])</script><script>self.__next_f.push([1,"3dd:Tb17,"])</script><script>self.__next_f.push([1,"\u003cp\u003eLearn foundations of GIS concepts and technology to view, understand, query, visualize, and interpret spatial data to reveal patterns and relationships for problem-solving and better decision making.\u003c/p\u003e\r\n\r\n\u003cp\u003e“GIS is about uncovering meaning and insights from within data.” Jack Dangermond, CEO of Esri, the creators of ArcGIS.\u003c/p\u003e\r\n\r\n\u003cp\u003eGeographic Information Systems (GIS) was once an esoteric technology limited to geographers and data scientists. It is now available to everyone. Thousands of organizations in virtually every field are using GIS to analyze data, make maps that reveal patterns and relationships, share insights, and solve complex problems of local to global significance.\u003c/p\u003e \r\n\r\n\u003cp\u003eThere is a growing need for a workforce skilled in GIS. This professional GIS certificate program will provide a comprehensive introduction to state-of-the-art methods and modern applications of GIS with the goal to prepare students for entry-level GIS jobs and undergraduate programs in geospatial sciences. Using the industry leading ArcGIS Pro GIS software, students will work with GIS datasets to support better decision making across many applications through data analysis of geographic data.\u003c/p\u003e\r\n\r\n\u003cp\u003eGIS is a growing technology field with widespread applications in government agencies and private industries. The global GIS market size stood at $7.5 billion in 2019, and it is expected to reach $25.6 billion by 2030 (\u003ca href=\"https://www.psmarketresearch.com/press-release/global-geographic-information-system-market\"\u003eSource\u003c/a\u003e).\u003c/p\u003e\r\n\r\n\u003cp\u003eThe rising investments in GIS-based infrastructure and solutions by the government sector, for military and aerospace applications, and by the private sector for real-time decision-making in industries such as construction, utilities, mining, and healthcare are the key factors driving the growth in the GIS applications.\u003c/p\u003e\r\n\r\n\u003cp\u003e\"The skills I have acquired from this program give me a boost in performing my tasks as a GIS Analyst. The courses are well structured. The Professional Certificate I have obtained became the reason for me to be invited as resource speaker in a local professional assembly and GIS workshops.\"\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ci\u003eArnold T. Tanondong, M.Sc.\u003c/br\u003e\r\nJ.H. Cerilles State College, Philippines\u003c/i\u003e\r\n\r\n\u003cp\u003e\"The ability to represent any kind of data on a map is very powerful. The material and the exercises in this program are about real world situations. This allows learners to create meaningful presentations of data to inform decisions that can allow a community, an organization, or even a country, to move forward.\"\u003c/p\u003e\r\n\r\n\u003ci\u003eYiorgos Theodoropoulos, GIS Essentials Learner\u003c/i\u003e\r\n\r\n\u003cp\u003e\u003cimg src=\"https://images.ctfassets.net/ii9ehdcj88bc/1N5N0fUMfskqh69SaOISWE/c45273d07183dd9b4a28010d171bc154/edxprize-2023-finalist.png\" alt=\"edX Prize Finalist 2023\"\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3de:Tbe2,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis comprehensive MicroBachelors program in Statistics Fundamentals introduces students to the essential statistical concepts, methods and techniques which they can use to grow their skills in quantitative careers, or as a step towards further study at undergraduate level or in specialised subjects.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u2028Spanning four individual courses, all of which are self-paced and asynchronous, this programme provides students with maximum flexibility to learn with a world-leading institution from anywhere in the world in a way that fits their schedule. Students will first be introduced to core statistical concepts and gain essential skills to analyse, summarise, and present data, before progressing to probability, distribution theory and statistical inference.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u2028\u2028These courses are based on service-level statistics courses offered as part of the University of London degree programmes in Economics, Management, Finance and the Social Sciences (EMFSS), with academic direction from the London School of Economics and Political Science (LSE). They equip students with the fundamental statistical knowledge and tools to set them up for success in second and third-year courses in subjects such as economics, finance, data science, mathematics, statistics, business analytics and programming.\u003c/p\u003e \r\n\r\n\u003cp\u003e\u2028Those that complete this MicroBachelors program may wish to go on to apply to the University of London's academically rigorous EMFSS degree programmes that give learners the opportunity to earn a BSc from a top London university wherever they are in the world.\u003c/p\u003e \r\n\r\n\u003cp\u003e\u2028Should you wish, you may elect to just study some of the individual courses within the MicroBachelors program, perhaps to build or refresh quantitative skills for career advancement.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u2028No prior statistics knowledge is required for this programme.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eStatistics 1a: Introductory statistics, probability and estimation\u003c/b\u003e\r\n\u003cul\u003e\r\n\u003cli\u003eMathematical revision and the nature of statistics\u003c/li\u003e\r\n\u003cli\u003eData visualisation and descriptive statistics\u003c/li\u003e \r\n\u003cli\u003eProbability theory\u003c/li\u003e \r\n\u003cli\u003eThe normal distribution and ideas of sampling\u003c/li\u003e \r\n\u003cli\u003ePoint and interval estimation\u003c/li\u003e \r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eStatistics 1b: Statistical methods\u003c/b\u003e\r\n\u003cul\u003e\r\n\u003cli\u003eHypothesis testing I\u003c/li\u003e \r\n\u003cli\u003eHypothesis testing II\u003c/li\u003e \r\n\u003cli\u003eContingency tables and the chi-squared test\u003c/li\u003e \r\n\u003cli\u003eSampling design and some ideas underlying causation\u003c/li\u003e \r\n\u003cli\u003eCorrelation and linear regression\u003c/li\u003e \r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eStatistics 2a: Probability and distribution theory \u003c/b\u003e\r\n\u003cul\u003e\r\n\u003cli\u003eProbability theory I\u003c/li\u003e \r\n\u003cli\u003eProbability theory II\u003c/li\u003e \r\n\u003cli\u003eRandom variables\u003c/li\u003e \r\n\u003cli\u003eCommon distributions of random variables\u003c/li\u003e \r\n\u003cli\u003eMultivariate random variables\u003c/li\u003e \r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eStatistics 2b: Statistical inference\u003c/b\u003e\r\n\u003cul\u003e\r\n\u003cli\u003eSampling distributions of statistics\u003c/li\u003e \r\n\u003cli\u003ePoint estimation I\u003c/li\u003e \r\n\u003cli\u003ePoint estimation II and interval estimation\u003c/li\u003e \r\n\u003cli\u003eHypothesis testing\u003c/li\u003e \r\n\u003cli\u003eAnalysis of variance (ANOVA)\u003c/li\u003e \r\n\u003c/ul\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3df:T808,"])</script><script>self.__next_f.push([1,"\u003cp\u003eData engineers and Big Data professionals are in overwhelming demand. NoSQL and Big Data technology skills such as Apache Spark are a must-have for modern day data-driven decision-making. This three-course Professional Certificate from IBM opens the door for data engineering and big data careers.\u003c/p\u003e\r\n\r\n\u003cp\u003eStarting with \u003cb\u003eNoSQL Database Basics\u003c/b\u003e, this course introduces you to NoSQL fundamentals, including the four key non-relational database categories. By the end of the course, you will have hands-on skills working with MongoDB, Cassandra, and IBM Cloudant NoSQL databases.\u003c/p\u003e\r\n\r\n\u003cp\u003eA crucial aspect of data engineering is the acquisition and management of Big Data and Big Data Analytics scalability and performance. When you enroll in \u003cb\u003eBig Data, Hadoop, and Spark Basics\u003c/b\u003e, you'll discover the characteristics, features, benefits, limitations, and applications of some of the more popular Big Data processing tools. You explore the open-source ecosystem of Apache tools, including Apache Hadoop, Apache Hive, and Apache Spark, including Spark on Kubernetes. Discover how to leverage Spark to deliver reliable insights. You'll gain hands-on data analysis skills using PySpark and Spark SQL and create a streaming analytics application using Spark Streaming, and more.\u003c/p\u003e\r\n\r\n\u003cp\u003eThen enroll in \u003cb\u003eApache Spark for Data Engineering and Machine Learning\u003c/b\u003e to discover how data and machine learning engineers use Spark Structured Streaming, GraphFrames, Regression, Classification, and clustering. Learn about clustering and how to apply the k-means clustering algorithm using Spark MLlib. Extraction Transformation and Loading, (ETL) is at the heart of data and machine learning engineering, and you'll gain skills using Spark to perform extract, transform and load (ETL) tasks. This course culminates with a hands-on Spark project.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis Professional Certificate does not require any prior programming or data science skills; however, prior basic data literacy and SQL skills will prove valuable in completing this program.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3e0:T925,"])</script><script>self.__next_f.push([1,"\u003cp\u003eWhether you want to make a strong start to a master’s degree, solidify your knowledge in a professional context or simply brush up on fundamentals in probability and statistics, this program will get you up to speed.\u003c/p\u003e\r\n\r\n\u003cp\u003eStatistics is used quite intensively in many engineering contexts and master’s programs. As soon as you are dealing with real-life data, you will need to get an idea of what these data tell you and how you can visualize this (descriptive statistics). You will also want to perform some analysis (inferential statistics), build a model that mimics reality, estimate some quantities, or test some hypotheses. Along the way you will learn how to apply these concepts to datasets, using the statistical software R.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program also provides an introduction to probability theory. You will encounter discrete and continuous random variables and learn in which situations they appear, what their properties are and how they interact. Probability theory can be applied to learn more about real-life problems, and it is useful for building models. Moreover, it provides the basis for statistics and applications in data analysis. Therefore, it is a useful subject for any aspiring engineer.\u003c/p\u003e\r\n\r\n\u003cp\u003eThese courses are self-paced, self-contained and modular, to make it easier to review specific topics and practice as often as you want without having to follow the entire courses.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program is ideal for:\r\n\u003cul\u003e\r\n\u003cli\u003eProspective engineering students who want to meet the prerequisites for a MSc program, be better prepared or refresh their mathematics knowledge before starting a master’s degree.\u003c/li\u003e\r\n\u003cli\u003eEngineering or bachelor students who realize that they have a gap in their math knowledge or would like an additional challenge in mathematics not offered by their studies.\u003c/li\u003e\r\n\u003cli\u003eWorking professionals who would like to improve their math knowledge.\u003c/li\u003e\r\n\u003cli\u003eAnyone interested in university level mathematics.\u003c/li\u003e\r\n\u003c/ul\u003e\r\n\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program will refresh your knowledge and review the relevant topics. As review courses, you are expected to have previously studied or be familiar with most of the material.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program is part of our series ‘Mastering Mathematics for Engineers’, together with ‘Mastering Calculus’ and ‘Mastering Linear Algebra’.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3e1:Ta51,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThe study of neuroscience includes many branches, including neuropsychology, molecular neuroscience, cognitive neuroscience, and behavioral neuroscience, among many others. Expand your biomedical research expertise by learning how to use mouse behaviors to study psychiatric disorders in humans. Before research is conducted to understand the human brain, cognition, and the nervous system, studies often first use mice because of the similarities in neuroanatomy, neurophysiology, and neurobiology. By studying brain function in mice, we gain insight into how the human brain works.\u003c/p\u003e \r\n\r\n\u003cp\u003eEnhance your knowledge of mental health conditions such as obsessive-compulsive disorder, anxiety, and depression in humans and animals, and the role of animal models and animal behavior in studying human behavior and psychiatric disorders by comparing and contrasting human and mouse behavior.\r\nCreated by Professor Abel Bult-Ito at the University of Alaska Fairbanks, this certificate program, helps you obtain the skills you need to succeed in the biomedical research field by covering the fundamentals of behavioral neuroscience research. Enrollment in the certificate program includes three online neuroscience courses that focus on research using mice and mental health conditions such as obsessive-compulsive disorder, depression, and anxiety. Behavioral health, such as the treatment of psychiatric disorders and prevention of suicide, is still one of the biggest challenges in medicine and healthcare today and will continue to be a top priority for scientific research.\u003c/p\u003e\r\n\r\n\u003cp\u003eBy completing these online courses, you’ll have learned responsible conduct of research in behavioral neuroscience; collected, analyzed, and interpreted behavioral data from mice; critically evaluated the use of different behavioral tests in mice; and established a foundation in using behavioral tests in rodents. (Backgrounds in data analysis or data science are not necessary to successfully complete these courses.) Learners will also have obtained important behavioral neuroscience tools, knowhow, and expertise.\r\n\u003cul\u003e\r\n\u003cli\u003eLearn responsible conduct of research in order to collect, analyze, and interpret behavioral data from mouse videos.\u003c/li\u003e\r\n\u003cli\u003eCritically evaluate the use of different behavioral tests in mice.\u003c/li\u003e\r\n\u003cli\u003eEstablish a foundation in using behavioral tests in rodents.\u003c/li\u003e\r\n\u003cli\u003eUnderstand obsessive-compulsive disorder, anxiety, and depression in humans and animals.\u003c/li\u003e\r\n\u003cli\u003eUnderstand the role of animal models in studying human psychiatric disorders by comparing and contrasting human and mouse behaviors.\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3e2:Tc66,"])</script><script>self.__next_f.push([1,"\u003cb\u003e\u003cp\u003eEssential Math for AI:\u003c/b\u003e\u003c/br\u003e\r\nEssential Math for AI is the first course within the two-part bridge series designed to ensure learners possess the prerequisite skills for more advanced courses in the \u003ca href=\"https://ai.engineering.columbia.edu/\"\u003eAI Professional Certificate program\u003c/a\u003e. This course serves as a review and refresher of the key mathematical concepts - discrete math, calculus, linear algebra, and probability theory. It is not an in-depth exploration of these topics; instead, it will focus on concepts that have applications in various areas of artificial intelligence.\u003c/p\u003e\r\n\r\n\u003cp\u003eBy completing this course, you will be prepared to tackle advanced AI courses with confidence. This course is specifically crafted to bridge any gaps in mathematical knowledge, ensuring a robust understanding of fundamental concepts in math. Throughout this course, you will develop and refine essential skills and knowledge, recalling and articulating basic concepts in discrete math, calculus, linear algebra, and probability theory. Additionally, you will be able to apply the acquired knowledge to solve problems across various areas of artificial intelligence.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis course is offered by Professor Daniel Bauer, a renowned Lecturer in the discipline of natural language processing in the department of computer science at Columbia Engineering, Columbia University. It offers a unique opportunity to learn from one of the top engineering schools, enhancing your credentials and positioning you for success in the rapidly evolving field of artificial intelligence.\u003c/p\u003e \r\n\r\n\u003cb\u003e\u003cp\u003eProgramming \u0026 Data Structures:\u003c/b\u003e\u003c/br\u003e\r\nProgramming \u0026 Data Structures is the second course within the two-part bridge series designed to ensure learners possess the prerequisite skills for more advanced courses in the \u003ca href=\"https://ai.engineering.columbia.edu/\"\u003eAI Professional Certificate program\u003c/a\u003e. This course serves as a review and refresher of the key concepts in programming and data structures, emphasizing their applications in various areas of artificial intelligence.\u003c/p\u003e \r\n\r\n\u003cp\u003eBy taking this course, you will develop fundamental programming skills and utilize built-in data structures and object-oriented programming concepts in Python for effective data manipulation and algorithm development. By the end of this course, you will be familiar with essential Python packages for data analysis, visualization, numeric computing, and machine learning. Additionally, you will be able to write and debug simple programs in Python, including using functions, object-oriented programming, and built-in data structures like lists and dictionaries. Finally, you will understand and use basic functionality in NumPy, Matplotlib, Sci-kit learn, and Pandas.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis course is offered by Professor Daniel Bauer, a renowned Lecturer in the discipline of natural language processing in the department of computer science at Columbia Engineering, Columbia University. It offers a unique opportunity to learn from one of the top engineering schools, enhancing your credentials and positioning you for success in the rapidly evolving field of artificial intelligence.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3e3:T40f,\u003cp\u003eThis Healthcare Data Analytics Toolkit MicroMasters program introduces you to the transformative role of data analysis in healthcare. It's designed specifically for those at the beginning of their journey in healthcare data analytics, providing foundational knowledge and skills. Throughout this series, you'll delve into the integration of data analytics in healthcare settings, gaining hands-on experience in analyzing data to draw meaningful conclusions and apply these insights to real-world healthcare challenges.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe introductory series focuses on the basics of data analysis and its application in healthcare settings. You’ll learn the essentials of analyzing data, understanding trends, and making data-informed decisions.\u003c/p\u003e\r\n\r\n\u003cp\u003eWhether you are working in a hospital, leading projects in a healthcare NGO, launching a digital health startup, or investing in cutting-edge healthcare technology, our program provides the foundation for the application of data analysis to solve healthcare's biggest challenges.\u003c/p\u003e3e4:Ta63,"])</script><script>self.__next_f.push([1,"\u003cp\u003eStatistics is everywhere! This program will prepare learners for subsequent advanced courses and eventually careers in consultancy, research, and industry – any professions involving data analysis and optimization of real-world systems. The course is brought to you by faculty from Georgia Tech’s top-ranked School of Industrial and Systems Engineering.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis first half of this program (Course 1) provides an introduction to basic statistical concepts. We begin by walking through a library of probability distributions, where we motivate their uses and go over their fundamental properties. These distributions include such important folks as the Bernoulli, binomial, geometric, Poisson, uniform, exponential, and normal distributions, just to name a few. Particular attention is paid to the normal distribution, because it leads to the Central Limit Theorem (the most-important mathematical result in the universe, actually), which enables us to make probability calculations for arbitrary averages and sums of random variables.\u003c/p\u003e \r\n\r\n\u003cp\u003eWe then discuss elementary descriptive statistics and estimation methods, including unbiased estimation, maximum likelihood estimation, and the method of moments – you gotta love your MoM! Finally, we describe the t, χ2, and F sampling distributions, which will prove to be useful in upcoming statistical applications.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe second half of the program (Course 2) covers two important methodologies in statistics – confidence intervals and hypothesis testing. Confidence intervals are encountered in everyday life, and allow us to make probabilistic statements such as: “Based on the sample of observations we conducted, we are 95% sure that the unknown mean lies between A and B,” and “We are 95% sure that Candidate Smith’s popularity is 52% +/- 3%.” We begin the course by discussing what a confidence interval is and how it is used. We then formulate and interpret confidence intervals for a variety of probability distributions and their parameters.\u003c/p\u003e\r\n\r\n\u003cp\u003eHypothesis testing allows us to pose hypotheses and test their validity in a statistically rigorous way. For instance, “Does a new drug result in a higher cure rate than the old drug” or “Is the mean tensile strength of item A greater than that of item B?” The second half the course begins by motivating hypothesis tests and how they are used. We then discuss with the types of errors that can occur with hypothesis testing, and how to design tests to mitigate those errors. Finally, we formulate and interpret hypothesis tests for a variety of probability distributions and their parameters.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3e5:Tb5c,"])</script><script>self.__next_f.push([1,"\u003cP\u003eRemote sensing observations from airborne and spaceborne SAR satellite platforms have become an essential tool in earth observation. They provide an immediate and large-area overview of the evolving earth environment, revealing important information on the state of ecosystems, unfolding natural hazards, as well as geodynamic phenomena across the earth’s surface such as volcanoes, earthquakes, and the cryosphere.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis certificate program will introduce you to the concepts and applications of Synthetic Aperture Radar (SAR), a remote sensing technology that can see the ground even during darkness and through rain, clouds, or smoke. As a participant completing the certificate, you will gain an intuitive understanding of the information contained in SAR observations. You will learn about the concepts of interferometric SAR and experience how SAR data acquired at different polarizations can reveal a wealth of information about the earth environment. Based on these foundations of SAR, interferometric SAR, and polarimetric SAR, participants will learn about how SAR and change detection techniques can be applied to the monitoring of natural hazards and to the analysis of the Earth’s ecosystems.\u003c/p\u003e\r\n\r\n\u003cp\u003eSpecific topics include:\r\n\u003cul\u003e\r\n\u003cli\u003eThe mathematical and physical principles of SAR remote sensing.\u003c/li\u003e\r\n\u003cli\u003eHow to access and visualize SAR data.\u003c/li\u003e\r\n\u003cli\u003eInterpretation of SAR imagery at different wavelengths and polarizations.\u003c/li\u003e\r\n\u003cli\u003eInterferometric SAR (InSAR) concepts.\u003c/li\u003e\r\n\u003cli\u003eThe principles of Polarimetric SAR (PolSAR).\u003c/li\u003e\r\n\u003cli\u003eApplication of SAR and InSAR to hazard monitoring with a focus on natural hazards such as earthquakes, volcanoes, landslides, wildfires, and flooding.\u003c/li\u003e\r\n\u003cli\u003eApplication of SAR and polarimetric SAR data to the analysis of ecosystems and their change including monitoring agriculture extent, forest monitoring for forest fires and deforestation, and forest biomass estimation.\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eLearners on the verified track will put their learned knowledge into action in data analysis and data processing exercises, in which class participants will analyze SAR, InSAR and polarimetric SAR data sets, and apply them in hazard and ecosystems monitoring case studies. Learners who select the verified track will also have access to online computational labs using Jupyter notebooks that will allow deeper exploration and practice.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eRecommended Prerequisites\u003c/p\u003e\u003c/b\u003e\r\n\r\n\u003cp\u003eIn order for learners to succeed in the courses in this professional certificate, the following prerequisites and trainings are recommended:\r\n\u003cul\u003e\r\n\u003cli\u003eGeneral proficiency in GIS\u003c/li\u003e\r\n\u003cli\u003eFor verified track: Basic knowledge in Python programming\u003c/li\u003e\r\n\u003cli\u003eARSET Level-0 Training “Fundamentals of Remote Sensing” or equivalent\u003c/li\u003e\r\n\u003cli\u003eARSET Level-1 Training “Introduction to Synthetic Aperture Radar” or equivalent\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3e6:T82e,"])</script><script>self.__next_f.push([1,"\u003cp\u003eDo you want to inform healthcare decisions by conducting research on the effectiveness, benefits, and potential harms of treatment options? Would you like to help patients choose care that best meets their needs? Perhaps you’d like to learn the basics of clinical research? Or maybe you’d like to use a national health data registry to answer a research question? If any of these questions resonate Comparative Effectiveness Research Training and Instruction – CERTaIN - Professional Certificate Program is right for you!\u003c/p\u003e\r\n\r\n\u003cp\u003eCreated by investigators from The University of Texas MD Anderson Cancer Center and partner institutions, the CERTaIN Professional Certificate program provides a comprehensive overview of core concepts, research methods and data analysis techniques used in comparative effectiveness (CER) and patient-centered outcomes research (PCOR) across five key areas:\r\n\r\n\u003cbr\u003e\u003cbr\u003eCourse 1. Introduction\r\n\u003cbr\u003eCourse 2. Knowledge Synthesis\r\n\u003cbr\u003eCourse 3. Patient-Centered Outcomes Research (PCOR)\r\n\u003cbr\u003eCourse 4. Pragmatic Clinical Trials and Healthcare Delivery Evaluations\r\n\u003cbr\u003eCourse 5. Observational Studies and Registries\u003c/p\u003e\r\n\r\n\u003cp\u003eLearn from expert decision scientists, biostatisticians, oncologists, economists, social scientists, health care policy experts and epidemiologists how to conduct CER/PCOR and see how CER/PCOR methods have been applied in the real world to conduct state of the art research studies.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe CERTaIN Professional Certificate Program is intended for anyone interested in CER/PCOR methods. This program is comprised of 5 courses and includes a combined total of almost 50 lectures in CER/PCOR topics. Each course consists of a series of lectures delivered by content experts and each lecture is segmented into short videos, followed by a quiz to assess your understanding of the material.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe CERTaIN Professional Certificate Program is supported by grant number R25HS023214 from the Agency for Healthcare Research and Quality. \u003c/p\u003e\r\n\r\n\u003cp\u003eThe CERTaIN Professional Certificate Program was created by investigators.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3e7:T774,\u003cp\u003eIt’s time to make a decision: beach or mountains? When choosing where you want to go for vacation, it can be simple. The options may be a or b. From a decision-making standpoint, it’s easy for the brain to process this decision tree. But, what happens when you’re faced with more complex, multifaceted decisions? You might make a comprehensive pro/con list, rank ordering the most important considerations. But, that can take endless amounts of time that you might not have to spare. When parsing through thousands or millions of data points, you and your organization need to tap into a more sophisticated approach. \u003c/p\u003e\n\u003cp\u003eThe solution? Harnessing the power of artificial intelligence (AI) through machine learning to enhance your decision-making processes. Machine learning with Python can not only help organize data, but machines can also be taught to analyze and learn from disparate data sets – forming hypotheses, creating predictions, and improving decisions.\u003c/p\u003e\n\u003cp\u003eIn Machine Learning and AI with Python, you will explore the most basic algorithm as a basis for your learning and understanding of machine learning: decision trees. Developing your core skills in machine learning will create the foundation for expanding your knowledge into bagging and random forests, and from there into more complex algorithms like gradient boosting. \u003c/p\u003e\n\u003cp\u003eUsing real-world cases and sample data sets, you will examine processes, chart your expectations, review the results, and measure the effectiveness of the machine’s techniques.\u003c/p\u003e\n\u003cp\u003eThroughout the course, you will witness the evolution of the machine learning models, incorporating additional data and criteria – testing your predictions and analyzing the results along the way to avoid overtraining your data, mitigating overfitting and preventing biased outcomes.\u003c/p\u003e\n\u003cp\u003ePut your data to work through machine learning with Python.\u003c/p\u003e3e8:T975,"])</script><script>self.__next_f.push([1,"\u003cp\u003eHow much would you like your smart home to know about you? Has your data been harvested and used for political advertising on social media? Would you be happy to be profiled by a predictive policing AI?\u003c/p\u003e\n\u003cp\u003eAs we create more data-driven technologies, those issues become increasingly urgent. We must begin to ask not only ‘what can we do?’, but also ‘what should we do?’ How should we design new technologies to make sure they are used for good, not bad purposes?\u003c/p\u003e\n\u003cp\u003eThe ‘good’, the ‘bad’, and the ‘should’ are a domain of ethics, and a basis for other important concepts such as justice, fairness, rights, respect. They further inform the law and what is legal. Finally, they are at the roots of an extremely important currency in the modern economy: trust.\u003c/p\u003e\n\u003cp\u003eThis story-driven course is taught by the leading experts in data science, AI, information law, science and technology studies, and responsible research and innovation, and informed by case studies supplied by digital business frontrunners and tech companies. We will look at real-world controversies and ethical challenges to introduce and critically discuss the social, political, legal and ethical issues surrounding data-driven innovation, including those posed by big data, AI systems, and machine learning systems. We will drill down into case studies, structured around core concerns being raised by society, governments and industry, such as bias, fairness, rights, data re-use, data protection and data privacy, discrimination, transparency and accountability. Throughout the course, we will emphasise the importance of being mindful of the realities and complexities of making ethical decisions in a landscape of competing interests.\u003c/p\u003e\n\u003cp\u003eWe will engage with data-based contexts such as facial recognition, predictive policing, medical screening, smart homes and cities, banking, and AI, to explore their social implications and the tools required to minimise harm, promote fairness, and safeguard and increase human autonomy and well-being. We address cutting edge issues being grappled with by practitioners and new approaches emerging in industry and offer the opportunity for participants to develop and feedback solutions.\u003c/p\u003e\n\u003cp\u003eCompleting this course will help you understand the challenges we are facing and inspire you to design, criticise, and develop better intelligent systems to shape our future.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3e9:T723,\u003cp\u003eAre you ready to scale your (tiny) machine learning application? Do you have the infrastructure in place to grow? Do you know what resources you need to take your product from a proof-of-concept algorithm on a device to a substantial business?\u003c/p\u003e\n\u003cp\u003eMachine Learning (ML) is more than just technology and an algorithm; it's about deployment, consistent feedback, and optimization. Today, more than 87% of data science projects never make it into production. To support organizations in coming up to speed faster in this critical domain it is essential to understand Machine Learning Operations (MLOps). This course introduces you to MLOps through the lens of TinyML (Tiny Machine Learning) to help you deploy and monitor your applications responsibly at scale.\u003c/p\u003e\n\u003cp\u003eMLOps is a systematic way of approaching Machine Learning from a business perspective. This course will teach you to consider the operational concerns around Machine Learning deployment, such as automating the deployment and maintenance of a (tiny) Machine Learning application at scale. In addition, you’ll learn about relevant advanced concepts including neural architecture search, allowing you to optimize your models' architectures automatically; federated learning, allowing your devices to learn from each other; and benchmarking, enabling you to performance test your hardware before pushing the models into production.\u003c/p\u003e\n\u003cp\u003eThis course focuses on MLOps for TinyML (Tiny Machine Learning) systems, revealing the unique challenges for TinyML deployments. Through real-world examples, you will learn how tiny devices, such as Google Homes or smartphones, are deployed and updated once they’re with the end consumer, experiencing the complete product life cycle instead of just laboratory examples.\u003c/p\u003e\n\u003cp\u003eAre you ready for a billion users?\u003c/p\u003e3ea:T457,\u003cp\u003eIn this course, you will:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eExplore essential data engineering platforms (Hadoop, Spark, and Snowflake) and learn how to optimize and manage them\u003c/li\u003e\n\u003cli\u003eDelve into Databricks, a powerful "])</script><script>self.__next_f.push([1,"platform for executing data analytics and machine learning tasks\u003c/li\u003e\n\u003cli\u003eHone your Python data science skills with PySpark\u003c/li\u003e\n\u003cli\u003eDiscover the key concepts of MLflow, an open-source platform for managing the end-to-end machine learning lifecycle, and learn how to integrate it with Databricks\u003c/li\u003e\n\u003cli\u003eGain methodologies to help you improve your project management and workflow skills for data engineering, including applying Kaizen, DevOps, and Data Ops best practices\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis course is designed for learners who want to pursue or advance their career in data science or data engineering, or for software developers or engineers who want to grow their data management skill set. With quizzes to test your knowledge throughout, this comprehensive course will help guide your learning journey to become a proficient data engineer, ready to tackle the challenges of today's data-driven world.\u003c/p\u003e3eb:T81f,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eThis data analytics course takes an interdisciplinary approach to demonstrate the data analytics process in the context of accounting and finance. The growing volume of both structured and unstructured data has pushed forward a more data-driven form of decision-making in accounting and finance. In order to keep up with the Big Data era advancements, accountants and finance professionals need to have a data analyst mindset to excel in their jobs.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis course will illustrate different concepts of accounting and finance with the application of data analytics. It will not only help the learners to develop their skills to ask the right questions but also teach them how to master the data and use different tools like Excel and Tableau to analyze the data. In the end, the learners will be able to interpret the results and make their decisions effectively.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis course will use a simple framework that helps the learners to develop an analytical mindset. This framework (QDAR) has four major components:\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e1.\u003cspan lang=\"EN-US\"\u003e Ask the right \u003cstrong\u003eQ\u003c/strong\u003e uestions to address an issue in accounting or finance contexts.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e2.\u003cspan lang=\"EN-US\"\u003e Understand the different data types and how to retrieve and clean \u003cstrong\u003eD\u003c/strong\u003e ata.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e3.\u003cspan lang=\"EN-US\"\u003e Conduct different data \u003cstrong\u003eA\u003c/strong\u003e nalyses to answer the questions\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e4.\u003cspan lang=\"EN-US\"\u003e Communicate the \u003cstrong\u003eR\u003c/strong\u003e esults to the decision-makers using graphs, visualizations and reports.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe whole course will cover different aspects of the framework in conjunction with different types of analyses. There will be additional datasets for the verified learners through which they can practice what they have learned during the course.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3ec:T6a7,\u003cp\u003eIn this course, you will learn ways in which technology has changed how financial markets and institutions function. Every part of the financial value chain is being “disrupted” by nimble technology-based innovators. Where personal relationships used to determine success and failure, the ability to process and act on massive amounts of data is taking over. \u003c/p\u003e\n\u003cp\u003eSecurities trading and securities issuance, typically the turf of large institutions, is being taken over by technology. High frequency and algorithmic trading take advantage of fast access to information and technology to trade in milliseconds. Financial market operators have morphed into pure technology firms and data providers. \u003c/p\u003e\n\u003cp\u003eTechnology and financial innovations can be used for good or not so good purposes. For instance, it can be used to exploit the information of less sophisticated investors, or to provide them with liquidity. The course will study both the good and bad, regulatory aspects, and ethical considerations. \u003c/p\u003e\n\u003cp\u003eAll these aspects are changing the skills required to be successful in digital capital markets. We will also cover how finance has changed and is changing, the skills required to be successful and what the future may bring. Where possible we will link new phenomena to classical finance theory and highlight where predictions and reality have diverged.\u003c/p\u003e\n\u003cp\u003eAn introduction to technology in capital markets, the course is taught by a world-class instructor, actively involved in academic and policy research and training the next generation of traders. This course will prepare students from many backgrounds for careers in financial markets and financial market technology.\u003c/p\u003e3ed:T4f7,\u003cp\u003eWith more than 328 million terabytes of data produced each day, the potential threat of security and data breaches worldwide is constantly looming. Considering more than 500 billion data records were compromised in 2023 alone, businesses and individuals grapple with the new reality that hacking and breaches are becoming mo"])</script><script>self.__next_f.push([1,"re sophisticated by the minute.\u003c/p\u003e\r\n\r\n\u003cp\u003eIn this changing landscape, those who are well equipped to not only manage threats or compromised systems, but also anticipate and prevent such attacks, will position themselves and their organizations for long term success.\u003c/p\u003e\r\n\r\n\u003cp\u003eBy combining two of HarvardX’s most popular courses, CS50's Introduction to Computer Science and CS50's Introduction to Cybersecurity, learners will engage with a unique blend of programming skills, computer science fundamentals, and specialized insights into the world of cybersecurity.\u003c/p\u003e\r\n\r\n\u003cp\u003eRegister today to explore the convergence of data science and cybersecurity in this CS50 Professional Certificate Program, where you will understand the threats and opportunities in the cybersecurity space, gaining the ability to make timely decisions when facing risk, predict vulnerabilities, and enable preemptive measures to safeguard against looming threats.\u003c/p\u003e3ee:T5d1,\u003cp\u003eThe transformational impact of artificial intelligence (AI) is reshaping our world. 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You’ll learn prompt engineering techniques to write effective prompts for producing desired outcomes from generative AI tools.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe program introduces you to chatbots and their benefits and teaches you how to build them without coding using Watson Assistant.\u003c/p\u003e\r\n\r\n\u003cp\u003eIn this Professional Certificate, you’ll work on projects that showcase proficiency in applying AI and building generative AI-powered solutions. You’ll also learn practical, hands-o"])</script><script>self.__next_f.push([1,"n Python skills to design, build, and deploy AI applications on the web. You'll learn to create AI applications, including generative AI-powered apps and chatbots, using Python and Flask. You'll utilize open-source resources and APIs from platforms like IBM watsonx to build smart applications with minimal coding.\u003c/p\u003e\r\n\r\n\u003cp\u003eEnroll today to leverage the power of AI in your career and life!\u003c/p\u003e3ef:T89a,"])</script><script>self.__next_f.push([1,"\u003cp\u003eSoftware Engineering professionals, especially those with DevOps skills, are in high demand! According to a recent GitLab report, the need for DevOps skills is expected to grow 122% over the next five years, making it one of the fastest growing markets in the workforce. Additionally, Glassdoor’s salary report shows the average total pay for a DevOps Engineer in the US is $121,000. There has never been a better time to start a DevOps career path.\u003c/p\u003e\r\n \r\n\u003cp\u003eThis self-paced certificate program of online courses, built for beginners, will equip you with the key DevOps concepts and technical know-how to build your Software Development skills and knowledge with DevOps practices, tools, and technologies. By the end of this program, you will be prepared for an entry-level role in Software Engineering with an organization of DevOps practitioners.\u003c/p\u003e \r\n \r\n\u003cp\u003eYou will develop skill sets in a variety of DevOps philosophies, fundamentals, and methodologies, including Agile Development, Scrum Methodology, Cloud Native Architecture, Behavior and Test-Driven Development (BDD and TDD), and Zero Downtime Deployments. The program also touches on data science, cloud computing, programming languages, machine learning, continuous delivery, IBM cloud, and agile software development for DevOps professionals.\u003c/p\u003e\r\n\r\n\u003cp\u003eGuided by experts at IBM, you will learn how to:\r\n\u003cli\u003eprogram with the Python language and Linux shell scripts,\u003c/li\u003e \r\n\u003cli\u003ecreate projects on GitHub,\u003c/li\u003e \r\n\u003cli\u003econtainerize and orchestrate your applications using Docker, Kubernetes \u0026 OpenShift,\u003c/li\u003e \r\n\u003cli\u003ecompose applications with microservices,\u003c/li\u003e \r\n\u003cli\u003eemploy serverless technologies,\u003c/li\u003e \r\n\u003cli\u003eperform continuous integration and delivery (CI/CD),\u003c/li\u003e \r\n\u003cli\u003edevelop test cases,\u003c/li\u003e \r\n\u003cli\u003eensure your code is secure,\u003c/li\u003e \r\n\u003cli\u003eand monitor \u0026 troubleshoot your cloud deployments.\u003c/li\u003e\r\n\u003c/ul\u003e\r\n\u003c/p\u003e\r\n \r\n\u003cp\u003eLabs and projects in this program are designed to equip you with job-ready hands-on skills to help you launch a new career in an in-demand field.\u003c/p\u003e \r\n \r\n\u003cp\u003eThis software engineering professional certificate is suitable for those with no or some programming experience and those with or without college degrees.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3f0:T663,\u003cp\u003eIn today’s world, businesses can only survive and remain competitive if they embrace and leverage core technologies like cloud, AI, and data science. These are critical to driving significant growth and innovation within organizations, and are essential for the future of business.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe courses featured in this program provide you with the core foundational knowledge of all three core technologies, including their business value and impact they provide, their applications and use cases, and an understanding of how these technologies work. You will not only become familiar with the common terms and tools associated with cloud, AI, and data science, but also experience them in action and learn hands-on skills to start working with them.\u003c/p\u003e\r\n\r\n\u003cp\u003eEach course includes hands-on assignments and interactions that demonstrate specific technologies in action. These practical exercises are suitable for even those without any specialized IT skills or programming knowledge. In fact, there is no prerequisite knowledge required to be successful in this program other than basic computer literacy skills and experiences with devices that support modern web browsers.\u003c/p\u003e\r\n\r\n\u003cp\u003eAlmost any organization that looks to disrupt or pioneer in a global or local market, and any individual looking to contribute to this vision needs to understand and leverage these essential technologies. What’s more, having skills and understanding in all three together will further increase your competitiveness and value in your company and the job market at large, and ultimately help you and your company reach your full potential.\u003c/p\u003e3f1:T5c0,\u003cp\u003eThe courses in the XSeries are designed to help people with no prior exposure to computer science or programming learn to think computationally and write programs to tackle useful problems. Some of the people taking the two courses will use them as a stepping stone to more advanced computer science courses, but for many it will be their first and last computer science courses. Since these "])</script><script>self.__next_f.push([1,"courses may be the only formal computer science courses many of the students take, we have chosen to focus on breadth rather than depth. The goal is to provide students with a brief introduction to many topics so they will have an idea of what is possible when they need to think about how to use computation to accomplish some goal later in their career. That said, they are not “computation appreciation” courses. They are challenging and rigorous courses in which the students spend a lot of time and effort learning to bend the computer to their will.\u003c/p\u003e\r\n\r\n\u003cp\u003eIntroduction to Computer Science and Programming Using Python covers the notion of computation, the Python programming language, some simple algorithms, testing and debugging, and informal introduction to algorithmic complexity, and some simple algorithms and data structures. Introduction to Computational Thinking and Data Science will teach you how to use computation to accomplish a variety of goals and provides you with a brief introduction to a variety of topics in computational problem solving.\u003c/p\u003e3f2:T5f1,\u003cp\u003eThe world of Artificial Intelligence (AI) is no longer science fiction. Instead, it is rapidly permeating across all industries and impacting every aspect of our daily-life. Whether you are an industry professional, an executive, an entrepreneur or a student – developing a foundational understanding of AI and its impact on your organization and our society is of paramount importance and will transform your career.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program is meant for those with little or no background in AI, computer science, or data science and does not require any programming skills. Each course is designed to provide a relevant and applicable comprehension of the basic concepts of AI and its many applications and use cases across various industries. You will also be introduced to terms like machine learning, deep learning, neural networks and natural language processing.\u003c/p\u003e\r\n \r\n\u003cp\u003eAs you journey through the series you will also be introduced to IBM Watson A"])</script><script>self.__next_f.push([1,"I services that enable any business to easily deploy pre-built AI smarts to across your business. When you are ready you can build up to more complex topics in our full 6-course Applied AI Professional Certificate program where you be able to develop and deploy AI powered applications.\u003c/p\u003e\r\n \r\n\u003cp\u003eThrough hands-on interactions with several AI environments and applications you will also learn about creating intelligent virtual assistants and how they can be leveraged in different scenarios in your current job or as a way to jumpstart your next career.\u003c/p\u003e3f3:Ta57,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIn this exciting Professional Certificate program offered by Harvard University and Google TensorFlow, you will learn about the emerging field of Tiny Machine Learning (TinyML), its real-world applications, and the future possibilities of this transformative technology.\u003c/p\u003e\r\n\r\n\u003cp\u003eTinyML is a cutting-edge field that brings the transformative power of machine learning (ML) to the performance- and power-constrained domain of tiny devices and embedded systems. Successful deployment in this field requires intimate knowledge of applications, algorithms, hardware, and software.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis first course in this series, Fundamentals of TinyML, will teach you the fundamentals of machine and deep learning. In this course, you will understand the language of tiny machine learning, which goes beyond the traditional machine learning toolkit due to the energy and memory constraints of tiny devices. The second course, Applications of TinyML, dives into an array of applications, where you will see how tools like voice recognition works in practice on small devices and you can see and implement common algorithms such as neural networks.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe third course, Deploying TinyML, will give you a chance to use an open source hardware and prototyping platform to build your own tiny device. Featuring projects based on an Arduino board—TinyML Program Kit—the program emphasizes hands-on experience with training and deploying machine learning into tiny embedded devices. The TinyML Program Kit has everything you need to unlock your imagination and build applications around image recognition, audio processing, and gesture detection. Before you know it, you’ll be implementing an entire tiny machine learning application.\u003c/p\u003e\r\n\r\n\u003cp\u003eThroughout the series, you will learn how the Python programming language using TensorFlow (Lite/Micro) is used to power these devices as well as important topics in the responsible design of Artificial Intelligence systems. These first-of-their-kind online courses combine data science, computer science, and engineering to feature real-world application case studies that examine the challenges facing TinyML deployments.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program is a collaboration between expert faculty at Harvard’s John A. Paulson School of Engineering and Applied Sciences (SEAS) and innovative members of Google’s TensorFlow team. Taught by Harvard Professor Vijay Janapa Reddi, Lead AI Advocate at Google, Laurence Moroney, and Technical Lead of Google’s TensorFlow and Micro team, Pete Warden, this course offers you the unique opportunity to learn from leaders in the AI and machine learning space.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3f4:Tc05,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis comprehensive MicroBachelors program in Mathematics and Statistics Fundamentals introduces students to the essential mathematical and statistical concepts, methods and techniques which they can use to grow their skills in quantitative careers, or as a step towards further study at undergraduate level or in specialised subjects.\u003c/p\u003e\r\n\r\n\u2028\u003cp\u003eSpanning four individual courses, all of which are self-paced and asynchronous, this programme provides students with maximum flexibility to learn with a world-leading institution from anywhere in the world in a way that fits their schedule. Students will be introduced to foundational mathematical and statistical concepts, as well as gain essential skills in the methods of calculus and linear algebra required for economic-based subjects.\u003c/p\u003e\r\n\u2028\r\n\u003cp\u003eThese courses are based on service-level statistics courses offered as part of the University of London degree programmes in Economics, Management, Finance and the Social Sciences (EMFSS), with academic direction from the London School of Economics and Political Science (LSE). They equip students with the fundamental knowledge and tools to set them up for success in second and third-year courses in subjects such as economics, finance, data science, mathematics, statistics, business analytics and programming.\u003c/p\u003e\r\n\r\n\u003cp\u003eThose that complete this MicroBachelors program may wish to go on to apply to the University of London's academically rigorous EMFSS degree programmes that give learners the opportunity to earn a BSc from a top London university wherever they are in the world.\u003c/p\u003e\r\n\r\n\u003cp\u003eShould you wish, you may elect to just study some of the individual courses within the MicroBachelors program, perhaps to build or refresh quantitative skills for career advancement.\u003c/p\u003e\r\n\r\n\u003cp\u003eNo prior mathematics or statistics knowledge is required for this programme.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eMathematics 1a: Differential calculus\u003c/b\u003e \r\n\u003cul\u003e\r\n\u003cli\u003eFunctions and graphs\u003c/li\u003e\r\n\u003cli\u003eThe derivative\u003c/li\u003e\r\n\u003cli\u003eCurve sketching and optimisation\u003c/li\u003e \r\n\u003cli\u003eFunctions of two variables and partial derivatives\u003c/li\u003e \r\n\u003cli\u003eCritical points of two-variable functions\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eMathematics 1b: Integral calculus, algebra, and applications\u003c/b\u003e \r\n\u003cul\u003e\r\n\u003cli\u003eIntegration\u003c/li\u003e\r\n\u003cli\u003eProfit maximisation\u003c/li\u003e\r\n\u003cli\u003eConstrained optimisation\u003c/li\u003e \r\n\u003cli\u003eMatrices, vectors, and linear equations\u003c/li\u003e \r\n\u003cli\u003eSequences, series, and financial modelling\u003c/li\u003e\r\n\u003c/ul\u003e\r\n \r\n\u003cp\u003e\u003cb\u003eStatistics 1a: Introductory statistics, probability and estimation\u003c/b\u003e \r\n\u003cul\u003e\r\n\u003cli\u003eMathematical revision and the nature of statistics\u003c/li\u003e \r\n\u003cli\u003eData visualisation and descriptive statistics\u003c/li\u003e \r\n\u003cli\u003eProbability theory\u003c/li\u003e \r\n\u003cli\u003eThe normal distribution and ideas of sampling\u003c/li\u003e \r\n\u003cli\u003ePoint and interval estimation\u003c/li\u003e \r\n\u003c/ul\u003e\u003c/p\u003e\r\n \r\n\u003cp\u003e\u003cb\u003eStatistics 1b: Statistical methods\u003c/b\u003e \r\n\u003cul\u003e\r\n\u003cli\u003eHypothesis testing I\u003c/li\u003e \r\n\u003cli\u003eHypothesis testing II\u003c/li\u003e\r\n\u003cli\u003eContingency tables and the chi-squared test\u003c/li\u003e \r\n\u003cli\u003eSampling design and some ideas underlying causation\u003c/li\u003e \r\n\u003cli\u003eCorrelation and linear regression\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3f5:Tb04,"])</script><script>self.__next_f.push([1,"\u003cp\u003eCada vez es más común escuchar sobre el big data y el Data Science o ciencia de datos y cómo estos son necesarios para llevar al éxito a las organizaciones.\u003c/p\u003e\r\n\r\n\u003cp\u003eEl análisis de datos y el big data están tomando cada vez más relevancia en las organizaciones hoy en día. De acuerdo al reporte de IBM: THE QUANT CRUNCH: HOW THE DEMAND FOR DATA SCIENCE SKILLS IS DISRUPTING THE JOB MARKET, se requiere de expertos en los datos en todas las áreas de las organizaciones para mejorar los procesos estratégicos, incrementar la eficiencia operativa, reducir costos e incrementar la productividad de las empresas. Los datos, además, ayudan a identificar de forma correcta los Indicadores clave del Negocio (KPIs Key Performance Indicators) y a la par, comprender su comportamiento para detectar situaciones de riesgo.\u003c/p\u003e\r\n\r\n\u003cp\u003eLa inteligencia de negocios es realmente importante en la actualidad, las organizaciones manejan una gran cantidad de datos, por lo cual es necesario poder procesar e interpretar esta información, para que así la organización pueda tomar decisiones informadas.\u003c/p\u003e\r\n\r\n\u003cp\u003eEste programa de Certificación Profesional te permitirá conocer cómo la ciencia de datos aplicados permitirá la toma de mejores decisiones para una organización. Conviértete en un científico de datos con este programa en línea, compuesto por tres cursos y desarróllalo para que avances en tu carrera profesional.\u003c/p\u003e\r\n\r\n\u003cp\u003eSi es la primera vez que manejas datos o solo estás familiarizado con el uso de Excel, este programa te ayudará a conocer más sobre el análisis de datos para que así la toma de decisiones basada en grandes volúmenes de información sea la mejor. No necesitas ser un data scientist (científico de datos), para dominar la ciencia de datos y el procesamiento de datos, debido a la inteligencia artificial, el machine learning (aprendizaje automático), el deep learning (aprendizaje profundo) y la transformación digital, cada vez más es necesario resolver problemas complejos basados en la extracción de datos, para tomar las mejores decisiones, por ejemplo el departamento de recursos humanos, de marketing o un programador que usualmente manejan grandes cantidades de datos, se pueden beneficiar del procesamiento de datos para la toma de decisiones exitosas y el mejoramiento de procesos.\u003c/p\u003e\r\n\r\n\u003cp\u003eLos científicos de datos, se pueden beneficiar del análisis estadístico, y de los lenguajes de programación para obtener la información necesaria para realizar recomendaciones para la toma de decisiones que ayudarán a la organización, los conjuntos de datos pueden guiar en crear una mejor experiencia para el usuario, a entender las decisiones de compra, a evaluar precios, etc, este programa te mostrará cómo los datos pueden ser tu aliado profesional.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3f6:T53f,\u003cp\u003eThis certificate program will take students through roughly seven weeks of MATH 1554, Linear Algebra, as taught in the School of Mathematics at The Georgia Institute of Technology.\u003c/p\u003e \r\n\r\n\u003cp\u003eIn the first course, you will explore the determinant, which yields two important results. First, you will be able to apply an invertibility criterion for a square matrix that plays a pivotal role in, for example, computer graphics and in other more advanced courses, such as multivariable calculus. The first course then moves on to eigenvalues and eigenvectors. The goal of this part of the course is to decompose the action of a linear transformation that may be visualized. The main applications described here are to discrete dynamical systems, including Markov chains. However, the basic concepts afforded by eigenvectors and eigenvalues are useful throughout industry, science, engineering and mathematics.\u003c/p\u003e\r\n\r\n\u003cp\u003eIn the second course you will explore methods to compute an approximate solution to an inconsistent system of equations that have no solutions. This has a central role in the understanding of current data science applications. The second course then turns to symmetric matrices. They arise often in applications of the singular value decomposition, which is another tool often found in data science and machine learning.\u003c/p\u003e3f7:T495,\u003cp\u003eArtificial Intelligence and Machine Learning have become integral techniques for most services and products and data is central to robust and successful AI/ML applications. Without solid data management, AI projects frequently underperform or even fail.\u003c/p\u003e \r\n \r\n\u003cp\u003eThis series of MOOCs provides unique insights and key considerations about data for AI. In this program you will discover why is data important to AI and what data AI requires. You will learn basic data engineering skills, including how to setup your Python notebook environment, explore data with advanced Pandas functions, and create simple and clear data visualizations. You will also understand how crowdsourc"])</script><script>self.__next_f.push([1,"ing offers a viable means of leveraging human intelligence at scale for data creation, enrichment and interpretation and discover its potential to both improve the performance and trustworthiness of AI systems and stimulate the increased adoption of AI in general.\u003c/p\u003e \r\n\r\n\u003cp\u003eThese learnings will provide you with an important set of skills that are essential for career trajectories in the field of Data Science, Machine Learning, and the broader realms of Artificial Intelligence.\u003c/p\u003e3f8:T56a,\u003cp\u003eLa ciencia de datos está transformando la manera en cómo las organizaciones trabajan con sus datos, ya que permite generar información más precisa, confiable, en tiempo real y que sustenta además de una toma de decisiones histórica, una estrategia proactiva. Con estas herramientas, las empresas modernas están automatizando procesos capaces de tomar decisiones autónomas lo que se convierte en una ventaja competitiva en el mercado actual.\u003c/p\u003e\r\n\r\n\u003cp\u003eEn este curso aprenderás no solo como funcionan las herramientas de la ciencia de datos (Data Science) sino aplicarlas de forma sencilla con uno de los ambientes de análisis mayormente aceptados como lo es “R”. El lenguaje R es un lenguaje de programación comunmente usado.\u003c/p\u003e\r\n\r\n\u003cp\u003eEn este programa de Certificación Profesional desarrollarás las actividades necesarias para aplicar la ciencia de datos y también aprenderás a través de casos prácticos cómo aprovechar mejor estas herramientas para el proceso de toma de decisiones.\u003c/p\u003e\r\n\r\n\u003cp\u003eLa metodología de este programa está pensanda para que sin importar tu nivel de conocimiento en el tema puedas alcanzar el nivel necesario para el análisis de datos en cada uno de los cursos en línea que lo componen.\u003c/p\u003e\r\n \r\n\u003cp\u003eTodas las herramientas del programa, compuesto por 3 cursos online, son de uso libre y corren en todas las plataformas de cómputo.\u003c/p\u003e3f9:T7f2,\u003cp\u003eLa cantidad de datos valiosos que se generan día a día pueden ser una fuente enormemente confiable de factores para la toma de decisiones, pero la clave "])</script><script>self.__next_f.push([1,"está en saber recolectar los tipos de datos y presentarlos de manera relevante con palabras clave.\u003c/p\u003e\r\n\r\n\u003cp\u003eHoy en día existen cientos de lenguajes de programación para analizar datos, sin embargo entre los más utilizados y conocidos en el entorno de desarrollo se encuentra Python, por lo que se ha convertido en el lenguaje con mayor demanda en el mundo del desarrollo de software.\u003c/p\u003e\r\n\r\n\u003cp\u003e¿A qué se debe? Es un lenguaje de alto nivel pero muy sencillo, similar al lenguaje humano, flexible, amigable con android, iOs y windows, con un gran número de bibliotecas de procesamiento y de código abierto, lo que permite a los desarrolladores crear sin límites y de forma gratuita.\u003c/p\u003e\r\n\r\n\u003cp\u003eLa versatilidad de Python permite utilizarlo en análisis de datos, estructuras de datos y big data, data mining, data science, desarrollo web, desarrollo de aplicaciones, machine learning, ciberseguridad, juegos y gráficos 3D e incluso en Blockchain. De igual forma, la programación orientada a objetos con Python, permite utilizarlo posteriormente en lenguajes más modernos como C#, Java, Javascript, Kotlin, Php y Ruby.\u003c/p\u003e\r\n\r\n\u003cp\u003ePython, al ser uno de los lenguajes más utilizados en código fuente y diversos sistemas operativos, se ha convertido en una habilidad cada vez más demandada por los empleadores. En España, México y otros países, se encuentra dentro del reporte de tendencias de empleo y aptitudes en auge 2021 de LinkedIn.\u003c/p\u003e\r\n\r\nEste programa de certificación orientado al lenguaje de programación Python, te permitirá aprender los conceptos básicos del lenguaje para aprender a programar de cero, extraer información y visualizar los datos por medio de la implementación de dashboards con gráficas interactivas.\r\n\r\nTransforma tu vida profesional con el lenguaje de Python y obtén una ventaja competitiva, que te servirá hoy y en el futuro.3fa:Tab8,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cem\u003ePlease Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis course provides students with the basics required to create data visualizations and dashboards using both Microsoft Excel and IBM Cognos Analytics. You begin the process of telling a story about your data by creating several basic and advanced charts in Excel and learning how to add them to a digital dashboard. You then become familiar with IBM Cognos Analytics - a popular tool for data visualization and analytics – and learn how to use it to create interactive and informative dashboards. By completing this course, you will gain a basic understanding of using spreadsheets as a tool for data visualization. You will also gain the ability to create effective data visualizations, such as charts or graphs, and you will start to see how they can play a key role in the communication of your data analysis findings to interested parties. All of this can be accomplished without the need to write any code. By the end of this course you will be able to describe common dashboarding tools used by a data analyst, design and create a dashboard in a cloud platform, and begin to raise your level of confidence when creating intermediate level data visualizations.\u003c/p\u003e\n\u003cp\u003eThe emphasis is on applied learning and hands-on practice in this course, and with each hands-on lab, you will gain further experience in the creation of basic and advanced charts and the creation of digital dashboards using both Excel and Cognos Analytics. The final assignment project will allow you to apply these newly acquired skills to create and use data visualizations and add them to a digital dashboard to fulfil a business scenario.\u003c/p\u003e\n\u003cp\u003eThis course makes it simple to get started using Excel and Cognos Analytics to create data\u003cbr /\u003e\nvisualizations and dashboards to help tell a story about your data, and it does not require any previous data analysis or computer science\u003c/p\u003e\n\u003cp\u003eexperience. The course also does not require you to perform any software downloads or installations. All that is required is basic computer literacy, high-school level math, a device with a modern web browser, and the ability to use (or create) a Microsoft account to access Excel online at no-cost, and a basic understanding of Excel spreadsheets. Although the Excel hands-on labs steps are specifically related to using ‘Excel for the web’, if you already have the full desktop version of Excel, you should be able to use that to follow along quite easily with the labs.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3fb:T87e,"])</script><script>self.__next_f.push([1,"\u003cp\u003eGIS is a framework for gathering, managing, and analyzing data. Using the principles of geographic science, it integrates many types of geospatial data, analyzes their relationships, and organizes them in layers of information for visualization and improved understanding. This free online course focuses on ESRI’s ArcGIS software, the industry standard within GIS software. Jack Dangermond, CEO of ESRI, describes GIS as “uncovering meaning and insights from within data.”\u003c/p\u003e\n\u003cp\u003eParticipants in this course will learn GIS concepts and fundamentals, tools, and processing applicable to problems in a variety of fields, including cartography, remote sensing, data science, public health, health care, information technology, social science, urban planning, government, and business. Your enrollment in this course will prepare you to work with spatial data and conduct spatial data analysis with datasets and geographic data.\u003c/p\u003e\n\u003cp\u003eThis self-paced course is designed for self-study. Coursework covers GIS applications of real-world problems, and prepares learners for more advanced applications such as 3D GIS and GIS Image Analysis, the two other GIS courses in the GIS Essentials Professional Certificate program. This course can be taken individually as an elective or as part of this larger specialization.\u003c/p\u003e\n\u003cp\u003eGIS, once an esoteric technology limited to geographers and data scientists, is now available to everyone. Thousands of organizations in virtually every field are using GIS to analyze data, make maps that reveal patterns and relationships, share insights, and solve complex problems of local to global significance in the areas of sustainability, land use, watershed mapping, and more. The ability to conduct this kind of geospatial analysis and conduct web mapping is a skill growing in relevance to employers.\u003c/p\u003e\n\u003cp\u003eVerified track learners will receive one-year ArcGIS Pro Desktop (Windows) license with support only for tools used in the course in addition to unlimited course access and a verified certificate.\u003c/p\u003e\n\u003cp\u003eSign up for this online course or full GIS certificate today to expand your skillset in geographic information systems.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3fc:T12ef,"])</script><script>self.__next_f.push([1,"\u003cp\u003eWe will explain how to perform the standard processing and normalization steps, starting with raw data, to get to the point where one can investigate relevant biological questions. Throughout the case studies, we will make use of exploratory plots to get a general overview of the shape of the data and the result of the experiment. We start with RNA-seq data analysis covering basic concepts and a first look at FASTQ files. We will also go over quality control of FASTQ files; aligning RNA-seq reads; visualizing alignments and move on to analyzing RNA-seq at the gene-level : counting reads in genes; Exploratory Data Analysis and variance stabilization for counts; count-based differential expression; normalization and batch effects. Finally, we cover RNA-seq at the transcript-level : inferring expression of transcripts (i.e. alternative isoforms); differential exon usage. We will learn the basic steps in analyzing DNA methylation data, including reading the raw data, normalization, and finding regions of differential methylation across multiple samples. The course will end with a brief description of the basic steps for analyzing ChIP-seq datasets, from read alignment, to peak calling, and assessing differential binding patterns across multiple samples.\u003c/p\u003e\n\u003cp\u003eGiven the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.\u003c/p\u003e\n\u003cp\u003eThese courses make up two Professional Certificates and are self-paced:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis for Life Sciences:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistics-and-r\"\u003ePH525.1x: Statistics and R for the Life Sciences\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-linear-models-and-matrix-algebra\"\u003ePH525.2x: Introduction to Linear Models and Matrix Algebra\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistical-inference-and-modeling-for-high-throug\"\u003ePH525.3x: Statistical Inference and Modeling for High-throughput Experiments\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/high-dimensional-data-analysis\"\u003ePH525.4x: High-Dimensional Data Analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eGenomics Data Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-bioconductor-annotation-and-analys\"\u003ePH525.5x: Introduction to Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/case-studies-in-functional-genomics\"\u003ePH525.6x: Case Studies in Functional Genomics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/advanced-bioconductor\"\u003ePH525.7x: Advanced Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis class was supported in part by NIH grant R25GM114818.\u003c/p\u003e\n\u003cp\u003eHarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the \u003ca href=\"https://www.edx.org/edx-terms-service\" title=\"Follow link\"\u003eedX honor code\u003c/a\u003e, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.\u003c/p\u003e\n\u003cp\u003eHarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our \u003ca href=\"http://harvardx.harvard.edu/research-statement\" title=\"Follow link\"\u003eresearch statement \u003c/a\u003eto learn more.\u003c/p\u003e\n\u003cp\u003eHarvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact \u003ca href=\"mailto:harvardx@harvard.edu\"\u003eharvardx@harvard.edu\u003c/a\u003e and/or \u003ca href=\"https://www.edx.org/contact-us\" title=\"Follow link\"\u003ereport your experience through the edX contact form\u003c/a\u003e.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3fd:T1129,"])</script><script>self.__next_f.push([1,"\u003cp\u003eWe begin with an introduction to the relevant biology, explaining what we measure and why. Then we focus on the two main measurement technologies: next generation sequencing and microarrays. We then move on to describing how raw data and experimental information are imported into R and how we use Bioconductor classes to organize these data, whether generated locally, or harvested from public repositories or institutional archives. Genomic features are generally identified using intervals in genomic coordinates, and highly efficient algorithms for computing with genomic intervals will be examined in detail. Statistical methods for testing gene-centric or pathway-centric hypotheses with genome-scale data are found in packages such as limma, some of these techniques will be illustrated in lectures and labs.\u003c/p\u003e\n\u003cp\u003eGiven the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.\u003c/p\u003e\n\u003cp\u003eThese courses make up two Professional Certificates and are self-paced:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis for Life Sciences:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistics-and-r\"\u003ePH525.1x: Statistics and R for the Life Sciences\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-linear-models-and-matrix-algebra\"\u003ePH525.2x: Introduction to Linear Models and Matrix Algebra\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistical-inference-and-modeling-for-high-throug\"\u003ePH525.3x: Statistical Inference and Modeling for High-throughput Experiments\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/high-dimensional-data-analysis\"\u003ePH525.4x: High-Dimensional Data Analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eGenomics Data Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-bioconductor-annotation-and-analys\"\u003ePH525.5x: Introduction to Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/case-studies-in-functional-genomics\"\u003ePH525.6x: Case Studies in Functional Genomics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/advanced-bioconductor\"\u003ePH525.7x: Advanced Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis class was supported in part by NIH grant R25GM114818.\u003c/p\u003e\n\u003cp\u003eHarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the \u003ca href=\"https://www.edx.org/edx-terms-service\" title=\"Follow link\"\u003eedX honor code\u003c/a\u003e, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.\u003c/p\u003e\n\u003cp\u003eHarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our \u003ca href=\"http://harvardx.harvard.edu/research-statement\" title=\"Follow link\"\u003eresearch statement \u003c/a\u003eto learn more.\u003c/p\u003e\n\u003cp\u003eHarvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact \u003ca href=\"mailto:harvardx@harvard.edu\"\u003eharvardx@harvard.edu\u003c/a\u003e and/or \u003ca href=\"https://www.edx.org/contact-us\" title=\"Follow link\"\u003ereport your experience through the edX contact form\u003c/a\u003e.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3fe:T75f,\u003cp\u003eA strong foundation in mathematics is critical for success in all science and engineering disciplines. Whether you want to make a strong start to a master’s degree, prepare for more advanced courses, solidify your knowledge in a professional context or simply brush up on fundamentals, this course will get you up to speed.\u003c/p\u003e\n\u003cp\u003eProbability theory can be applied to learn more about real-life problems, and it is useful for building models. Moreover, it provides the basis for statistics and applications in data analysis. Therefore, it is a useful subject for any aspiring or practicing engineer.\u003c/p\u003e\n\u003cp\u003eWe will use some basic calculus, in particular (partial) differentiation and (multiple) integration. The focus will be on the interpretation rather than on the computation; so the required techniques will be low-level. If, however, you feel insecure about these topics, you can brush up on them in our calculus courses within this series.\u003c/p\u003e\n\u003cp\u003eThis course will offer you an overview of the probability theory elements common to most engineering bachelor programs. It will provide enough depth to cover the probability theory you need to succeed in your engineering master’s or profession in areas such as modeling, finance, signal processing, logistics and more.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis is a review course\u003c/strong\u003e\u003cbr /\u003e\nThis self-contained course is modular, so you do not need to follow the entire course if you wish to focus on a particular aspect. As a review course you are expected to have previously studied or be familiar with most of the material. Hence the pace will be higher than in an introductory course.\u003c/p\u003e\n\u003cp\u003eThis format is ideal for refreshing your bachelor level mathematics and letting you practice as much as you want. Through the Grasple platform, you will have access to plenty of exercises and receive intelligent, personal and immediate feedback.\u003c/p\u003e3ff:T505,\u003cp\u003e\u003cem\u003ePlease Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential"])</script><script>self.__next_f.push([1," that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\"A picture is worth a thousand words.\" We are all familiar with this expression. It especially applies when trying to explain the insights obtained from the analysis of increasingly large datasets. Data visualization plays an essential role in the representation of both small and large-scale data.\u003c/p\u003e\n\u003cp\u003eOne of the key skills of a data scientist is the ability to tell a compelling story, visualizing data and findings in an approachable and stimulating way.\u003c/p\u003e\n\u003cp\u003eIn this course, you will learn how to leverage a software tool to visualize data that will also enable you to extract information, better understand the data, and make more effective decisions.\u003c/p\u003e\n\u003cp\u003eWhen you sign up for this course, you get free access to \u003ca href=\"https://cocl.us/DS0101EN_DSX\"\u003eIBM Watson Studio\u003c/a\u003e. In Watson Studio, you’ll be able to start creating your own data science projects and collaborating with other data scientists. Start now and take advantage of everything this platform has to offer!\u003c/p\u003e400:T423,\u003cp\u003eWith the explosion of data collection enabled by the internet, mobile applications and transformation into the cloud, effective data analytics is turning into a critical tool in practically every domain – from academia to enterprise.\u003c/p\u003e\n\u003cp\u003eStart off with an overview of different types of data analysis techniques (descriptive, diagnostic, predictive and prescriptive) before diving deeper into descriptive analysis. Then, apply your knowledge with a guided project that makes use of a simple, but powerful dataset available by default in every AWS account: the logs from AWS CloudTrail. The CloudTrail service enables governance, compliance, operational auditing, and risk auditing of your AWS account. Through the project you’ll also get an introduction to Amazon Athena and Amazon QuickSight. Examples of common data analysis scenarios and benefits of doing analytics in the cloud will "])</script><script>self.__next_f.push([1,"be discussed. And, you’ll learn how to build a basic security dashboard as a simple but practical method of applying your newfound data analytics knowledge.\u003c/p\u003e401:T9dc,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThree innovations are driving the data revolution in medicine. \u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNext Generation Sequencing, and in particular, the ability to sequence individual genomes at diminishing costs.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eElectronic Medical Records, and our ability to mine, using machine learning techniques, huge datasets of medical records.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWearable devices, the Web, social networks and crowdsourcing - exemplifying the surprising capacity to collect medical data using non-conventional resources.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn order to take advantage of these technologies and participate in the revolution, physicians need a new toolbox that is generally lacking in the medical school curriculum. \u003c/p\u003e\n\u003cp\u003eThis course is a product of a decade of a collaborative effort between researchers from the computational biology program at Bar-Ilan University, and clinicians from Sheba Medical Center to develop and deliver an extended curriculum in genomics and biomedical informatics. The program has been endorsed by the Israeli Medicine Association and Ministry of Health. Here, we present a condensed online course that includes selected topics chosen from the extended program. \u003c/p\u003e\n\u003cp\u003eThis GaBI course on edX presents clinicians and digital health enthusiasts with an overview of the data revolution in medicine, and how to take advantage of it for research and in the clinic. In the scope of this single course, you will not become a bioinformatician, but you will be able to familiarize yourself with the main concepts, tools, algorithms, and databases used in this field, and understand the types of problems that these analysis techniques can help address. \u003c/p\u003e\n\u003cp\u003eThe syllabus covers the main topics of this discipline in a logical order: \u003c/p\u003e\n\u003cp\u003e● Methods used to obtain medical data (genotypic and phenotypic) \u003c/p\u003e\n\u003cp\u003e● Analysis of biological molecules such as DNA, RNA, and proteins using various computational tools from the field of bioinformatics \u003c/p\u003e\n\u003cp\u003e● Use of machine learning and artificial intelligence tools to mine the huge databases of medical information accumulating in Electronic Medical Records (EMRs), the Web, and numerous data science projects in medicine \u003c/p\u003e\n\u003cp\u003e● Analysis of complex interaction networks between DNA, RNA and protein molecules to gain a more holistic and systematic view of biological systems and medical conditions \u003c/p\u003e\n\u003cp\u003e● Practical applications in the clinic and in personalized medicine research, and the use of cutting edge technology to improve health\u003c/p\u003e"])</script><script>self.__next_f.push([1,"402:T6d8,\u003cp\u003eA strong foundation in mathematics is critical for success in all science and engineering disciplines. Whether you want to make a strong start to a master’s degree, prepare for more advanced courses, solidify your knowledge in a professional context or simply brush up on fundamentals, this course will get you up to speed.\u003c/p\u003e\n\u003cp\u003eIn many engineering master’s programs, statistics is used quite intensively. As soon as you are dealing with real-life data, you will need to get an idea of what these data tell you and how you can visualize this (descriptive statistics). But you will also want to perform some analysis (inferential statistics): you may want to build a model that mimics reality, estimate some quantities, or test some hypotheses.\u003c/p\u003e\n\u003cp\u003eThe statistics course in this series will help you refresh your knowledge on these topics. Along the way you will learn how to apply these concepts to datasets, using the statistical software R.\u003c/p\u003e\n\u003cp\u003eThis course offers enough depth to cover the statistics you need to succeed in your engineering master’s or profession in areas such as machine learning, data science and more.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis is a review course\u003c/strong\u003e\u003cbr /\u003e\nThis self-contained course is modular, so you do not need to follow the entire course if you wish to focus on a particular aspect. As a review course you are expected to have previously studied or be familiar with most of the material. Hence the pace will be higher than in an introductory course.\u003c/p\u003e\n\u003cp\u003eThis format is ideal for refreshing your bachelor level mathematics and letting you practice as much as you want. You will get many exercises, to be solved using Grasple or R, for which you will receive intelligent, personal and immediate feedback.\u003c/p\u003e403:T67f,\u003cp\u003eAnalytics have revolutionized sport, providing a competitive advantage to organizational decision-making both on and off the field. This course introduces best practices utilized by data analysts in sport business analytics. The course touches on sports data collection, fact"])</script><script>self.__next_f.push([1," finding, visualization, and metrics that guide strategic decision-making in the sport industry. This course will touch on many aspects of the sports industry, including professional sports. You do do not need a comprehensive background in data science, computer science/Python, or machine learning to complete this edX course.\u003c/p\u003e\n\u003cp\u003eThrough the duration of this self-paced, online course, learners will:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eIdentify the concepts and characteristics of sports analytics in the sporting industry, historically and today.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInterpret aspects of analytics in the sporting industry (e.g., the impact of analytics in sport, player data points, athlete performance data tracking, organizational key performance indicators, etc.)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eComprehend and engage in critical thinking with analytic topics in the sporting industry.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eObtain a perspective of the growing trend and field of sport analytics.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGain insight on the strategies, analytical techniques, and concepts used to evaluate players, team performance, and front-office strategies.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDiscuss topics related to sport analytics\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUtilize data analysis and statistical analysis techniques to identify problems and propose innovative solutions for improving performance both on the field and in sport management.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e404:T11a6,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIn this course, we begin with approaches to visualization of genome-scale data, and provide tools to build interactive graphical interfaces to speed discovery and interpretation. Using knitr and rmarkdown as basic authoring tools, the concept of reproducible research is developed, and the concept of an executable document is presented. In this framework reports are linked tightly to the underlying data and code, enhancing reproducibility and extensibility of completed analyses. We study out-of-memory approaches to the analysis of very large data resources, using relational databases or HDF5 as \"back ends\" with familiar R interfaces. Multiomic data integration is illustrated using a curated version of The Cancer Genome Atlas. Finally, we explore cloud-resident resources developed for the Encyclopedia of DNA Elements (the ENCODE project). These address transcription factor binding, ATAC-seq, and RNA-seq with CRISPR interference.\u003c/p\u003e\n\u003cp\u003eGiven the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.\u003c/p\u003e\n\u003cp\u003eThese courses make up two Professional Certificates and are self-paced:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis for Life Sciences:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistics-and-r\"\u003ePH525.1x: Statistics and R for the Life Sciences\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-linear-models-and-matrix-algebra\"\u003ePH525.2x: Introduction to Linear Models and Matrix Algebra\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistical-inference-and-modeling-for-high-throug\"\u003ePH525.3x: Statistical Inference and Modeling for High-throughput Experiments\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/high-dimensional-data-analysis\"\u003ePH525.4x: High-Dimensional Data Analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eGenomics Data Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-bioconductor-annotation-and-analys\"\u003ePH525.5x: Introduction to Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/case-studies-in-functional-genomics\"\u003ePH525.6x: Case Studies in Functional Genomics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/advanced-bioconductor\"\u003ePH525.7x: Advanced Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis class was supported in part by NIH grant R25GM114818.\u003c/p\u003e\n\u003cp\u003eHarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the \u003ca href=\"https://www.edx.org/edx-terms-service\" title=\"Follow link\"\u003eedX honor code\u003c/a\u003e, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.\u003c/p\u003e\n\u003cp\u003eHarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our \u003ca href=\"http://harvardx.harvard.edu/research-statement\" title=\"Follow link\"\u003eresearch statement \u003c/a\u003eto learn more.\u003c/p\u003e\n\u003cp\u003eHarvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact \u003ca href=\"mailto:harvardx@harvard.edu\"\u003eharvardx@harvard.edu\u003c/a\u003e and/or \u003ca href=\"https://www.edx.org/contact-us\" title=\"Follow link\"\u003ereport your experience through the edX contact form\u003c/a\u003e.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"405:T89e,"])</script><script>self.__next_f.push([1,"\u003cp\u003eRemote sensing observations from airborne and spaceborne platforms have become an essential tool in earth observation and earth sciences. They provide an immediate and large-area overview of the evolving earth environment, revealing important information on the state of ecosystems, unfolding natural hazards, enabling change detection of geodynamic phenomena such as volcanoes, earthquakes, and the cryosphere.\u003c/p\u003e\n\u003cp\u003eThis course will introduce you to Synthetic Aperture Radar (SAR), a remote sensing technology that can see the earth surface even during darkness and through weather conditions such as rain, clouds, or smoke. As a participant in this course, you will gain an intuitive understanding of the information contained in SAR observations. You will learn about the concepts and applications of interferometric SAR and experience how SAR data acquired at different polarizations can reveal a wealth of information about the earth environment. Each SAR analysis concept will be illustrated with relevant applications. Specific topics include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe mathematical and physical principles of SAR remote sensing\u003c/li\u003e\n\u003cli\u003eHow to access and visualize SAR data\u003c/li\u003e\n\u003cli\u003eInterpretation of SAR images at different wavelengths and polarizations\u003c/li\u003e\n\u003cli\u003eInterferometric SAR (InSAR) concepts\u003c/li\u003e\n\u003cli\u003eThe principles of Polarimetric SAR (PolSAR)\u003c/li\u003e\n\u003cli\u003eA summary of applications of InSAR and PolSAR in geoscience, hazard mapping, and ecosystem monitoring\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLearners on the verified track will put their learned knowledge into action in data analysis and data processing exercises, in which class participants will analyze SAR data sets, generate and interpret interferometric SAR data, and explore the importance of polarization in earth observation. Learners who select the verified track will also have access to online computational labs and tutorials using Jupyter notebooks that will allow deeper exploration and practice.\u003c/p\u003e\n\u003cp\u003eThis course is produced by the \u003ca href=\"https://asf.alaska.edu/\" rel=\"noopener\" target=\"_blank\"\u003eAlaska Satellite Facility\u003c/a\u003e at the University of Alaska Fairbanks, which has been selected as the NASA data hub for the upcoming NISAR mission.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"406:T42b,\u003cp\u003eMicroorganisms play a major role in the biosphere and within our bodies, but only a tiny fraction has been cultured so far. Microbiome data, that is the genetic information of microorganisms, is therefore an important window into the hidden microbial world.\u003c/p\u003e\n\u003cp\u003eMicrobiome data analysis elucidates the composition of microbial communities and how it changes in response to the environment. When analyzing sequencing data, we learn whether microbial diversity differs across conditions and identify links between microbes. In brief, microbiome data analysis gives us a first idea of how a microbial ecosystem works.\u003c/p\u003e\n\u003cp\u003eThis course will illustrate with the help of real-world example data how to carry out typical analysis tasks, such as comparing microbial composition and diversity, clustering samples and computing associations. If you plan to work with microbiome data, this course will get you up to speed. \u003c/p\u003e\n\u003cp\u003eThe instructors are experienced bioinformaticians who are internationally known for their analysis of large-scale microbiome data sets.\u003c/p\u003e407:T53d,\u003cp\u003eIn this course, you will learn about the Grammar of Graphics, a system for describing and building graphs, and how the ggplot2 data visualization package for R applies this concept to basic bar charts, histograms, pie charts, scatter plots, line plots, and box plots. You will also learn how to further customize your charts and plots using themes and other techniques. You will then learn how to use another R data visualization package called Leaflet to create map plots, a unique way to plot data based on geolocation data. Finally, you will learn about creating interactive dashboards using the R Shiny package. You will learn how to create and customize Shiny apps, alter the appearance of the apps by adding HTML and image components, and deploy your interactive data apps on the web.\u003c/p\u003e\n\u003cp\u003eYou will practice what you learn and build hands-on experience by completing labs in each module and a final project at the end of the course. Enroll in the cours"])</script><script>self.__next_f.push([1,"e, watch the videos, work through the labs, and then watch your data science skills grow!\u003c/p\u003e\n\u003cp\u003eNOTE: This course requires knowledge of working with R and data. If you do not have these skills, it is highly recommended that you first take the Introduction to R Programming for Data Science as well as the Data Analysis with R courses from IBM prior to starting this course.\u003c/p\u003e408:T68f,\u003cp\u003eEvery modern organization is a digital organization or will rapidly become digital. Artificial intelligence, Google/Amazon/Facebook/Uber, and big data have dramatically raised customer expectations and demand. \u003c/p\u003e\n\u003cp\u003eOrganizations that are effective in using data will win in the economies of the mid-21st century. These must-have core competencies include data analysis, machine learning, data visualizations, data mining, and predictive analytics, and deep learning. Organizations that won't or can't digitally transform will go the way of Blockbuster or Border's Bookstore. \u003c/p\u003e\n\u003cp\u003eThe organization that better harnesses the power of data to create a superior customer experience will thrive in the new business realities.\u003c/p\u003e\n\u003cp\u003eThe question is, how does an organization digitally transform? There are many digital technologies for organizations to choose from - too many choices! And digital technologies are only part of creating a digital organization. The employees must be trained in the new technologies, leaders must learn how to use data in making strategic decisions, and the organization's business processes must be reinvented. So many choices to make and the stakes have never been higher!\u003c/p\u003e\n\u003cp\u003eThis course will give you a framework to help you successfully navigate the challenges posed by digital transformation. First, we will discuss how to use the organization's dynamic capabilities to start the digital transformation. Second, we will use fitness landscapes to build a competitive digital business model. Finally, we will implement a strategic foresight function to help evolve the digital business model for the organization's cont"])</script><script>self.__next_f.push([1,"inued success.\u003c/p\u003e409:T403,\u003cp\u003eQuantum computing is a fast-growing technology and semiconductor chips are one of the most promising platforms for quantum devices.\u003cbr /\u003e\nThe current bottleneck for scaling is the ability to control semiconductor computing chips quickly and efficiently. \u003c/p\u003e\n\u003cp\u003eThis course, aimed at students with experience equivalent to a master’s degree in physics, computer science or electrical engineering introduces hands-on machine learning examples for the application of machine learning in the field of semiconductor quantum devices. Examples include coarse tuning into the correct quantum dot regime, specific charge state tuning, fine tuning and unsupervised quantum dot data analysis. \u003c/p\u003e\n\u003cp\u003eAfter the completion of the course students will be able to\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eassess the suitability of machine learning for specific qubit tuning or control task and\u003c/li\u003e\n\u003cli\u003eimplement a machine learning prototype that is ready to be embedded into their experimental or theoretical quantum research and engineering workflow.\u003c/li\u003e\n\u003c/ol\u003e40a:T853,"])</script><script>self.__next_f.push([1,"\u003cp\u003eWhat makes a good business decision? \u003c/p\u003e\n\u003cp\u003eHow can we combine effective data analytics and feed robust foresight and scenario planning processes? \u003c/p\u003e\n\u003cp\u003eWe need to rethink the organization, and see it as essentially a “decision factory.” Like a factory, employees at all levels make or contribute to decisions that, taken together, gives the organization the competitive edge in the marketplace. The news media is filled with stories of how a minor decision has major ramifications on the organization. In this course, we will learn how to train organizational members to effectively data products in their business decisions.\u003c/p\u003e\n\u003cp\u003eDigital organizations capture an enormous amount of data. Knowing how to mine and refine that data for strategic decision making effectively is what will separate the winners from the losers. As the business guru, Dr. Roger L. Martin, wrote in a 2013 Harvard Business Review article, knowledge workers turn the \"raw material\" of data into decisions. Decision-makers need the best data to make the best decisions.\u003c/p\u003e\n\u003cp\u003eThis course will help your organization inventory the decisions its customers, employees, and leaders make and their data needs. We will discuss how to make good decisions and build quality data creation processes. You will also learn how to work with incomplete or ambiguous data and how to learn effectively from experience.\u003c/p\u003e\n\u003cp\u003eWe will close out the course by examining two recent trends in data analytics. The first trend is the use of low-code/no-code tools by non-technical employees to create data applications. We will discuss best practices for creating low-code/no-code applications while providing a robust data infrastructure for the apps.\u003c/p\u003e\n\u003cp\u003eThe second trend is the use of artificial intelligence (A.I.) and robotic process automation (RPA) in data analytics. We will examine the use of these tools, along with two of the most popular advanced data analysis tools: R and Microsoft's Power Platform. \u003c/p\u003e\n\u003cp\u003eThis course is a high-level view of topics that we will explore in greater depth in the Architect certification portion of this program.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"40b:T50c,\u003cp\u003e\u003cspan lang=\"EN\"\u003eAdvanced\u003c/span\u003e Bayesian Data Analysis Using R is part two of the Bayesian Data Analysis in R professional certificate.\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis course is directed at people who are already familiar with the fundamentals of Bayesian inference. It explores further the concepts, methods, and algorithms introduced in the part one (Introductory Bayesian Data Analysis Using R).\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe course places mixed effects regression models useful for experiments with repeated measures or additional hierarchy often encountered in biostatistics, ecology and health sciences among others within the Bayesian context. It takes a closer look at the Markov Chain Monte Carlo (MCMC) algorithms, why they work and how to implement them in the R programming language. Convergence assessment and visualisation of the results are discussed in some detail. The course also explores Bayesian model averaging, often used in machine learning, all within the context of practical examples. \u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eFinally, we discuss different kinds of missing data, and the Bayesian methods of dealing with such situations.\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003ePrior facility in basic algebra and calculus as well as programming in R is highly recommended.\u003c/p\u003e40c:T526,\u003cp\u003eThose enrolled in Probability for Actuaries: Introduction to Discrete Distributions will learn to:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDescribe basic data types\u003c/li\u003e\n\u003cli\u003eDescribe the central tendency measures of datasets: mean, median and mode\u003c/li\u003e\n\u003cli\u003eDescribe the dispersion measures of datasets: range, percentiles and variance\u003c/li\u003e\n\u003cli\u003eDescribe basic probability concepts including sample space, events and set operations\u003c/li\u003e\n\u003cli\u003eCalculate probabilities for simple discrete events\u003c/li\u003e\n\u003cli\u003eDifferentiate between a discrete and continuous random variable\u003c/li\u003e\n\u003cli\u003eDescribe Bayes Theorem, conditional probability, law of total probability and statistical independence\u003c/li\u003e\n\u003cli\u003eDescribe and use a probability mass function, probability density function and"])</script><script>self.__next_f.push([1," cumulative distribution function\u003c/li\u003e\n\u003cli\u003eDescribe and calculate the mathematical expectation of a random variable\u003c/li\u003e\n\u003cli\u003eDescribe the features and application of the following discrete distributions: Uniform, Binomial, Poisson\u003c/li\u003e\n\u003cli\u003eCalculate probabilities for random variables governed by a Uniform, Binomial or Poisson distribution\u003c/li\u003e\n\u003cli\u003eDescribe the features and application of the following discrete distributions: Geometric, Negative Binomial\u003c/li\u003e\n\u003cli\u003eCalculate probabilities for random variables governed by a Geometric or Negative Binomial distribution\u003c/li\u003e\n\u003c/ul\u003e40d:T99b,"])</script><script>self.__next_f.push([1,"\u003cp\u003eEngineers in the automotive industry are required to understand basic safety concepts. With increasing worldwide efforts to develop connected and self-driving vehicles, traffic safety is facing huge new challenges. This course is for students or professionals who have a bachelor's degree in mechanical engineering or similar and who are interested in a future in the vehicle industry or in road design and traffic engineering. It's also of value for people already working in these areas who wantbetter insight into safety issues. \u003c/p\u003e\n\u003cp\u003eThis course teaches the fundamentals of active safety (systems for avoiding crashes or reducing crash consequences) as well as passive safety (systems for avoiding or reducing injuries). Key concepts include in-crash protective systems, collision avoidance, and safe automated driving. The course will introduce scientific and engineering methodologies that are used in the development and assessment of traffic safety and vehicle safety. This includes methods to study the different components of real-world traffic systems with the goal to identify and understand safety problems and hazards. It includes methods to investigate the attitudes and behavior of drivers and other road users as well as recent solutions to improve active safety. Italso includes methods to study human body tolerance to impact and solutions to minimize the injury risk in crashes. \u003c/p\u003e\n\u003cp\u003eStudy topics include crash data analysis and in-situ observational studies of drivers and other road users by the use of instrumented vehicles and roadside camera systems. Solutions in active safety, such as driver alertness monitoring, driver information as well as collision avoidance and collision mitigation systems, will be described. Examples of in-crash protective systems are combinations of traditional restraints such as seat belts and airbags but with advanced functions such as automatic adaption to the individual occupant as well as pre-collision activation based on advanced integrated sensor systems and communication systems. \u003c/p\u003e\n\u003cp\u003eThe course will be based on recorded lectures that use videos and animations to enhance the experience. Online tutorials that access simulation models will give the participants an experience of influencing parameters in active safety and passive safety systems. \u003c/p\u003e\n\u003cp\u003eAs a result of support from MathWorks, students will be granted access to MATLAB/Simulink for the duration of the course.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"40e:T53c,\u003cp\u003eAI continues to contribute to progress against leading causes of disease and death whether through sharing data and information about clinical trials in real-time or using AI to develop new insights into the diagnosis and treatment of diseases. Specific applications include improving patient care through machine learning and data analysis, fast and accurate diagnosis, precision in treatment planning, medical imaging, and patient data analysis. \u003c/p\u003e\n\u003cp\u003eProfessionals who work in non-technical roles in healthcare also need foundational knowledge and practical insights to help them take advantage of AI. Objectives here range from the safe and ethical application of AI in clinical settings to AI applications in hospital management. This MOOC is a quick start to the applications of AI for this class of professionals, focusing entirely on deep learning, particularly on smart and AI-based automation in the healthcare sector. It aims to propagate ideas about how to proactively engage with AI in the healthcare domain.\u003c/p\u003e\n\u003cp\u003eThe course will help participants bridge the gap between healthcare and technology. Participants will possess the knowledge and confidence to engage with AI projects, advocate for responsible AI adoption, and identify opportunities to leverage AI for better patient outcomes and operational efficiency.\u003c/p\u003e40f:T5e4,\u003cp\u003eIn this course, experts will discuss the options a researcher must consider when embarking on clinical research. What research design should I choose? How do I start the process of getting my research approved? How will I analyze the data I collect? These are all important questions that a researcher faces. \u003c/p\u003e\n\u003cp\u003eWe will discuss the key decisions a researcher needs to make when preparing for and conducting research, as well as tools for data analysis. You will learn what a pragmatic clinical trial is and how to calculate power and sample size for your study. You will also be exposed to more complex study designs sometimes used in pragmatic clinical trials, such as Bayesian and"])</script><script>self.__next_f.push([1," adaptive designs. \u003c/p\u003e\n\u003cp\u003eThis course includes the following 11 lectures: \u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eOverview of Design Options for Pragmatic Clinical Trials\u003c/li\u003e\n\u003cli\u003eOutcome Measures in Clinical Trials\u003c/li\u003e\n\u003cli\u003eNon-inferiority Trials\u003c/li\u003e\n\u003cli\u003eBasic Analytic Methods\u003c/li\u003e\n\u003cli\u003eBasic Power and Sample Size Calculations\u003c/li\u003e\n\u003cli\u003eSMART: Adaptive Treatment Strategies\u003c/li\u003e\n\u003cli\u003eIntroduction to Bayesian Methods\u003c/li\u003e\n\u003cli\u003eBayesian Designs\u003c/li\u003e\n\u003cli\u003eQuasi-Experiment in Health Services Research\u003c/li\u003e\n\u003cli\u003eAdaptive Trial Design\u003c/li\u003e\n\u003cli\u003eLogistics of Clinical Trials\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis course is intended for anyone interested in comparative effectiveness research (CER) and patient-centered outcomes research (PCOR) methods. \u003c/p\u003e\n\u003cp\u003eThis course is supported by grant number R25HS023214 from the Agency for Healthcare Research and Quality.\u003c/p\u003e410:Td45,"])</script><script>self.__next_f.push([1,"\u003cp\u003eWant to learn how to identify and solve every day ethical issues in engineering, science and Artificial Intelligence (AI)? If yes, this is the course for you! Ethics plays an integral role when it comes to engineering and science practice and recently is impacted by AI and big data analysis. This course originally released in 2017 teaches traditional preventive engineering ethics but emphasizes aspirational ethics. A new module was added that covers the topics of AI and Data ethics, which engineers and scientists also need to understand.\u003c/p\u003e\n\u003cp\u003eThe learning objectives of this course are as follows:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003erecognize the significant social and environmental impact of engineering/scientific solutions.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eapply a practical seven-step ethical guide to real-world cases.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ecritique, analyze, and develop best ethical solutions across micro- to meta- levels toward real-world problems.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eunderstand how one behaves in an organization professionally as an ethical engineer.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003elearn how to apply ethical principles on advanced technologies like artificial intelligence.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eunderstand different AI related ethical guidelines and how to apply them.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003egrasp how AI related ethical \u0026amp; technical standards are influenced by bias, trade-offs and norms\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe first six units cover science and engineering topics and the lectures are given in Japanese and dubbed in English. Slides, quizzes and transcripts are available both in English and Japanese. In unit 7 on AI \u0026amp; Data ethics, the lectures and all materials are in English with closed captions in English and Japanese.\u003c/p\u003e\n\u003cp\u003e本コースは、工学、科学、AI（人工知能）分野で日々起こる倫理的問題を捉え、解決する方法を学びたいと考えている方に最適のコースです。倫理は、工学と科学を実践する際に不可欠な役割を果たし、最近では、AIとビッグデータ分析の影響も受けています。 2017年にリリースされた本コースの前身となるコースでは、工学分野の伝統的な「予防倫理」および「志向倫理」を取扱ってきましたが、今回の改訂により、すべての技術者および科学者に必要なAIとデータ倫理を取扱う新しいモジュールが追加されました。\u003c/p\u003e\n\u003cp\u003e本コースの学習目標は以下のとおりです。\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e工学的・科学的解決策が社会・環境に与える影響の大きさを認識する。\u003c/li\u003e\n\u003cli\u003e事例研究を通して実践的な倫理手法であるセブン・ステップ・ガイドを応用する。\u003c/li\u003e\n\u003cli\u003e現実世界の問題に対し、マクロからメタレベルにかけて倫理的に最も良い解決策を批評、分析、発展させる。\u003c/li\u003e\n\u003cli\u003e事例研究を通じて、倫理的な技術者として専門的な組織内でどのように個人がふるまうかを理解する。\u003c/li\u003e\n\u003cli\u003eAIのような発展中の技術に対し、どう倫理原則を適用するかを理解する。\u003c/li\u003e\n\u003cli\u003eAIに関連する様々な倫理ガイドラインとその適用方法を理解する。\u003c/li\u003e\n\u003cli\u003eAI関連の倫理的および技術的な基準が偏見、トレードオフの関係、規範等によってどのような影響を受けるのかと把握する。\u003c/li\u003e\n\u003c/ol\u003e"])</script><script>self.__next_f.push([1,"411:T62d,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eTraining in hypothesis testing offers several benefits that can enhance your analytical and decision-making skills, whether you work in manufacturing, business, healthcare, research, or any field that involves data analysis. 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In this course, you will learn to identify the characteristics of obs"])</script><script>self.__next_f.push([1,"ervational studies, to interpret the results of observational studies, and to describe the use of health registries in comparative effectiveness research (CER). \u003c/p\u003e\n\u003cp\u003eThis course includes the following 11 lectures: \u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eOverview of Using Observational Data in Comparative Effectiveness Research (CER)\u003c/li\u003e\n\u003cli\u003eCancer Registries and Data Linkage\u003c/li\u003e\n\u003cli\u003eSEER-Medicare and Other Data Sources\u003c/li\u003e\n\u003cli\u003eOverview of Analytic Methods I\u003c/li\u003e\n\u003cli\u003eOverview of Analytic Methods II\u003c/li\u003e\n\u003cli\u003eLongitudinal Data Analysis\u003c/li\u003e\n\u003cli\u003eAdvanced Methods in CER I\u003c/li\u003e\n\u003cli\u003eAdvanced Methods in CER II\u003c/li\u003e\n\u003cli\u003eSurvival Analysis\u003c/li\u003e\n\u003cli\u003eAnalysis of Medical Cost Data in Observational Studies\u003c/li\u003e\n\u003cli\u003eHealthcare Policy Research\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis course is intended for anyone interested in comparative effectiveness research (CER) and patient-centered outcomes research (PCOR) methods. \u003c/p\u003e\n\u003cp\u003eThis course is supported by grant number R25HS023214 from the Agency for Healthcare Research and Quality.\u003c/p\u003e413:T4b5,\u003cp\u003eIn this course, you will develop a working knowledge of linear relationship data in healthcare and practice using R statistical programming to analyze this data. 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It’s the lens you need to bring data-led decision-making into focus.\u003c/p\u003e\n\u003cp\u003e‍Learn what data analytics is, identify the different types (descriptive, diagnostic, predictive, and prescriptive), and understand the business case for implementing it in your organization. Realize what it means to be data-led and gain essential data literacy skills to drive your decision-making through data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLearn from data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIdentify key components of the data analytics ecosystem and understand how data connects - well, everything.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlan with data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEffectively diagnose the pits and peaks of your workflows, and how to optimize them through data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThrive through data\u003c/strong\u003e\u003cbr /\u003e\nCome away equipped with a step-by-step process of how to create and apply a data-driven business strategy.\u003c/p\u003e415:T4ab,\u003cp\u003eBy the end of the course, you will be able to...\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMake data work for you. Understand the challenges and opportunities of an effective data analytics ecosystem in businesses of all sizes.\u003c/li\u003e\n\u003cli\u003eBecome data literate. Identify the purpose and value of the different types of data analytics, including their principles, benefits and challenges.\u003c/li\u003e\n\u003cli\u003eGain a competitive edge. Identify the factors involved in evaluating data analytics projects, and learn how to use data to gain competitive advantage.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eExpert knowledge\u003cbr /\u003e\n\u003c/strong\u003e Interviews with world-renowned data experts help you understand how to use data analytics to make decisions and improve your business. Digest relevant statistics and facts that widen your understanding of the topic and industry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e"])</script><script>self.__next_f.push([1,"‍Visual learning\u003cbr /\u003e\n\u003c/strong\u003e A visual step-by-step approach walks you through the entire process of becoming data-led. Beautiful infographics and engaging gifs explain the most complicated data analytics topics and theories, guiding you through the learning journey.\u003c/p\u003e\n\u003cp\u003eCreated by BoxPlay, an Emmy award-winning company on a mission to democratize career success.\u003c/p\u003e416:T79a,\u003cp\u003eThere are underlying fundamental principles and concepts that apply to all supply chains, which can be expressed in relatively straightforward models. However, to actually implement them across a real supply chain requires the use of technology across multiple systems. Supply chains have a long history of using technology to improve efficiency and effectiveness. The shear scale and scope of most supply chains require many distinct systems to interact with each other.\u003c/p\u003e\n\u003cp\u003eUnfortunately, technology is a moving target. It is constantly evolving and improving so that today's technology is outdated within a few years or months. Rather than focusing on a specific software system, this business and management course will focus on three aspects: fundamental concepts, core systems, and data analysis.\u003c/p\u003e\n\u003cp\u003eWe will start with the introduction of fundamental concepts that are used in all software tools. We will cover IT fundamentals, including project management and software processes, data modeling, UML, relational databases and SQL. We will also introduce Internet technologies, such as XML, web services, and service-oriented architectures. No prior programming experience required.\u003c/p\u003e\n\u003cp\u003eWe will then provide an overview of the main types of supply chain software including ERP, WMS, and TMS systems. We will describe their main functionality, how they work, how they are used, their architecture, data flows, and how they are organized into modules. We will also cover the software selection process and how software upgrade and implementation projects should be organized and managed.\u003c/p\u003e\n\u003cp\u003eFinally, we will dive into data analysis that is cor"])</script><script>self.__next_f.push([1,"e to all large supply chains. We will introduce visualization and big data analysis techniques that are used in practice today.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis is an open enrollment course\u003c/strong\u003e , making it accessible for almost anyone, anywhere in the world to enroll and learn for free.\u003c/p\u003e417:Tab8,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eThis course includes the ISCEA Certified Forecaster and Demand Planner (CFDP) Exam. Upon successful completion of the exam course, you will earn both an edX course completion certificate, as well as the ISCEA Certified Forecaster and Demand Planner Certification. A passing rate of 70% on the CFDP Exam is indicative of successful completion.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eBefore you can take this exam, you are required to complete the three preparatory courses listed in the Certified Forecaster and Demand Planner (CFDP) program. This program will provide you with the skills and knowledge you need to be successful in the CFDP Exam. \u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eLearners are not permitted to take the CFDP Exam course without completing all three of the preparatory courses:\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eCFDP01: Improving Supply Chain Performance through Demand Planning. \u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eKnowledge assessed: Supply chain management essentials, Inventory planning fundamentals, Bullwhip effect, Introduction to sales and operations planning / integrated business planning - S\u0026amp;OP / IBP, Consensus, prioritization and integration with strategic planning, and Collaborative Planning, Forecasting and Replenishment - CPFR.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eCFDP02: Forecasting Techniques for Slow and Rapidly Changing Demand.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eKnowledge assessed: Forecasting myths, realities and challenges, Forecasting based on historical data, Seasonal demand forecasting, Intermittent demand forecasting, Judgmental and causal forecasting models, improving forecasting with Machine Learning, New product forecasting, forecast performance, impact of randomness and disruptive events in forecasts, and Improving forecasting with Lean and Six Sigma Principles.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eCFDP03: Demand Management in a Demand Driven Supply Chain.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eKnowledge assessed: Demand driven supply chain framework, Demand sensing, improving forecasting with POS data and demand sensing, Principles of data analysis, Supply chain response, Introduction to Demand Driven Materials Requirement Planning– DDMRP\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eSign up for this fully online exam and become an ISCEA Certified Forecaster and Demand Planner - CFDP!\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eISCEA, the International Supply Chain Education Alliance was the first organization on the globe certifying Supply Chain Managers - CSCM, Supply Chain Analysts - CSCA and Demand-Driven Planners - CDDP, and it remains the worldwide authoritative resource for Supply Chain career validation with thousands of certificate holders commanding top-tier salaries.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"418:T552,\u003cp\u003ePython is one of the most popular and in-demand programming languages in the world — largely because of how readable and versatile it is. If you’re interested in learning Python, this free, introductory course will demonstrate how learning to code in Python could benefit your career. No previous programming experience is required.\u003c/p\u003e\n\u003cp\u003eFrom analyzing large datasets to building web applications, Python can be used for a variety of projects including:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eWriting scripts for automating tasks\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWeb development\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCollecting data from websites (also known as “web scraping”)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eScientific and numeric computing\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eData analysis\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eData visualization\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMachine learning\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePython is also a useful skill applicable to roles across a wide range of industries, including:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eEnergy\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFinance\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHealthcare\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMarketing\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eIT\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRetail\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis course is an introduction to our Boot Camps, which combine data analysis and machine learning to prepare learners for careers such as data analysts, financial analysts, data scientists, and more. Learn more \u003ca href=\"https://www.edx.org/boot-camps?linked_from=sitenav\"\u003ehere\u003c/a\u003e.\u003c/p\u003e419:T699,\u003cp\u003eLearn data literacy online using R programming\u003c/p\u003e\n\u003cp\u003eWhat is data literacy and why is it important? In this data literacy course, you will learn how to become data literate. This will be accomplished by performing data analysis, data visualization, and communicating with data, using real datasets and examples that are relevant to a variety of audiences and academic disciplines. Data is part of every field, but not everyone has had the opportunity to gain the skills necessary to find the data they need and use it in ways that add to their work. Whether you are in public health, healthcare, banking, law, education, graduate school, or a variety of other fiel"])</script><script>self.__next_f.push([1,"ds, there is a way to understand and make use of related data. \u003c/p\u003e\n\u003cp\u003eEarn your data literacy certificate online\u003c/p\u003e\n\u003cp\u003eThis free four-week course will give you the opportunity to build and leverage your data skills for upward mobility at any stage in your career. It will take you through the six steps of the data lifecycle, using different case studies and contexts, and teach you how to analyze, manage, and communicate data, working in R to achieve basic R programming competencies. R is a statistical programming language that is a great resource to analyze data, manage data, and visualize data.\u003c/p\u003e\n\u003cp\u003eNo experience is required to learn this in-demand skill. By the end of this data literacy training course, you will be able to identify key principles of data analysis, use critical thinking skills, and become proficient in building powerful visuals. If you are interested in building a career in data analytics, first learning these foundational lessons is vital.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePhoto by NASA on Unsplash\u003c/em\u003e\u003c/p\u003e41a:T430,\u003cp\u003eThis course provides an introduction to data analytics for individuals with no prior knowledge of data science or machine learning. The course starts with an extensive review of probability theory as the language of uncertainty, discusses Monte Carlo sampling for uncertainty propagation, covers the basics of supervised (Bayesian generalized linear regression, logistic regression, Gaussian processes, deep neural networks, convolutional neural networks), unsupervised learning (k-means clustering, principal component analysis, Gaussian mixtures) and state space models (Kalman filters). The course also reviews the state-of-the-art in physics-informed deep learning and ends with a discussion of automated Bayesian inference using probabilistic programming (Markov chain Monte Carlo, sequential Monte Carlo, and variational inference). Throughout the course, the instructor follows a probabilistic perspective that highlights the first principles behind the presented methods with the ultimate goal of teaching t"])</script><script>self.__next_f.push([1,"he student how to create and fit their own models.\u003c/p\u003e41b:T643,\u003cp\u003eProbability and inference are used everywhere. For example, they help us figure out which of your emails are spam, what results to show you when you search on Google, how a self-driving car should navigate its environment, or even how a computer can beat the best Jeopardy and Go players! What do all of these examples have in common? They are all situations in which a computer program can carry out inferences in the face of uncertainty at a speed and accuracy that far exceed what we could do in our heads or on a piece of paper.\u003c/p\u003e\n\u003cp\u003eIn this data analysis and computer programming course, you will learn the principles of probability and inference. We will put these mathematical concepts to work in code that solves problems people care about. You will learn about different data structures for storing probability distributions, such as probabilistic graphical models, and build efficient algorithms for reasoning with these data structures.\u003c/p\u003e\n\u003cp\u003eBy the end of this course, you will know how to model real-world problems with probability, and how to use the resulting models for inference.\u003c/p\u003e\n\u003cp\u003eYou don’t need to have prior experience in either probability or inference, but you should be comfortable with basic Python programming and calculus.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e“I love that you can do so much with the material, from programming a robot to move in an unfamiliar environment, to segmenting foreground/background of an image, to classifying tweets on Twitter—all homework examples taken from the class!”\u003c/em\u003e – Previous Student in the residential version of this new online course.\u003c/p\u003e41c:T455,This course is an introduction to metabolomics principles and their applications in various fields of life sciences. \u003cbr /\u003e\u003cbr /\u003eWe will provide a summary of all steps in metabolomics research; from experimental design, sample preparation, analytical procedures, to data analysis. The course also provides case studies of various kinds of research samples to attract studen"])</script><script>self.__next_f.push([1,"ts that are not familiar with metabolomics, providing them enough explanation to utilize metabolomics technology for their respective research fields. \u003cbr /\u003e\u003cbr /\u003eSeveral examples of metabolomics applications will\u0026nbsp;be introduced throughout the lectures. These include examples within food science and technology, metabolic engineering, basic biology, introduction to imaging\u0026nbsp;mass spectrometry, and application in medical science.\u003cbr /\u003e\u003cbr /\u003eNo previous knowledge on metabolomics is needed but we recommend that students have an\u0026nbsp;undergraduate-level understanding of Biochemistry, Analytical Chemistry, and\u0026nbsp;Biostatistics, and that they learn about basic principles of multivariable analysis prior to taking this course.41d:T429,\u003cp\u003eWith the continuous generation of massive amounts of biomedical data on a daily basis, whether from research laboratories or clinical labs, we need to improve our ability to understand and analyze the data in order to take full advantage of its power in scientific discoveries and patient care. For non-bioinformaticians, “handling” big data remains a daunting task. This course was designed to facilitate the understanding, analysis, and interpretation of biomedical big data to those in the biomedical field with limited or no significant experience in bioinformatics. The goal of this course is to “demystify” the process of analyzing biomedical big data through a series of lectures and online hands-on training sessions and demos. You will learn how to use publicly available online resources and tools for genomic, transcriptomic, and proteomic data analysis, as well as other analytic tools and online resources. This course is funded by a research grant from the US National Institutes of Health (NIH)-Big Data to Knowledge (BD2K) Initiative.\u003c/p\u003e41e:Tadb,"])</script><script>self.__next_f.push([1,"\u003cp\u003eAre you someone who likes to “tinker with technology” but don’t know anything about writing code or programming languages? This class will satisfy that excitement by diving headfirst into a number of well-known Low Code/No Code platforms and their no-code tools without the perquisite of coding skills. Showing you the fundamentals to get you interested to see more. \u003c/p\u003e\n\u003cp\u003eWe will cover exciting topics like data modeling best practices, creating a solid and consistent user experience and building automations and workflows your users will love. We will also touch on some of the LCNC strategies in support of integrations (leveraging open APIs) and discuss strategies for managing data sources. \u003c/p\u003e\n\u003cp\u003eIn the first course (Platform Product Essentials) of this certificate, we discussed some of the history behind LCNC platforms and the roles of the key players that support your success when building applications. \u003c/p\u003e\n\u003cp\u003eThis class is all about app development. Students will be asked to dive into a LCNC tool and define and build a LCNC app using the methodologies provided to create no-code solutions. For this class we will dive into the functionality of your own app. You can come out of this course calling yourself an app builder or, even better, a citizen developer! \u003c/p\u003e\n\u003cp\u003eAs part of this class, we will be assigned a common use case and build an app front/dashboard. Here we will focus on the front-end and back-end perspective through low-code app development and different no-code softwares. These are intentionally designed so that non-programmers, who have never written a single line of code, can still develop.\u003c/p\u003e\n\u003cp\u003eThis course will consist of the following lectures:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGetting to know your application Part1. What are the core tenants of your selected platform and how do they work? This class will focus on using drag and drop tools to start your development process. \u003c/li\u003e\n\u003cli\u003eGetting to know your application Part 2. How far can you go with customization when building apps? How far can you go until it breaks? What happens when it does? Here we will look at leveraging layout templates for your apps and how to enhance your web application so business users will love them. We will also look into some custom code elements that can be used in your digital transformation.\u003c/li\u003e\n\u003cli\u003eData Sources. What is your data and where does it come from?\u003c/li\u003e\n\u003cli\u003eData Modeling. Ensuring consistency in your data from these code applications.\u003c/li\u003e\n\u003cli\u003eUser Experience. If your user needs instructions, you are doing something wrong.\u003c/li\u003e\n\u003cli\u003eSystem Testing. How to ensure your application is of the highest quality. Is your user interface up to par with this low-code development?\u003c/li\u003e\n\u003cli\u003eCode promotion - and what does that mean?\u003c/li\u003e\n\u003c/ul\u003e"])</script><script>self.__next_f.push([1,"41f:T7f9,\u003cp\u003eIn the past few decades, China's cities have experienced a period of rapid development. Great changes have taken place in both urban space and urban life. With the booming of information and communications technology (ICT), ‘Big data’ such as mobile phone signaling, public transportation smart card records and ‘open data’ from commercial websites and government websites jointly promote the formation of the ‘new data environment’, thus providing a novel perspective for a better understanding of what changes have happened or are happening in China’s cities. \u003c/p\u003e\n\u003cp\u003eThis course combines both the new data generated for urban analysis and its research applications. The content ranges from big data acquisition, analysis, visualization and applications in the context of China’s urbanization and its city planning, to urban modeling methods and typical models, as well as the emerging trend and potential revolution of big data in urban planning. \u003c/p\u003e\n\u003cp\u003eWe have categorized the overall content of this online course into five sections, namely, overview, data, data processing, application, and perspective. The section of overview introduces cities in transition and describe the changing of urban space and urban life in China. The second section lists some commonly used open data and big data in the ‘new data environment’. Then, methods for data acquisition, cleaning and analysis are illustrated in data processing section. To better explain the data analysis method, the fourth part introduces several Chinese research cases to illustrate the application of these methods in urban research. Last but not least, the last section is the most future-oriented one, which is composed of some methodologies and proposals such as Data Augmented Design (DAD) and Big Model. \u003c/p\u003e\n\u003cp\u003eThis course, which shares experiences on big data analysis and its research application, will suit those concerning contemporary urbanizing China and its urban planning in the context of information and communication technologies.\u003c/"])</script><script>self.__next_f.push([1,"p\u003e420:T51a,\u003cp\u003eWith rapid globalization and proliferation of social media, businesses and organizations are in face of enormous communication challenges. How to communicate effectively on social media, how to manage social media data analysis as well as handling fake news are definitely at the top of the list.\u003c/p\u003e\n\u003cp\u003eBy seeing challenge as opportunity, this course aims to unfold the communication challenges induced by the rise of social media in business corporations and most importantly, offer solutions to overcome these challenges. In addition, the course serves presents a vantage point to forge an interface of synergy between academics and practitioners to discuss and address global communication challenges. Participants can benefit from meaningful synergy between academics and practitioners as well as learning materials and meaningful multilateral discussions surrounding authentic communication cases and industrial practices engaging both instructors and participants.\u003c/p\u003e\n\u003cp\u003eIn sum, this course provides insights for business leaders, senior managerial members, and communication professionals by discussing the major communication challenges encountered by businesses around the world which in turn, helps the participants to advance their career in the ever-changing communication environment.\u003c/p\u003e421:T76b,\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIntroducing Natural Language Processing\u003c/em\u003e\u003c/strong\u003e is part one of the \u003ca href=\"https://courses.edx.org/dashboard/programs/b8d701a5-01e2-4e28-8313-cdf889550314/\" title=\"Text Analytics with Python professional certificate\"\u003e\u003cem\u003e\u003cstrong\u003eText Analytics with Python\u003c/strong\u003e\u003c/em\u003e professional certificate\u003c/a\u003e (or you can study it as a stand-alone course). This first course introduces the core techniques of natural language processing (NLP) and computational linguistics. But we introduce these techniques from data science alongside the cognitive science that makes them possible.\u003c/p\u003e\n\u003cp\u003eHow can we make sense out of the incredible amount of knowledge that has been stored as text data? This course is a prac"])</script><script>self.__next_f.push([1,"tical and scientific introduction to natural language processing. That means you’ll learn how it works and why it works at the same time.\u003c/p\u003e\n\u003cp\u003eOn the practical side, you’ll learn how to actually do an analysis in Python: creating pipelines for text classification and text similarity that use machine learning. These pipelines are automated workflows that go all the way from data collection to visualization. You’ll learn to use Python packages like pandas, scikit-learn, and tensorflow.\u003c/p\u003e\n\u003cp\u003eOn the scientific side, you’ll learn what it means to understand language computationally. Artificial intelligence and humans don’t view documents in the same way. Sometimes AI sees patterns that are invisible to us. But other times AI can miss the obvious. We have to understand the limits of a computational approach to language and the ethical guidelines for applying it to real-world problems. For example, we can identify individuals from their tweets. But we could never predict future criminal behaviour using social media.\u003c/p\u003e\n\u003cp\u003eThis course will cover topics you may have heard of, like text processing, text mining, sentiment analysis, and topic modeling.\u003c/p\u003e422:T684,\u003cp\u003eExcel is one of the most widely used solutions for analyzing and visualizing data. It now includes tools that enable the analysis of more data, with improved visualizations and more sophisticated business logics. In this data science course, you will get an introduction to the latest versions of these new tools in Excel 2016 from an expert on the Excel Product Team at Microsoft.\u003c/p\u003e\r\n\u003cp\u003eLearn how to import data from different sources, create mashups between data sources, and prepare data for analysis. After preparing the data, find out how business calculations can be expressed using the DAX calculation engine. See how the data can be visualized and shared to the Power BI cloud service, after which it can be used in dashboards, queried using plain English sentences, and even consumed on mobile devices.\u003c/p\u003e\r\n\u003cp\u003eDo you feel that the contents of this"])</script><script>self.__next_f.push([1," course is a bit too advanced for you and you need to fill some gaps in your Excel knowledge? Do you need a better understanding of how pivot tables, pivot charts and slicers work together, and help in creating dashboards? If so, check out \u003ca href=\"https://aka.ms/edx-dat205x-about\"\u003eDAT205x: Introduction to Data Analysis using Excel\u003c/a\u003e.\u003c/p\u003e\r\n\u003cp\u003eedX offers financial assistance for learners who want to earn Verified Certificates but who may not be able to pay the fee. To apply for financial assistance, enroll in the course, then follow \u003ca href=\"https://courses.edx.org/financial-assistance/\"\u003ethis link\u003c/a\u003e to complete an application for assistance.\u003c/p\u003e\r\n\u003cp\u003e\u003cstrong\u003e*Note:\u003c/strong\u003e *This course will retire at the end of October. Please enroll only if you are able to finish your coursework in time.\u003c/p\u003e423:T419,\u003cp\u003eMaking decisions based on financial data is essential to the success of any company. This accounting and finance Professional Certificate, brought to you by Babson College, the #1 school in Entrepreneurship (U.S. News \u0026 World Report), will give learners the foundational accounting and finance skills needed to make critical business decisions.\u003c/p\u003e\r\n\r\n\u003cp\u003eWhether you’re an entrepreneur, in management, or looking to become a financial analyst, this program will help you make informed decisions by teaching you financial fundamentals: terms, metrics, and the pillars of primary financial statements: income statement, balance sheet, and cash flow.\u003c/p\u003e \r\n\r\n\u003cp\u003eYou'll learn how to interpret and use the information contained in financial statements to make key operating decisions, evaluate a company’s performance, and create forecasts of profits and cash flow. You will learn how to use ratios to diagnose a company's financial health and apply these concepts and tools to valuation, liquidity and other factors for sound decision-making.\u003c/p\u003e424:T438,\u003cp\u003eWhat does it take to succeed in today’s competitive job market? The workplace skills that employers value are changing, and most traditional education paths do not match "])</script><script>self.__next_f.push([1,"employers’ evolving needs. Applicants across industries and job types need more than just the standard credentials to get hired and be successful.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe five-course Fullbridge Career Development: Skills for Success is designed to help you build in-demand workplace competencies that will ensure you stand out as an applicant and employee.\r\nThis self-paced program explores contemporary business fundamentals and helps you create a marketable personal brand. Through innovative and engaging online learning techniques, you will build and enhance the critical hard and soft skills that will help you stand out in your current or future profession.\u003c/p\u003e\r\n\r\n\u003cp\u003eUpon completion of the five courses, the Fullbridge Career Development: Skills for Success certificate will enhance your resume and tell the world that you are equipped with the skills to succeed in the new hyper-competitive workplace.\u003c/p\u003e425:Ta40,"])</script><script>self.__next_f.push([1,"\u003cp\u003eDemand planning has always been an essential process for virtually every company. But due to consumer behavior changes in recent years, improving forecasting has become a priority. That is the reason why Certified Demand Planners have never been in such high demand.\u003c/p\u003e\r\n\r\n\u003cp\u003eDemand management in today’s business environment is a challenging task. Disruptive events like pandemics, economic sanctions, and armed conflicts have a huge impact on both consumer behavior and goods availability. Top companies are currently recruiting Skilled Demand Planners to be able to adapt to the \"new normal\" conditions, and ISCEA’s Certified Forecaster and Demand Planner (CFDP) Program will place you at the center stage.\u003c/p\u003e\r\n\r\n\u003cp\u003eWhether you are looking to start or boost your career as an Internationally Recognized Demand planner - ISCEA’s CFDP certificate will make you stand out from the crowd. The CFDP credential is sought by recruiters worldwide because it demonstrates that you can reduce procurement, inventory control, and replenishment costs while maximizing the value from inventory management, supply chain planning, ERP, statistical forecasting, and other software investments. These goals can be achieved by improving demand forecasting processes and forecast accuracy.\u003c/p\u003e\r\n\r\n\u003cp\u003eWhen you finish this program, you will be able to help companies cope with rapidly changing demand and improve decision-making processes in the context of Strategic Planning, S\u0026OP (Sales and Operations Planning) / IBP (Integrated Business Planning) processes, and Demand-Driven Supply Chain practices. You will also be able to develop seasonal demand forecasting and new product forecasting strategies, as well as implement a successful CPFR (Collaborative Planning, Forecasting, and Replenishment) process from the ground up.\u003c/p\u003e\r\n\r\n\u003cp\u003eWith no prerequisites, the online courses in this Professional Certification Program will provide you with the knowledge and skills highly regarded by top companies looking to increase their profitability and remain competitive.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis Certification Program is offered by the International Supply Chain Education Alliance (ISCEA), a world-renowned and globally recognized developer of internationally recognized certification programs for supply chain professionals, including Certified Supply Chain Manager (CSCM), Certified Supply Chain Analyst (CSCA) and Certified Forecaster and Demand Planner (CFDP).\u003c/p\u003e\r\n\r\n\u003cp\u003eTo become an ISCEA certified professional Forecaster and Demand Planner (CFDP), you must complete all three preparatory courses and successfully pass the CFDP Exam.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"426:T5b6,\u003cp\u003eAs data becomes more available and less well-known property types become more established, investors are increasingly looking to diversify their portfolios and gain access to the different risk-and-return profiles these sectors can potentially offer. In a constrained property environment, investors and developers with an understanding of the macroeconomic drivers that influence the development of commercial property in a country stand to benefit the most. This includes the knowledge of the economics underpinning financing and valuing commercial property, including valuation theory explored in a commercial property context, and the use of financial modeling to forecast returns.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe Financing and Valuing Commercial Property Professional Certificate program from the University of Cape Town (UCT) provides a sound understanding of the economics underpinning the investing in commercial property, as well as insight into the specialized tools required for property valuation.\u003c/p\u003e \r\n\r\n\u003cp\u003eGuided by UCT faculty with a knowledge of property feasibility analysis, valuation, and property finance, the program offers theoretical perspectives and practical approaches to commercial property investment. Understand different types of commercial properties, analyze the market, and discover investment vehicles. Finally, explore a broad overview of the South African listed property market and how it can be leveraged as an investment opportunity.\u003c/p\u003e427:T563,\u003cp\u003eThis course is the first of a two-course sequence: Introduction to Computer Science and Programming Using Python, and Introduction to Computational Thinking and Data Science. Together, they are designed to help people with no prior exposure to computer science or programming learn to think computationally and write programs to tackle useful problems. Some of the people taking the two courses will use them as a stepping stone to more advanced computer science courses, but for many it will be their first and last computer science courses. This run features lect"])</script><script>self.__next_f.push([1,"ure videos, lecture exercises, and problem sets using Python 3.5. Even if you previously took the course with Python 2.7, you will be able to easily transition to Python 3.5 in future courses, or enroll now to refresh your learning. \u003c/p\u003e\n\u003cp\u003eSince these courses may be the only formal computer science courses many of the students take, we have chosen to focus on breadth rather than depth. The goal is to provide students with a brief introduction to many topics so they will have an idea of what is possible when they need to think about how to use computation to accomplish some goal later in their career. That said, they are not \"computation appreciation\" courses. They are challenging and rigorous courses in which the students spend a lot of time and effort learning to bend the computer to their will\u003c/p\u003e428:T611,\u003cp\u003eWhat do you know about TinyML? Tiny Machine Learning (TinyML) is one of the fastest-growing areas of Deep Learning and is rapidly becoming more accessible. This course provides a foundation for you to understand this emerging field.\u003c/p\u003e\n\u003cp\u003eTinyML is at the intersection of embedded Machine Learning (ML) applications, algorithms, hardware, and software. TinyML differs from mainstream machine learning (e.g., server and cloud) in that it requires not only software expertise, but also embedded-hardware expertise.\u003c/p\u003e\n\u003cp\u003eThe first course in the TinyML Certificate series, Fundamentals of TinyML will focus on the basics of machine learning, deep learning, and embedded devices and systems, such as smartphones and other tiny devices. Throughout the course, you will learn data science techniques for collecting data and develop an understanding of learning algorithms to train basic machine learning models. At the end of this course, you will be able to understand the “language” behind TinyML and be ready to dive into the application of TinyML in future courses.\u003c/p\u003e\n\u003cp\u003eFollowing Fundamentals of TinyML, the other courses in the TinyML Professional Certificate program will allow you to see the code behind widely-used"])</script><script>self.__next_f.push([1," Tiny ML applications—such as tiny devices and smartphones—and deploy code to your own physical TinyML device. Fundamentals of TinyML provides an introduction to TinyML and is not a prerequisite for Applications of TinyML or Deploying TinyML for those with sufficient machine learning and embedded systems experience.\u003c/p\u003e429:T8c9,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eDo you want to learn more about data and gain programming experience? If yes, this is the right course for you to start!\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e“Big data”, “data science”, “data-mining” and “artificial intelligence” are all popular terms that are often encountered nowadays in the academic and in business worlds.\u003c/p\u003e\n\u003cp\u003eComputer Science is a field where the usage of computers and “computations” have continuously evolved. As computational power increases, computation becomes an indispensable tool for solving complex problems and making predictions. We are now able to “compute” various things from DNA sequencing to aerodynamics simulations and weather forecasting.\u003c/p\u003e\n\u003cp\u003eIn this course, you will learn the essence of computer science. You will obtain an overview of cutting-edge computer science as well as learn the basics and introductory level knowledge of computer science, while experiencing, designing and writing your own simple programs. This revised course consists of 5 weekly units. A separate course covering the same content is also offered where the lectures are given in Japanese. \u003c/p\u003e\n\u003cp\u003eThe course begins by introducing the notion of computation and data and how things work inside a computer. Before jumping into advanced topics like encryption and cryptanalysis, we will discover important notions like \"arrays\", \"characters\" and \"strings\" and we will know more about functions and subroutines throughout the lectures. Along the way we will practice with exercises to have a hands-on experience and deeper understanding of the explained notions. All exercises have files that can be downloaded and used. A detailed explanation about setting up the environment to run the programs using the language Ruby is provided at the beginning of the course.\u003c/p\u003e\n\u003cp\u003eBy the end of this course you will be able to write your own programs for encryption and decryption of English texts. The course concludes with a general overview of the recent progress and research trends in the computer science field. This last part covers topics discussing the nature of computation as well as applications of computation in our modern society such as simulations, data mining and artificial intelligence AI.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"42a:T56c,\u003cp\u003e\u003cem\u003ePlease Note: Learners who successfully complete this IBM course can earn a skill badge —a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLooking to kickstart a career in deep learning? Look no further. This course will introduce you to the field of deep learning and teach you the fundamentals. You will learn about some of the exciting applications of deep learning, the basics fo neural networks, different deep learning models, and how to build your first deep learning model using the easy yet powerful library Keras.\u003c/p\u003e\n\u003cp\u003eThis course will presentsimplified explanations to some oftoday's hottest topics in data science, including:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhat is deep learning?\u003c/li\u003e\n\u003cli\u003eHow do neural networks learn and what are activation functions?\u003c/li\u003e\n\u003cli\u003eWhat are deep learning libraries and how do they compare to one another?\u003c/li\u003e\n\u003cli\u003eWhat are supervised and unsupervised deep learning models?\u003c/li\u003e\n\u003cli\u003eHow to use Keras to build, train, and test deep learning models?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe demand fordeep learning skills-- and the job salaries of deep learning practitioners -- arecontinuing to grow, as AI becomes more pervasive in our societies. This course will help you build the knowledge you need to future-proofyour career.\u003c/p\u003e42b:T4ea,\u003cp\u003e\u003cem\u003ePlease Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis course builds on your existing SQL knowledge to learn about additional techniques that are key to Data Engineers.\u003c/p\u003e\n\u003cp\u003eYou will learn how to create and use views to simplify and control access to underlying tables. You will learn about the advantages provided by stored procedures and how to write and run them. You will discover the importan"])</script><script>self.__next_f.push([1,"ce of ACID transactions to maintain the integrity of your data and how to use them in your code. Finally, you will learn how to use different types of joins to accurately retrieve related data from multiple tables.\u003c/p\u003e\n\u003cp\u003ePRE-REQUISITE: This course is intended as a follow on to SQL for Data Science course from IBM. Please ensure that you have either completed that course prior to starting this one, or have foundational/intermediate knowledge of SQL and are familiar with creating tables, performing select statements, filtering, sorting and grouping data, and working with nested queries and multiple tables.\u003c/p\u003e42c:T6d6,\u003cp\u003eDigamos que necesitas comprender big data; miles o incluso millones de filas de datos, y tienes poco tiempo para hacerlo. los datos pueden provenir de tu equipo, en cuyo caso tal vez ya estés familiarizado con lo que estás midiendo y de los resultados que se esperan. O puede provenir de otro equipo, o tal vez de varios equipos a la vez, y estar completamente familiarizado.\u003c/p\u003e\n\u003cp\u003eDe cualquier manera, la razón por la que lo estás viendo es porque tienes que tomar una decisión lo mas rápido posible y sería de gran ayuda que los datos te informen como tomarla (inteligencia de negocios); pues esa decisión podrá afectar positiva o negativamente a un cliente, a un producto o a toda la organización. En este curso online, conocerás una parte importante de la ciencia de datos o Data Science; la visualización de los datos y las herramientas que puedes usar para aplicarlos.\u003c/p\u003e\n\u003cp\u003eDebido a la forma en que el cerebro humano procesa la información, usar tablas o gráficos para visualizar grandes cantidades de datos complejos es mas fácil que analizar hojas de cálculo o informes. La visualización de datos es una forma rápida y fácil de transmitir conceptos de manera universal, y es posible el poder experimentar con diferentes escenarios haciendo pequeños ajustes.\u003c/p\u003e\n\u003cp\u003eLa visualización de datos es la presentación de datos en un formato gráfico.\u003c/p\u003e\n\u003cp\u003ePermite a los tomadores de decisiones"])</script><script>self.__next_f.push([1," ver el análisis presentados visualmente, para que puedan comprender conceptos dificiles o identificar nuevos patrones. La visualización de datos revela informacion inadvertida, especiamente en grandes conjuntos de datos (big data); da respuestas mas rápido; y ayuda a entender mas fácilmente la relación causa-efecto.\u003c/p\u003e42d:Tbf9,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cstrong\u003eDemystify complex big data technologies\u003cbr /\u003e\n\u003c/strong\u003e Compared to traditional data processing, modern tools can be complex to grasp. Before we can use these tools effectively, we need to know how to handle big data sets. You will understand how and why certain principles – such as immutability and pure functions – enable parallel data processing (‘divide and conquer’), which is necessary to manage big data. \u003c/p\u003e\n\u003cp\u003eDuring this course you will acquire this principal foundation from which to move forward. Namely, how to recognise and put into practice the scalable solution that’s right for your situation. \u003c/p\u003e\n\u003cp\u003eThe insights and tools of this course are regardless of programming language, but user-friendly examples are provided in Python, Hadoop HDFS and Apache Spark. Although these principles can also be applied to other sectors, we will use examples from the agri-food sector.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData collection and processing in an Agri-food context\u003cbr /\u003e\n\u003c/strong\u003e Agri-food deserves special focus when it comes to choosing robust data management technologies due to its inherent variability and uncertainty. Wageningen University \u0026amp; Research’s knowledge domain is healthy food and the living environment. That makes our data experts especially equipped to forge the bridge between the agri-food business on the one hand, and data science, artificial intelligence (AI) on the other. \u003c/p\u003e\n\u003cp\u003eCombining data from the latest sensing technologies with machine learning/deep learning methodologies, allows us to unlock insights we didn’t have access to before. In the areas of smart farming and precision agriculture this allows us to:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBetter manage dairy cattle by combining animal-level data on behaviour, health and feed with milk production and composition from milking machines.\u003c/li\u003e\n\u003cli\u003eReduce the amount of fertilisers (nitrogen), pesticides (chemicals) and water used on crops by monitoring individual plants with a robot or drone.\u003c/li\u003e\n\u003cli\u003eMore accurately predict crop yields on a continental scale by combining current with historic data on soil, weather patterns and crop yields.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn short, this course’s foundational knowledge and skills for big data prepare you for the next step: to find more effective and scalable solutions for smarter, innovative insights.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFor whom?\u003c/strong\u003e\u003cbr /\u003e\nYou are a manager or researcher with a big data set on your hands, perhaps considering investing in big data tools. You’ve done some programming before, but your skills are a bit rusty. You want to learn how to effectively and efficiently manage very large datasets. This course will enable you to see and evaluate opportunities for the application of big data technologies within your domain. Enrol now.\u003c/p\u003e\n\u003cp\u003eThis course has been partially supported by the European Union Horizon 2020 Research and Innovation program (Grant #810\u003cspan lang=\"EN-US\"\u003e 775, \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e“\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003eDragon\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e”\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e).\u003c/span\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"42e:Ta58,"])</script><script>self.__next_f.push([1,"\u003cp\u003eGain a good understanding of what Deep Learning is, what types of problems it resolves, and what are the fundamental concepts and methods it entails. The course developed by IVADO, Mila and Université de Montréal offers diversified learning tools for you to fully grasp the extent of this ground-breaking cross-cutting technology, a critical need in the field.\u003ca href=\"http://www.ivado.ca/en\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIVADO, a scientific and economic data science hub bridging industrial, academic and government partners with expertise in digital intelligence designed the course, and the world-renowned \u003ca href=\"https://mila.quebec/en/\"\u003eMila\u003c/a\u003e, rallying researchers specialized in Deep Learning, created the content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis course is based on presentations from an event held in Montreal, from September 9 to 13, 2019. It was adapted to an online course (MOOC) format and was released, for the first time, in March 2020. The tutorials' material was updated on Colab Notebook in Spring of 2021.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMila’s founder and IVADO’s scientific director, \u003cstrong\u003e\u003ca href=\"https://mila.quebec/en/yoshua-bengio/\"\u003eYoshua Bengio\u003c/a\u003e, also a professor at Université de Montréal, is a world-leading expert in artificial intelligence and a pioneer in deep learning as well as the scientific director of this course\u003c/strong\u003e. He is also a joint recipient of the 2018 A.M. Turing Award, “the Nobel Prize of Computing”, for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.\u003c/p\u003e\n\u003cp\u003eDeep Learning is an extension of Machine Learning where machines can learn by experience without human intervention. It is largely influenced by the human brain in the fact that algorithms, or artificial neural networks, are able to learn from massive amounts of data and acquire skills that a human brain would. Thus, Deep learning is now able to tackle a large variety of tasks that were considered out of reach a few years ago in computer vision, signal processing, natural language processing, robotics, and sequential decision-making. Because of these recent advances, various industries are now deploying deep learning models that impact various economic sectors such as transport, health, finance, energy, as well as our daily life in general.\u003c/p\u003e\n\u003cp\u003eIf you are a professional, a scientist or an academic with basic knowledge in mathematics and programming, this MOOC is designed for you! Atop the rich Deep Learning content, discover issues of bias and discrimination in machine learning and benefit from this sociotechnical topic that has proven to be a great eye-opener for many.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"42f:T47b,\u003cp\u003eIn this course, you will:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBuild foundational cloud computing infrastructure, including websites using serverless technology, virtual machines, and PaaS (Platform as a Service).\u003c/li\u003e\n\u003cli\u003eApply agile software development techniques to small and large projects, useful for building portfolio projects and global-scale cloud infrastructures.\u003c/li\u003e\n\u003cli\u003eLearn how to effectively choose the right level of abstraction: IaaS (Infrastructure as a Service), MaaS (Metal as a Service), PaaS, and Serverless.\u003c/li\u003e\n\u003cli\u003eApply DevOps principles to Cloud Computing, Data Engineering, and Machine Learning.\u003c/li\u003e\n\u003cli\u003eUtilize IaC (Infrastructure as Code) to manage and provision Cloud infrastructure in a repeatable and idempotent process.\u003c/li\u003e\n\u003cli\u003eDevelop Continuous Delivery pipelines for efficient cloud infrastructure management.\u003c/li\u003e\n\u003cli\u003eEvaluate best practices for implementing solutions with Cloud Computing.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis course is ideal for beginners and intermediate students interested in applying cloud computing to data science, machine learning, and data engineering. Students should have beginner-level Linux and Python skills.\u003c/p\u003e430:T799,\u003cp\u003eAdvances in Artificial Intelligence and Machine Learning have led to technological revolutions. Yet, AI systems at the forefront of such innovations have been the center of growing concerns. These involve reports of system failure when conditions are only slightly different from the training phase and they also trigger ethical and societal considerations that arise as a result of their use. \u003c/p\u003e\n\u003cp\u003eMachine learning models have been criticized for lacking robustness, fairness and transparency. Such model-related problems can generally be attributed to a large extent to issues with data. In order to learn comprehensive, fine-grained and unbiased patterns, models have to be trained on a large number of high-quality data instances with distribution that accurately represents real application scenarios. Creating such data is not only a long, laborious and expensive process"])</script><script>self.__next_f.push([1,", but sometimes even impossible when the data is extremely imbalanced, or the distribution constantly evolves over time.\u003c/p\u003e\n\u003cp\u003eThis course will introduce an important method that can be used to gather data for training machine learning models and building AI systems. Crowdsourcing offers a viable means of leveraging human intelligence at scale for data creation, enrichment and interpretation with great potential to improve the performance of AI systems and increase the wider adoption of AI in general. \u003c/p\u003e\n\u003cp\u003eBy the end of this course you will be able to understand and apply crowdsourcing methods to elicit human input as a means of gathering high-quality data for machine learning. You will be able to identify biases in datasets as a result of how they are gathered or created and select from task design choices that can optimize data quality. These learnings will contribute to an important set of skills that are essential for career trajectories in the field of Data Science, Machine Learning, and the broader realms of Artificial Intelligence.\u003c/p\u003e431:T5e3,\u003cp\u003eCuando se trata de herramientas para el análisis de datos, siempre tenemos las siguientes preguntas: ¿Cuál es la diferencia entre tantas herramientas que existen?¿Cuál es la mejor?¿Cuál deberia aprender?\u003c/p\u003e\n\u003cp\u003eLas funciones que realizan los científicos de datos incluyen la identificación de preguntas relevantes, la recopilación de datos de diferentes fuentes de datos, la organización de datos, la transformación de datos a la solución y la comunicación de estos hallazgos para tomar mejores decisiones comerciales.\u003c/p\u003e\n\u003cp\u003eLas herramientas de ciencia de datos o Data Science pueden ser de dos tipos:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eUno para aquellos que tienen conocimientos de programación.\u003c/li\u003e\n\u003cli\u003eOtro para los usuarios comerciales.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLas herramientas para el primer tipo, tienen que ver con el área de las tecnologías de información en donde se busca que la persona tenga conocimientos de algún lenguaje de programación como R o Python y comunmente a es"])</script><script>self.__next_f.push([1,"tas personas se les denomina científicos de datos.\u003c/p\u003e\n\u003cp\u003eLas herramientas que son para los usuarios comerciales se enfocan en automatizar el análisis de datos; en este tipo, los usuarios tienen conocimientos básicos de un lenguaje de programación, pero un fuerte conocimiento del área de dominio; por lo que se han empezado a llamar ciudadanos científicos de datos.\u003c/p\u003e\n\u003cp\u003eEstas herramientas te permitirán tomar las mejores decisiones basadas en el análisis de datos (también conocido como inteligencia de negocios).\u003c/p\u003e432:T913,"])</script><script>self.__next_f.push([1,"\u003cp\u003eHistóricamente, las matemáticas nacieron por primera vez, debido a la necesidad de entender nuestro entorno y tomar decisiones. En particular, la ciencia de datos (data science), se enfoca en el procesamiento de datos: analizar, explorar e interpretar conjuntos de datos, y con base en ello, tener un panorama completo del presente.\u003c/p\u003e\n\u003cp\u003eAl escuchar sobre la ciencia de los datos, se podría pensar que es un tema únicamente relacionado con los científicos de datos y el big data, dirigido a personas que hablan lenguajes de programación como python o que concierne solo a empresas de base tecnológica e inteligencia artificial como IBM o Amazon. Sin embargo, cada vez más, es necesario hacer uso de la inteligencia de negocios y por medio de la minería de datos obtener resultados para una mejor toma de decisiones sin importar el giro de la empresa.\u003c/p\u003e\n\u003cp\u003eConsiderando que la mercadotecnia tiene entre sus objetivos la identificación de necesidades y preferencias de los consumidores para satisfacer sus necesidades, aprender a realizar encuestas que reúnan datos y conocer las herramientas de análisis de datos idóneas para grandes cantidades de datos, se convierte imprescindible para el crecimiento de toda empresa o negocio.\u003c/p\u003e\n\u003cp\u003eSin necesidad de ser un experto en data science o programador, actualmente existen una serie de técnicas estadísticas que pueden ser utilizadas por un marketer, como excel y spss, y estas le permitirán realizar data mining o extracción de datos, para identificar las variables importantes de un producto, clasificar a los consumidores, organizar sus gustos y en general, tomar decisiones precisas con una base matemática a problemas complejos.\u003c/p\u003e\n\u003cp\u003eEste curso se encuentra enfocado en su totalidad al estudio de mercado, desde los ejemplos hasta las técnicas de análisis estadístico seleccionadas, tanto para pequeños como grandes volúmenes. Utilizando softwares estadísticos para realizar el proceso matemático en las bases de datos y la visualización de datos, permitiendo un enfoque interpretativo de los resultados para la toma de decisiones.\u003c/p\u003e\n\u003cp\u003eUtiliza el business intelligence a tu favor y adquiere las herramientas necesarias para llevar el marketing de tu empresa o negocio al siguiente nivel y realizar una toma de decisiones acertada.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"433:T5d6,\u003cp\u003eIs my code fast? Can it be faster? Scientific computing, machine learning, and data science are about solving problems that are compute intensive. Choosing the right algorithm, extracting parallelism at various levels, and amortizing the cost of data movement are vital to achieving scalable speedup and high performance. \u003c/p\u003e\n\u003cp\u003eIn this course, the simple but important example of matrix-matrix multiplication is used to illustrate fundamental techniques for attaining high-performance on modern CPUs. A carefully designed and scaffolded sequence of exercises leads the learner from a naive implementation to one that effectively utilizes instruction level parallelism and culminates in a high-performance multithreaded implementation. Along the way, it is discovered that careful attention to data movement is key to efficient computing. \u003c/p\u003e\n\u003cp\u003ePrerequisites for this course are a basic understanding of matrix computations (roughly equivalent toWeeks 1-5 of Linear Algebra: Foundations to Frontiers on edX) and an exposure to programming. Hands-on exercises start with skeletal code in the C programming language that is progressively modified, so that extensive experience with C is not required. Access to a relatively recent x86 processor such as Intel Haswell or AMD Ryzen (or newer) running Linux is required. \u003c/p\u003e\n\u003cp\u003eMATLAB Online licenses will be made available to the participants free of charge for the duration of the course. \u003c/p\u003e\n\u003cp\u003eJoin us to satisfy your need for speed!\u003c/p\u003e434:T5f4,\u003cp\u003eEngage in this course pertaining to a highly impactful yet, too rarely discussed, AI-related topic. You will learn from international experts in the field, also speakers at IVADO’s International School on Bias and Discrimination in AI, which took place in Montreal, and explore the social and technical aspects of bias, discrimination and fairness in machine learning and algorithm design.\u003c/p\u003e\n\u003cp\u003eThe main focus of this course is: gender, race and socioeconomic-based bias as well as bias in data-driven predictive models leading t"])</script><script>self.__next_f.push([1,"o decisions. The course is primarily intended for professionals and academics with basic knowledge in mathematics and programming, but the rich content will be of great use to whomever uses, or is interested in, AI in any other way. These sociotechnical topics have proven to be great eye-openers for technical professionals!\u003c/p\u003e\n\u003cp\u003eThe total duration of the video content available in this course is 7:30 hours, cut into relevant segments that you may watch at your own pace. There are also comprehensive quizzes at the end of each segment to measure your understanding of the content.\u003ca href=\"http://www.ivado.ca/en\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIVADO is a scientific and economic data science hub bridging industrial, academic and governmental partners with expertise in digital intelligence. One of its missions is to contribute to the advancement of digital knowledge and train new generations of bias-aware data scientists.\u003c/p\u003e\n\u003cp\u003eWelcome to this enlightening journey in the world of ethical AI!\u003c/p\u003e435:T449,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eJupyter Notebook is a popular, streamlined application for analyzing data and creating data science projects. However, developers often experience difficulty when attempting to share the files created on Jupyter Notebook.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eJupyter Book is a powerful tool you can use to build widely shareable, interactive HTML documentation, including interactive visualizations in books using data from your Jupyter Notebook projects.\u003c/p\u003e\n\u003cp\u003eYou will learn how to build, customize, and share your first Jupyter Book in under an hour. You will also learn how to display LaTex block style math, display Plotly plots, and exclude certain files from the final build. Completing this project will provide you with practical experience and provide you with fundamental Jupyter Book skills.\u003c/p\u003e\n\u003cp\u003eReady to start? We have a workspace waiting for you. Get started fast using a pre-configured cloud-based IDE lab environment. You’ll find all the required software needed to get started, such as Jupyter Book, preinstalled. All you need is"])</script><script>self.__next_f.push([1," a recent version of a modern web browser.\u003c/p\u003e436:T83a,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis course is part III of the series of Quantum computing courses, which covers aspects from fundamentals to present-day hardware platforms to quantum software and programming.\u003c/p\u003e\n\u003cp\u003eThe goal of part III is to discuss some of the key domain-specific algorithms that are developed by exploiting the fundamental quantum phenomena (e.g. entanglement)and computing models discussed in part I. We will begin by discussing classic examples of quantum Fourier transform and search algorithms, along with its application for factorization (the famous Shor’s algorithm). Next, we will focus on the more recently developed algorithms focusing on applications to optimization, quantum simulation, quantum chemistry, machine learning, and data science.\u003c/p\u003e\n\u003cp\u003eA particularly exciting recent development has been the emergence of near-intermediate scale quantum (NISQ) computers. We will also discuss how these machines are driving new algorithmic development. A key aspect of the course is to provide hands-on training for running (few qubit instances of) the quantum algorithms on present-day quantum hardware. For this purpose, we will take advantage of the availability of cloud-based access to quantum computers and quantum software.\u003c/p\u003e\n\u003cp\u003eThe material will appeal to engineering students, natural sciences students, and professionals whose interests are in using as well as developing quantum technologies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAttention:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQuantum Computing 1: Fundamentals\u003c/em\u003e is an essential prerequisite to \u003cem\u003eQuantum Computing 2: Hardware\u003c/em\u003e and \u003cem\u003eQuantum Computing 3: Algorithm and Software\u003c/em\u003e. Learners should plan to complete Fundamentals (1) before enrolling in the Hardware (2) or the Algorithm and Software (3) courses.\u003c/p\u003e\n\u003cp\u003eAlternatively, learners can enroll in courses 2 or 3 if they have solid experience with or knowledge of quantum computing fundamentals, including the following: 1) postulates of quantum mechanics; 2) gate-based quantum computing; 3) quantum errors and error correction; 3) adiabatic quantum computing; and 5) quantum applications and NISQ-era.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"437:T4cc,\u003cp\u003eLinear algebra is at the core of all of modern mathematics, and is used everywhere from statistics and data science, to economics, physics and electrical engineering. However, learning the subject is not principally about acquiring computational ability, but is more a matter of fluency in its language and theory.\u003c/p\u003e\n\u003cp\u003eIn this course, we will start with systems of linear equations, and connect them to vectors and vector spaces, matrices, and linear transformations. We will be emphasizing the vocabulary throughout, so that students become comfortable working with the different aspects.\u003c/p\u003e\n\u003cp\u003eWe will then introduce matrix and vector operations such as matrix multiplication and inverses, paying particular attention to their underlying purposes. Students will learn not just how to calculate them, but also why they work the way that they do.\u003c/p\u003e\n\u003cp\u003eWe willdiscuss the key concepts of basis and dimension, which form the foundation for many of the more advanced concepts of linear algebra.\u003c/p\u003e\n\u003cp\u003eThe last chapter concerns inner products, which allow us to use linear algebra for approximating solutions; we will see how this allows for applications ranging from statistics and linear regression to digital audio.\u003c/p\u003e438:T4f9,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eJupyter Notebook is a popular, streamlined application for analyzing data and creating data science projects. However, developers often experience difficulty when attempting to share the files created on Jupyter Notebook.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eJupyter Book is a powerful tool you can use to build widely shareable, interactive HTML documentation, including interactive visualizations in books using data from your Jupyter Notebook projects.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eYou will learn how to build, customize, and share your first Jupyter Book in under an hour. You will also learn how to display LaTex block style math, display Plotly plots, and exclude certain files from the final build. Completing this"])</script><script>self.__next_f.push([1," project will provide you with practical experience and provide you with fundamental Jupyter Book skills.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eReady to start? We have a workspace waiting for you. Get started fast using a pre-configured cloud-based IDE lab environment. You’ll find all the required software needed to get started, such as Jupyter Book, preinstalled. All you need is a recent version of a modern web browser.\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e439:T5f8,\u003cp\u003eLa ciencia de datos y las habilidades de aprendizaje automático continúan teniendo una gran demanda en todas las industrias, y la necesidad de profesionales de datos está en auge. Al completar este programa de Certificación Profesional, contarás con las habilidades y la experiencia que necesitas para comenzar tu carrera profesional en la ciencia de datos y el aprendizaje automático.\u003c/p\u003e\r\n\r\n\u003cp\u003eA través de tareas prácticas e instrucción de alta calidad, crearás un porfolio utilizando herramientas de ciencia de datos reales y problemas y conjuntos de datos del mundo real. El plan de estudios cubrirá una amplia gama de temas de ciencia de datos que incluyen: herramientas y bibliotecas de código abierto, metodologías, Python, bases de datos, SQL, visualización de datos, análisis de datos y aprendizaje automático. No se requieren conocimientos previos de informática o programación para poder tomar este programa.\u003c/p\u003e\r\n\r\n\u003cp\u003eCualquier persona con algunas habilidades informáticas y una pasión por el autoaprendizaje puede tener éxito, ya que comenzamos de lo básico y poco a poco vamos desarrollando problemas y temas más complejos.\u003c/p\u003e\r\n\r\n\u003cp\u003eCon la gran necesidad de profesionales de la ciencia de datos y analistas de datos en el mercado hoy en día, este programa impulsará tu camino en la ciencia de datos y te preparará con una cartera de entregables de ciencia de datos para brindarte la confianza para dar el paso y comenzar tu carrera profesional en el campo de la ciencia de datos.\u003c/p\u003e43a:T438,\u003cp\u003eIf you have ever used a navi"])</script><script>self.__next_f.push([1,"gation service to find the optimal route and estimate time to destination, you've used algorithms on graphs.\u003c/p\u003e\n\u003cp\u003eGraphs arise in various real-world situations, as there are road networks, water and electricity supply networks, computer networks and, most recently, social networks! If you're looking for the fastest time to get to work, cheapest way to connect set of computers into a network or efficient algorithm to automatically find communities and opinion leaders in Facebook, you're going to work with graphs and algorithms on graphs.\u003c/p\u003e\n\u003cp\u003eIn this course, part of the Algorithms and Data Structures MicroMasters program, you will learn what a graph is and its most important properties. You’ll learn several ways to traverse graphs and how you can do useful things while traversing the graph in some order. We will also talk about shortest paths algorithms. We will finish with minimum spanning trees, which are used to plan road, telephone and computer networks and also find applications in clustering and approximate algorithms.\u003c/p\u003e43b:T554,\u003cp\u003eCon este curso en línea aprenderás a tomar decisiones empresariales exitosas. Para ello, aprenderás el proceso completo desde extraer data, hasta su integración, visualización, depuración, análisis y uso. \u003cstrong\u003ePodrás transformar data cruda en insumos para la toma de decisiones.\u003c/strong\u003e Dominarás el uso de software, herramientas y sistemas de apoyo.\u003c/p\u003e\n\u003cp\u003ePodras \u003cstrong\u003eutilizar Power BI\u003c/strong\u003e como una poderosa herramienta para el análisis de negocios, aprendiendo a utilizar un dashboard para hacer más fácil \u003cstrong\u003eel proceso de decision making y reporting.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEste curso en línea sobre \u003cstrong\u003eherramientas de business intelligence\u003c/strong\u003e te permitirá \u003cstrong\u003epracticar la teoría\u003c/strong\u003e. Tendrás acceso a recursos como tutoriales, casos practicos y ejemplos de casos reales que nutrirán el conocimiento adquirido. Conocerás \u003cstrong\u003eel poder de la data para aplicarla\u003c/strong\u003e a negocios grandes o pequeños.\u003c/p\u003e\n\u003cp\u003eEste es el segundo cur"])</script><script>self.__next_f.push([1,"so del \u003cstrong\u003ePrograma de Certificación Profesional de Inteligencia de Negocios.\u003c/strong\u003e El primero es sobre \u003cstrong\u003eEstadística aplicada a negocios\u003c/strong\u003e. Te recomendamos completar ambos para que adquieras conocimiento teórico y experiencia práctica. Tendrás el respaldo de la experiencia de \u003cstrong\u003eJorge Samayoa, Ph.D. por la Universidad de Purdue.\u003c/strong\u003e\u003c/p\u003e43c:T4f3,\u003cul\u003e\n\u003cli\u003eCómo usar Power BI\u003c/li\u003e\n\u003cli\u003eCómo usar sistemas para recolectar datos (OLAP)\u003c/li\u003e\n\u003cli\u003eCómo recolectar, analizar y depurar data para convertirla en datos\u003c/li\u003e\n\u003cli\u003eCómo descubrir datos útiles para la operación y transformarlos en información\u003c/li\u003e\n\u003cli\u003eQué proceso seguir para tomar de decisiones (Decision Making)\u003c/li\u003e\n\u003cli\u003eCómo usar sistemas de soporte para la toma de decisiones (DSS)\u003c/li\u003e\n\u003cli\u003eCuáles son las buenas prácticas de inteligencia de negocios\u003c/li\u003e\n\u003cli\u003eCómo configurar un dashboard de inteligencia de negocios\u003c/li\u003e\n\u003cli\u003eCómo usar herramientas de Business Intelligence\u003c/li\u003e\n\u003cli\u003eQué software apoya la inteligencia de negocios\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eLo que no puedes dejar pasar\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAprenderás a utilizar Power BI\u003c/li\u003e\n\u003cli\u003eTendrás acceso a tutoriales para aprender y dominar todo tipo de conceptos: desde los más básicos hasta los más especializados\u003c/li\u003e\n\u003cli\u003eEncontrarás casos prácticos para aplicar la teoría a negocios reales\u003c/li\u003e\n\u003cli\u003eContarás con la guía de un PhD en el campo que diseño los cursos con base en su experiencia y busca aportar conocimiento valioso y actualizado\u003c/li\u003e\n\u003cli\u003eTendrás acceso a una red de contactos con los que podrás realizar networking con profesionales de todo el mundo\u003c/li\u003e\n\u003c/ul\u003e43d:T443,\u003cp\u003eToday, businesses, consumers, and societies leave behind massive amounts of data as a by-product of their activities. Leading-edge companies in every industry are using analytics to replace intuition and guesswork in their decision-making. As a result, managers are collecting and analyzing enormous data sets to discover new patterns and insights and running controlled experiments to "])</script><script>self.__next_f.push([1,"test hypotheses. \u003c/p\u003e\n\u003cp\u003eThis course prepares students to understand business analytics and become leaders in these areas in business organizations. This course teaches the scientific process of transforming data into insights for making better business decisions. It covers the methodologies, issues, and challenges related to analyzing business data. It will illustrate the processes of analytics by allowing students to apply business analytics algorithms and methodologies to business problems. The use of examples places business analytics techniques in context and teaches students how to avoid the common pitfalls, emphasizing the importance of applying proper business analytics techniques.\u003c/p\u003e43e:T417,\u003cp\u003eAnalytical models are key to understanding data, generating predictions, and making business decisions. Without models it’s nearly impossible to gain insights from data. 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Which customer is likely to default in his/her loan repayment? Which products should be recommended to an existing customer? Finding right answers to these questions can be chal"])</script><script>self.__next_f.push([1,"lenging yet rewarding.\u003c/p\u003e\r\n\u003cp\u003ePredictive analytics is emerging as a competitive strategy across many business sectors and can set apart high performing companies. It aims to predict the probability of the occurrence of a future event such as customer churn, loan defaults, and stock market fluctuations – leading to effective business management.\u003c/p\u003e\r\n\u003cp\u003eModels such as multiple linear regression, logistic regression, auto-regressive integrated moving average (ARIMA), decision trees, and neural networks are frequently used in solving predictive analytics problems. Regression models help us understand the relationships among these variables and how their relationships can be exploited to make decisions.\u003c/p\u003e\r\n\u003cp\u003eThis course is suitable for students/practitioners interested in improving their knowledge in the field of predictive analytics. The course will also prepare the learner for a career in the field of data analytics. If you are in the quest for the right competitive strategy to make companies successful, then join us to master the tools of predictive analytics.\u003c/p\u003e440:T6b7,\u003cp\u003eIn the last decade, the amount of data available to organizations has reached unprecedented levels. Data is transforming business, social interactions, and the future of our society. In this course, you will learn how to use data and analytics to give an edge to your career and your life. We will examine real world examples of how analytics have been used to significantly improve a business or industry. These examples include Moneyball, eHarmony, the Framingham Heart Study, Twitter, IBM Watson, and Netflix. Through these examples and many more, we will teach you the following analytics methods: linear regression, logistic regression, trees, text analytics, clustering, visualization, and optimization. We will be using the statistical software R to build models and work with data. The contents of this course are essentially the same as those of the corresponding MIT class (The Analytics Edge). It is a challenging class, but it will enable "])</script><script>self.__next_f.push([1,"you to apply analytics to real-world applications.\u003c/p\u003e\n\u003cp\u003eThe class will consist of lecture videos, which are broken into small pieces, usually between 4 and 8 minutes each. After each lecture piece, we will ask you a \"quick question\" to assess your understanding of the material. There will also be a recitation, in which one of the teaching assistants will go over the methods introduced with a new example and data set. Each week will have a homework assignment that involves working in R or LibreOffice with various data sets. (R is a free statistical and computing software environment we'll use in the course. See the Software FAQ below for more info). At the end of the class there will be a final exam, which will be similar to the homework assignments.\u003c/p\u003e441:T946,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThe business landscape is changing so rapidly that traditional management, business and computing courses do not meet the needs for the next generation of workers in the business world. Most traditional methods are of a repetitive, rule-based nature and will be gradually replaced by Artificial Intelligence. In the knowledge era, the most value added job will be to manage knowledge, which includes how knowledge is created, mined, processed, shared and reused in different trades and industry. At the same time, the amount of data and information (prerequisites of knowledge) is exploding exponentially. By 2020, IDC projects that the size of the digital universe will reach 40 zetabytes from all sources including, websites, weblog, sensors, and social media. Digitalisation, Cloud Computing, Big data will transform how we live, work and even think in a Networked Economy. These trends and more will have a profound effect on how we see the world and create policies. 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Most of our research is company and industry based. Capabilities and competencies of the KMIRC are further strengthened by the international alliances it has formed with leading practitioners, many of which are regarded as members of the \"Hall of Fame\" in knowledge management, and renowned worldwide. The course is suitable for participants with a background in humanities, management, social science, physical science or engineering. No prior technical background is needed.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"442:T5ad,\u003cp\u003eInterrupted time series analysis and regression discontinuity designs are two of the most rigorous ways to evaluate policies with routinely collected data. 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These studies have cut across the social sciences, including:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eStudying the effect of traffic speed zones on mortality\u003c/li\u003e\n\u003cli\u003eQuantifying the impact of incentive payments to workers on productivity\u003c/li\u003e\n\u003cli\u003eAssessing whether alcohol policies reduce suicide\u003c/li\u003e\n\u003cli\u003eMeasuring the impact of incentive payments to physicians on quality of care\u003c/li\u003e\n\u003cli\u003eDetermining whether the use of HPV vaccination influences adolescent sexual behavior\u003c/li\u003e\n\u003c/ul\u003e443:T4ef,\u003cp\u003eDo you want to be more reflective in your teaching practice and wonder if there are technologies that can help? Are you curious about how data-driven, evidence-based teaching practices can improve your students’ learning? 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Para ello, te guiaremos a través de dos cursos en los que aprenderás \u003cstrong\u003ecómo cumplir objetivos empresariales \u003c/strong\u003ecomo: reducir costos, analizar datos, optimizar operaciones, anticipar demanda, tomar decisiones acertadas y obtener información valiosa de tus clientes. Todo, a partir de data que habrás sabido extraer, recolectar, integrar, depurar y analizar. Podrás integrar datos a repositorios y convertirlos en información de valor. Sabrás cómo usar herramientas y software de business intelligence. \u003c/p\u003e\r\n\r\n\u003cp\u003eEste \u003cstrong\u003e Programa de Certificación Profesional en Business Intelligence \u003c/strong\u003e está compuesto de dos cursos desarrollados por \u003cstrong\u003e Jorge Samayoa, Ph.D. por la Universidad de Purdue \u003c/strong\u003e. Tendrás el respaldo de su experiencia como consultor en big data, Director de la Maestría de Investigación de Operaciones en Universidad Galileo y docente universitario.\u003c/p\u003e\r\n\r\n\u003cp\u003eEl primer curso online de \u003cstrong\u003eestadística aplicada a negocios \u003c/strong\u003ete dará acceso a videos, ejemplos profesionales y ejercicios prácticos. Te permitirá conocer \u003cstrong\u003e criterios para tomar decisiones \u003c/strong\u003ea partir del análisis de datos e información relevante. Iniciarás utilizando herramientas como \u003cstrong\u003eExcel \u003c/strong\u003epara el análisis de datos y serás capaz de utilizar probabilidad, de trabajar con estimadores, de utilizar econometría y de realizar análisis regresional.\u003c/p\u003e\r\n\r\n\u003cp\u003eEn el segundo \u003cstrong\u003ecurso \u003c/strong\u003e aprenderás sobre decision making: \u003cstrong\u003ecómo tomar decisiones empresariales a partir de datos. \u003c/strong\u003eSerás capaz de utilizar sistemas para recolectar datos (OLAP), modelos para tomar de decisiones, sistemas de soporte para la toma de decisiones (DSS), buenas prácticas de inteligencia de negocios, herramientas y software. Podrás utilizar \u003cstrong\u003ePower BI como herramienta de inteligencia de negocios \u003c/strong\u003e. Tendrás acceso a tutoriales guiados y casos prácticos aplicados a negocios para reforzar tu conocimiento.\u003c/p\u003e\r\n\r\n\u003cp\u003eEste es tu momento de \u003cstrong\u003eavanzar profesional. \u003c/strong\u003e Anímate a inscribirte en los dos cursos del \u003cstrong\u003e Programa de Certificación Profesional en Inteligencia de Negocios \u003c/strong\u003e. Notarás un \"antes\" y un \"después\" en tu carrera profesional.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"445:T54d,\u003cp\u003eIn today's ever-changing business landscape, it is more important than ever for leaders to have an understanding of artificial intelligence and data analytics. AI is expected to drive significant growth and value for the global economy, generated by the companies and countries that leverage it over the coming years. With that in mind, it's critical for leaders, managers, executives, and board members to develop their AI skills and understand how to leverage data to make the right decisions to grow their businesses.\u003c/p\u003e \r\n\r\n\u003cp\u003eThis Professional Certificate in AI and Data Analytics, brought to you by Babson College, the #1 school in Entrepreneurship (U.S. News \u0026 World Report), will give leaders a basic understanding of AI and how autonomous data can be used to make critical business decisions. The courses in this program will give learners the skills, strategies, and tactics to create AI-powered business models and explain how AI will impact their customers, employees, investors, operations, and product/service offerings.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe program will also dive deeper into data analytics to help business leaders understand the fundamental concepts of sound statistical thinking. Key concepts such as understanding variation, perceiving the relative risk of alternative decisions, and pinpointing sources of variation will be highlighted.\u003c/p\u003e446:Ta3d,"])</script><script>self.__next_f.push([1,"\u003cp\u003eTwenty-first century societies are highly connected, digitalized and networked. According to the International Data Corporation, by 2025 there will be 175 zetabytes of digital assets on the web. These include data, multimedia files, computing resources, software and more. There also social networks, many of which are purposely cultivated, which serve as a great source of collaboration and harnessing of collective wisdom. Meanwhile, Industry 4.0, or the Industrial Internet, provides an abundance of new opportunities for deploying intelligent applications in areas such as automation, monitoring, correction, decision, prediction and customization. Properly sourced data and harnessed knowledge are crucial for enabling business excellence. Competitive advantage is no longer achieved through an organization’s internal operational excellence and competition with rival firms, but through competency building, harnessing collective wisdom, and collaboration through networks. New value chains that lead to superior customer experience are often realized by applying newfound technological knowledge to fields such as processes management, machine learning, Big Data, automated decision-making and business model innovation.\u003c/p\u003e \r\n \r\n\u003cp\u003eBalancing theories and practice, and supplemented by in-depth case studies and practical illustrations, this program equips you with the skills and knowledge to strategize investments in digital transformations in an Industry 4.0 world. It covers everything from mining data, harnessing/sharing knowledge in organizations, leveraging the cloud for collaboration, competency building, innovation and fostering learning communities to identifying the skills today’s knowledge workers need. Case studies focus on healthcare, manufacturing, logistics, engineering, education, and the public sector. Learners may also join a vibrant online learning community made up of current and past learners, to co-learn together both during and after the program.\u003c/p\u003e\r\n \r\n\u003cp\u003eBased on two proven courses that have been on offer for the past three to five years, this program is designed to help managers and consultants from a non-technical background decide where to invest and design their digital transformation strategies in connected, networked societies, and develop smart products, services, systems in manufacturing, logistics, transportation, engineering, businesses, education and the home. Most learners who took the two MOOCs that form part of this program had backgrounds in product development, engineering, research, human resources, IT, logistics, and corporate planning.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"447:T4a7,\u003cp\u003eMaking good business decisions can make or break an entrepreneur. This business and management XSeries, brought to you by Babson College, the #1 school in Entrepreneurship (U.S. News \u0026 World Report), will explore topics such as financial accounting, analytics, customer behavior, and strategy all with the lens of an entrepreneur who has to make critical decisions for the success of the business. \u003c/p\u003e\r\n\u003cp\u003eWhether you are contributing to the strategy and development of your company, starting your own business, or working for a non-profit, this series will prepare you to make better, more informed decisions and contribute to the success and health of the organization. Before we begin with some of these core business concepts, we will define what entrepreneurial thinking is and discover some key methodologies to help with these decisions. \u003c/p\u003e\r\n\u003cp\u003eThis XSeries is targeted at:\u003c/p\u003e\r\n\u003cul\u003e\r\n\u003cli\u003eLearners interested in starting their own business.\u003c/li\u003e\r\n\u003cli\u003eLearners who want to or are asked to contribute to the decisions of any organization.\u003c/li\u003e\r\n\u003cli\u003eLearners who are ready to move forward in his or her organization.\u003c/li\u003e\r\n\u003cli\u003eLearners who are ready to take action!\u003c/li\u003e\r\n\u003c/ul\u003e448:T71b,\u003cp\u003eThis course is an online learning content that matches the \"CAAD Internship\" for the second-year undergraduate students at Tsinghua University School of Architecture. The \"CAAD Internship\" course lasts for one week and is held throughout the week. It is a compulsory internship course for undergraduates majoring in architecture, urban planning, and landscape architecture in the School of Architecture. There are about 120 students. This online course is the first part of the \"CAAD Practical\" course and teaches Grasshopper programming methods. Using the parametric method to design is to introduce the thinking of programming into the design and use algorithmic logic to generate geometric shapes. Such a design method can describe the logic of form, function and structure in the design through algorithms, which not only expands th"])</script><script>self.__next_f.push([1,"e architect's ability to control form, but also makes the design more rational and adaptable. Grasshopper is a widely used parametric design platform. It is based on Rhinoceros three-dimensional modeling software and performs parametric modeling programming based on geometric modeling systems such as Nurbs and Mesh. Grasshopper adopts a graphical programming method. Many common algorithms are encapsulated in modules and combined in a graphical interface. Therefore, it is easy to write and highly efficient, making it very suitable for designers to learn and apply. In addition, Grasshopper, as a parametric design platform, has many plug-ins that can introduce different geometric modeling, physical simulation, and performance optimization algorithms into its system to expand its functions, and these plug-ins are still growing rapidly. Therefore, Grasshopper is a very valuable tool in scheme creation and daily design work, and it is worth learning by every designer.\u003c/p\u003e449:T866,"])</script><script>self.__next_f.push([1,"\u003cp\u003eTechnical analysis is a discipline that uses market data to define the state of financial markets. It provides a framework to forecast the expected trend in the market.\u003c/p\u003e\r\n\r\n\u003cp\u003eTools of technical analysis can be applied in any time frame. This makes them useful for day traders, individual investors managing retirement accounts or analysts preparing detailed reports that include opinions on the likelihood of gains in a market.\u003c/p\u003e\r\n\r\n\u003cp\u003eBecause the analysis is based solely on market data, technical analysis can be applied to individual stocks, broad market indexes, futures contracts, cryptocurrencies, foreign exchange or any other market.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe information in these courses can be used to develop a standalone trading methodology. It can also be used as a supplement to fundamental analysis, using fundamentals to determine what to buy or sell while using technicals to determine when to make the trades. In this way, technical analysis helps an analyst add value to their reports.\u003c/p\u003e\r\n\r\n\u003cp\u003eFundamentals of Technical Analysis focuses on defining the tools of technical analysis including chart patterns and indicators. Quantitative Technical Analysis provides a strategy for incorporating the tools into an algorithmic format that can be automated or applied manually in a disciplined manner.\u003c/p\u003e\r\n\r\n\u003cp\u003eCombined, the courses provide all the information needed to become a trader in any market around the world or an analyst specializing in technical reports of financial markets.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis professional certificate is comprised of the following courses:\r\n\u003cul\u003e\r\n\u003cli\u003eC01: Fundamentals of Technical Analysis\u003c/li\u003e\r\n\u003cli\u003eC02: Quantitative Technical Analysis\u003c/li\u003e\r\n\u003c/ul\u003e\r\n\u003c/p\u003e\r\n\r\n\u003cp\u003eFree Preview! Access the first 3 modules from Course 1 for free. For full course access, upgrade to a verified certificate.\u003c/p\u003e\r\n\r\n\u003cp\u003eNOTE: Completing both courses is MANDATORY to achieve the edX Professional Certificate in Introduction to Trading with Technical Analysis. A verified learner must pass all courses in the program with a minimum grade of 70% to earn a Professional Certificate for Introduction to Trading with Technical Analysis.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"44a:T7dd,\u003cp\u003e\u003cem\u003ePlease Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNOTE: In order to be successful in completing this course, please ensure you are familiar with PyTorch Basics and have practical knowledge to apply it to Machine Learning. If you do not have this pre-requiste knowledge, it is highly recommended you complete the \u003ca href=\"https://www.edx.org/course/pytorch-basics-for-machine-learning\"\u003ePyTorch Basics for Machine Learning\u003c/a\u003e course prior to starting this course.\u003c/p\u003e\n\u003cp\u003eThis course is the second part of a two-part course on how to develop Deep Learning models using Pytorch.\u003c/p\u003e\n\u003cp\u003eIn the first course, you learned the basics of PyTorch; in this course, you will learn how to build deep neural networks in PyTorch. Also, you will learn how to train these models using state of the art methods. You will first review multiclass classification, learning how to build and train a multiclass linear classifier in PyTorch. This will be followed by an in-depth introduction on how to construct Feed-forward neural networks in PyTorch, learning how to train these models, how to adjust hyperparameters such as activation functions and the number of neurons.\u003c/p\u003e\n\u003cp\u003eYou will then learn how to build and train deep neural networks—learning how to apply methods such as dropout, initialization, different types of optimizers and batch normalization. We will then focus on Convolutional Neural Networks, training your model on a GPU and Transfer Learning (pre-trained models). You will finally learn about dimensionality reduction and autoencoders. Including principal component analysis, data whitening, shallow autoencoders, deep autoencoders, transfer learning with autoencoders, and autoencoder applications.\u003c/p\u003e\n\u003cp\u003eFinally, you will test your skills in a final project.\u003c/p\u003e44b:T4a0,\u003cp\u003eUnderstanding "])</script><script>self.__next_f.push([1,"the brain requires an integrated understanding of different scales of organisation of the brain. This means studying the role that genes, channels, cells, microcircuits, and even whole brain regions have in different types of behaviour: From perception to action, while asleep or when being awake. \u003c/p\u003e\n\u003cp\u003eThis coursewill take the you through the latest data, models and techniques for investigating the different levels of the brain. We will show how we can put the pieces together and attain new insights and derive new theories. With contributions from more than 10 international neuroscientists from six different research institutions, the MOOC gives a broad overview of the latest tools and techniques for neuroinformatics, analysis, modelling and simulation.\u003cbr /\u003e\nAt the same time, several different tutorials on available data and data tools, such as those from the Allen Institute for Brain Science, provide you with in-depth knowledge on brain atlases, gene expression data and modeling neurons. These tutorials will be followed by exercises that give you the opportunity to acquire the necessary skills to use the tools and data for your own research.\u003c/p\u003e44c:T747,The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic modeling and the related field of statistical inference are the keys to analyzing data and making scientifically sound predictions.\u003cbr /\u003e\u003cbr /\u003eThis course is part of a 2-part sequence on the basic tools of probabilistic modeling. Topics covered in this course include:\u0026nbsp;\u0026nbsp;\u003cbr /\u003e\r\n\u003cul\u003e\r\n\u003cli\u003elaws of large numbers\u003c/li\u003e\r\n\u003cli\u003ethe main tools of Bayesian inference methods\u003c/li\u003e\r\n\u003cli\u003ean introduction to classical statistical methods\u003c/li\u003e\r\n\u003cli\u003ean introduction to random processes (Poisson processes and Markov chains)\u003c/li\u003e\r\n\u003c/ul\u003e\r\n\u003cbr /\u003eThis course is a follow-up to Introduction to Probability: Part I - The Fundamentals, which introduced the general framework of probability models, multiple discrete or continuous random v"])</script><script>self.__next_f.push([1,"ariables, expectations, conditional distributions, and various powerful tools of general applicability. The contents of the two parts of the course are essentially the same as those of the corresponding MIT class, which has been offered and continuously refined over more than 50 years. It is a challenging class, but will enable you to apply the tools of probability theory to real-world applications or your research.\u003cbr /\u003e\u003cbr /\u003eProbabilistic models use the language of mathematics. But instead of relying on the traditional \"theorem - proof\" format, we develop the material in an intuitive - but still rigorous and mathematically precise - manner. Furthermore, while the applications are multiple and evident, we emphasize the basic concepts and methodologies that are universally applicable.\u003cbr /\u003e\u003cbr /\u003e\u003cem\u003ePhoto by Pablo Ruiz M\u0026uacute;zquiz on Flickr.\u0026nbsp;(\u003ca href=\"https://creativecommons.org/licenses/by-nc-sa/2.0/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003eCC BY-NC-SA 2.0\u003c/a\u003e)\u003c/em\u003e44d:T1264,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThe electric grid of the future will need to be more resilient, decentralized, and capable of integrating more distributed energy resources including on-site renewable energy technologies, energy storage and even electric vehicle (EV) charging. Microgrids are an important building block in designing this sustainable grid architecture of the future. This course covers fundamental concepts of microgrid design from a community-centric perspective and emphasizes a holistic approach to energy systems management.\u003c/p\u003e\r\n\u003cp\u003eThis course:\u003c/p\u003e\r\n\u003cul\u003e\r\n\u003cli\u003eProvides knowledge and insights to critically evaluate microgrid systems design and related topics such as distributed renewable energy systems, energy storage demand response, and other load management techniques.\u003c/li\u003e\r\n\u003cli\u003ePrioritizes a community-centric approach with a holistic approach to energy services, including not only electricity supply but also heating, cooling, and transportation applications.\u003c/li\u003e\r\n\u003cli\u003eEmphasizes resilience and sustainability with a strong focus on strategies for economically integrating high levels of distributed renewable energy generation such as solar photovoltaic, wind, and small hydroelectric.\u003c/li\u003e\r\n\u003cli\u003eExplores concepts and best practices through extensive real-world examples, including site visits to numerous operational microgrid systems.\u003c/li\u003e\r\n\u003cli\u003eProvides insights from experts who design, build, and operate some of the most innovative and advanced microgrids in the world.\u003c/li\u003e\r\n\u003cli\u003eEstablishes a strong foundation of what to consider at all stages of developing a microgrid project, including flexible and iterative approaches to project conceptualization, data collection, design, and modeling.\u003c/li\u003e\r\n\u003c/ul\u003e\r\n\u003cp\u003eAlaska is an early adopter of microgrids that integrate renewable energy due to economic necessity, with over 100 systems representing the largest installed capacity of any U.S. state. Alaska is home to microgrids that are constantly evolving to take advantage of new technologies and integration approaches. The University of Alaska Fairbanks works closely with communities, utilities, and developers across the state – and around the world – in designing and developing robust, cost-effective, and resilient energy solutions based on distributed energy resources (DER) and microgrid system architectures.\u003c/p\u003e\r\n\u003cp\u003eSpecific real-world systems that will be explored include:\u003c/p\u003e\r\n\u003cul\u003e\r\n\u003cli\u003eKodiak Island, Alaska is a community of approximately 10,000 residents that has systematically transitioned to 100% renewable energy from a combination of resources including wind turbines and hydroelectric power coupled with a flywheel and battery (Li-ion) storage system.\u003c/li\u003e\r\n\u003cli\u003eCordova, Alaska is a fishing community with highly variable and seasonal industrial loads striving to transition to 100% renewable energy using a combination of run-of-river hydroelectric, energy storage, EVs, and hierarchical control strategies using smart grid enabling technologies.\u003c/li\u003e\r\n\u003cli\u003eKongiganak, Alaska is a small Yupik Eskimo community that has developed an innovative wind-based microgrid system that uses real-time response algorithms to manage dispatchable thermal loads to achieve 100% wind energy penetration for significant periods of time.\u003c/li\u003e\r\n\u003cli\u003eKotzebue, Alaska is an Inupiat Eskimo community that has over two decades of experience in wind and solar PV development. Kotzebue has taken a holistic approach to energy management that extends from investment in EVs to making ice for local fishermen using absorption refrigeration.\u003c/li\u003e\r\n\u003cli\u003eKing Island, Australia is home to a highly innovative microgrid system, owned and operated by Hydro Tasmania. The system combines nearly 3 MW of solar and wind with a range of innovative supporting technologies. The system is capable of 100% renewable operation, and supplies over 65% of King Island’s annual energy needs using renewable energy.\u003c/li\u003e\r\n\u003cli\u003eHawaii is a U.S. leader in the integration of variable renewable energy, with a goal to generate 100 percent clean energy by 2045. While some of Hawaii’s individual island grids are too big to typically be categorized as microgrids, several microgrids have been installed at military bases and commercial and industrial sites. There are many lessons learned from Hawaii’s experience with both microgrids and regional grids that are transferable to similar small grid architectures. \u003c/li\u003e\r\n\u003cli\u003eFairbanks, Alaska is our hometown, but also home to some unique energy systems that support a very large multi-community microgrid operated by Golden Valley Electric Association with some unique features, including a very large battery energy storage system coupled with a flywheel.\u003c/li\u003e\r\n\u003c/ul\u003e"])</script><script>self.__next_f.push([1,"44e:T4d3,\u003cul\u003e\r\n\u003cli\u003eTypes of microgrids and the energy and infrastructure services they provide.\u003c/li\u003e\r\n\u003cli\u003eNon-grid connected/off-grid or islanded microgrid projects and systems, systems sometimes also labeled as “mini-grids”\u003c/li\u003e\r\n\u003cli\u003eStrategies for low, medium, and high contribution renewable energy systems integration\u003c/li\u003e\r\n\u003cli\u003eDispatchable and non-dispatchable power sources\u003c/li\u003e\r\n\u003cli\u003eSystem control, regulation, and optimization\u003c/li\u003e\r\n\u003cli\u003ePower electronics advances with inverters and energy storage devices to enable increasing proportions of variable renewable resources into microgrids.\u003c/li\u003e\r\n\u003cli\u003eLearn about different system designs as well as control and optimization strategies for converter-dominated power systems ranging from simple droop frequency (or voltage) control to advanced smart grid enabling technologies. \u003c/li\u003e\r\n\u003cli\u003eExplore the role of energy storage technologies such as batteries and flywheels coupled with the importance of inverter technologies and other power electronics in enabling very high penetration levels of renewable resources such as wind, and photovoltaics.\u003c/li\u003e\r\n\u003cli\u003eStrategies and best practices for designing a microgrid system with a focus on scoping, data collection, and modeling.\u003c/li\u003e\r\n\u003c/ul\u003e44f:T79a,\u003cp\u003eWe have witnessed the power of mechanization in the early nineteen century, automation in the seventies, information and the internet in the last decades. But now, the adaptation of connected intelligence into the business and social fabrics is advancing at an astonishing speed, which will completely change the way we conduct business. \u003c/p\u003e\n\u003cp\u003eIn this course,we will discuss changes/predictions we forsee in the future, such as: \u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eThe credit card business will fade out gradually\u003c/li\u003e\n\u003cli\u003eAll currency will be bitcoin\u003c/li\u003e\n\u003cli\u003eWhereas traditional internet gives rise to digital divide, the mobile internet will narrow the wealth gap\u003c/li\u003e\n\u003cli\u003eMost e-business models will become obsolete, leaving customer to factory (C2F)\u003c/li\u003e\n\u003cli\u003eNo more talents to hide except partners\u003c/li\u003e\n\u003cli"])</script><script>self.__next_f.push([1,"\u003eMobile phones will be outdated and replaced by augmented virtual reality (AVR)\u003c/li\u003e\n\u003cli\u003eBig corporates will transform to big platforms\u003c/li\u003e\n\u003cli\u003eThe birth of real Internet economics\u003c/li\u003e\n\u003cli\u003eThe death of global manufacturing to networked and dispersed manufacturing\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis course will explain how these changes will be brought about by the extensive use of digital intelligence, which will be available on mobile, internet, and pervasive computing as homes, offices and factories become a well knitted cyber-physical system. \u003c/p\u003e\n\u003cp\u003eEnabling tools such as Cloud Computing, Big Data, Internet of Things and Cyber Physical Systems are introduced. Automation, intelligence and collaborations are also discussed with particular reference to smart manufacturing, smart products/services and smart cities, and their opportunities and challenges. \u003c/p\u003e\n\u003cp\u003eThis is not a technical course; instead part of the focus is on organizational readiness, skills gaps and competencies for knowledge workers to fully leverage the power of Industry 4.0. \u003c/p\u003e\n\u003cp\u003eSuitable for learners from all disciplines and interested in the mega changes to our society.\u003c/p\u003e450:T527,\u003cp\u003eI sistemi di database sono diventati fondamentali nei sistemi di gestione delle informazioni, contribuendo allo sviluppo di una società sempre più basata sulla conoscenza. Tutte le moderne applicazioni IT sfruttano un database per la memorizzazione, l'elaborazione e il recupero delle informazioni. Viene quindi presentato il linguaggio SQL per l'interazione con un database relazionale e vengono introdotti i problemi di progettazione dei sistemi che interagiscono con i database. Infine, le tecnologie dei moderni sistemi di database sono descritte utilizzando il DBMS Oracle.\u003c/p\u003e\n\u003cp\u003eDatabase Systems have become fundamental in information management systems, contributing to the development of an increasingly knowledge-based society. All modern IT applications now exploit a database for storing, processing and retrieving information. The course illustrates the basic concepts f"])</script><script>self.__next_f.push([1,"or understanding not only database models and their evolution, but also the principles of their conceptual, logical and physical design within so-called information systems. The SQL language for interaction with a relational database is then presented and the problems of designing systems that interact with databases are introduced. Finally, the technologies of modern database systems are described by using the Oracle DBMS.\u003c/p\u003e451:T926,"])</script><script>self.__next_f.push([1,"\u003cp\u003eI sondaggi sono diventati un elemento centrale della cassetta degli attrezzi degli attori della politica e dell'informazione. Questo corso offre un viaggio all'interno del mondo dei sondaggi, nel modo in cui sono stati usati nel tempo, per riscoprire il loro valore principale di strumento della democrazia e per eliminare i rischi di un utilizzo distorto dei sondaggi. Gli insuccessi incassati negli ultimi anni dagli exit polls hanno contribuito a rilanciare il dibattito sull’attendibilità e l’utilità dello strumento sondaggistico. \u003c/p\u003e\n\u003cp\u003eIl corso si propone di mettere ordine nella discussione, chiarendo, in particolare: \u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ecosa è un sondaggio e cosa non lo è;\u003c/li\u003e\n\u003cli\u003equali sono le modalità di realizzazione dei sondaggi e le problematiche ad esse connesse;\u003c/li\u003e\n\u003cli\u003equali sfide sono messe in campo dalla diffusione delle nuove tecnologie dell'informazione;\u003c/li\u003e\n\u003cli\u003ein che misura i cambiamenti in atto nel quadro politico e nella composizione dell'elettorato condizionano il lavoro dell'analista dell'opinione pubblica;\u003c/li\u003e\n\u003cli\u003ein che modo si può ragionare sull'uso pubblico dei sondaggi d'opinione;\u003c/li\u003e\n\u003cli\u003equali sono i rischi reali della sondocrazia e quali le potenzialità legate all'uso governativo dei sondaggi.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eThe power of opinion polls\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOpinion polls play a central role in politics and information. This course explores the world of opinion polls and how they are used, to discover their value as tools for democracy and to reduce the risk of survey results being wrongly used. Failure on the part of exit polls in recent elections has contributed to renewed debate on their reliability and usefulness. \u003c/p\u003e\n\u003cp\u003eThis course aims to shed light on this debate, explaining, in particular: \u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ewhat a poll is, and what it is not\u003c/li\u003e\n\u003cli\u003ehow opinion polls are carried out\u003c/li\u003e\n\u003cli\u003ewhat the related problems and issues are\u003c/li\u003e\n\u003cli\u003ewhat are the main challenges posed by new information technologies\u003c/li\u003e\n\u003cli\u003eto what extent do current changes in the political environment and in the composition of the electorate affect the work of public opinion analysts\u003c/li\u003e\n\u003cli\u003ewhat are the main issues surrounding the use of opinion polls\u003c/li\u003e\n\u003cli\u003ewhat are the real risks and potential benefits related to the use of opinion polls at the level of government\u003c/li\u003e\n\u003c/ul\u003e"])</script><script>self.__next_f.push([1,"452:T93a,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIdentify, implement and evaluate an individual business project of your organization and compensate the course fee by its financial benefits. \r\n\r\n\u003cp\u003eYou will travel along the DMAIC, guided by digital resources and a Master Black Belt as co-pilot. You will stop at each key Sigma tool and document it in your project storybook. These results are reviewed at each DMAIC milestone and serve as the basis for individual coaching and certification. Your completed storybook will demonstrate the operational excellence of your work and the benefits you gained.\r\nOur goal is for you to successfully complete your project, for the benefits of your project to exceed the course fee, and for you to develop the generic competence to successfully identify, implement, and evaluate future projects. To this end, we will guide you digitally and in person.\r\n\r\n\u003cp\u003eOur digital guidance provides videos, eBooks and tasks to introduce every step of a project. SigmaGuide software offers frameworks for each step, while Minitab provides quantitative tools. Documenting every step of the project in a traceable way is necessary to prove methodical competence and provide a basis for steering the project.\r\n\r\n\u003cp\u003eOur \u003cb\u003epersonal guidance\u003c/b\u003e supports your individual needs in three formats:\r\n\u003cul\u003e\r\n\u003cli\u003e\u003cb\u003eDMAIC Phase Reviews\u003c/b\u003e will review the results of each phase, correct errors, show alternatives and suggest next steps.\u003c/li\u003e\r\n\u003cli\u003e\u003cb\u003eProject Coaching\u003c/b\u003e involves checking the suitability of the project topic and determining the focus (PreDEFINE), structuring the project and prioritizing the problems to be solved (DEFINE), prioritize your hypotheses and plan the collection of necessary data (MEASURE), optimizing the analysis of data, and reaching key milestones (ANALYSE). The IMPROVE phase typically does not require methodical support and the path through CONTROL is determined by ANALYSE.\u003c/li\u003e\r\n\u003cli\u003e\u003cb\u003eGreen Belt Lectures\u003c/b\u003e: Our online Green Belt lectures reinforce topics covered in the course material, such as scientific observation, tool application, hypothesis generation, data collection, hypothesis testing (Minitab), and problem modeling - do's and don'ts.\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eIn addition, you will acquire the basics of digital lean competence with our interactive process mining module. You can also use process mining for your project - if feasible.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"453:T12df,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis course reflects the most current version of the PMP exam, based on the Project Management Institute, Inc’s (PMI) current exam content outline (ECO). This will not only prepare you for the PMP, but also teach you valuable project management skills useful for program managers and project managers in this PMP training course.\u003c/p\u003e\r\n\r\n\u003cp\u003eCovers PMBOK Guide 7th Edition, PMBOK Guide 6th Edition, Agile Practice Guide, and more. \r\nPassing the PMP certification exam is an important step for any individual with project management experience looking to advance their career in the field and wants to earn this valuable credential. This course covers content related to predictive (traditional), adaptive (Agile), and hybrid projects. Each course allows you to earn up to 10 professional development units (PDUs). When you enroll as a verified learner, you’ll have immediate access to study materials for your PMP exam prep.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis course is taught by Instructor and global keynote speaker, Crystal Richards. Crystal is an experienced project manager and has over 20 years of experience working with project teams in the private-sector and public-sector. Crystal’s client work has been in the healthcare and federal government. She specializes in project management training and is a PMI PMP and PMI-ACP credential holder. Crystal has taught foundational project management courses and project management certification boot camp courses to thousands of students around the world both in the classroom and online.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe entire course will include the following:\r\n\u003cul\u003e\r\n\u003cli\u003eEarn 35 PDUs/Contact Hours by completing the entire course as required by PMI\u003c/li\u003e\r\n\u003cli\u003eContent based on the current PMP Examination Content Outline\u003c/li\u003e\r\n\u003cli\u003eExpert guidance completing the PMP application to meet exam eligibility requirements\u003c/li\u003e\r\n\u003cli\u003eExplanation of the project management processes\u003c/li\u003e\r\n\u003cli\u003eDiscussion of key project management topics such as scope management, cost management,\u003c/li\u003e schedule management, and risk management\u003c/li\u003e\r\n\u003cli\u003eDemonstrate use of key formulas, charts, and graphs\u003c/li\u003e\r\n\u003cli\u003eStrong foundation in Agile project management such as scrum, XP, and Kanban\u003c/li\u003e\r\n\u003cli\u003eExposure to challenging exam questions on practice exams—including \"wordy\" questions, questions with formulas, and questions with more than one correct answer\u003c/li\u003e\r\n\u003cli\u003eGuidance on the logistical details to sit for the exam such as information on the exam fee for PMI members and non-members, paying for PMI membership, prerequisites, and information on test centers.\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eThe course is broken up into 4 modules:\r\n\u003cul\u003e\r\n\u003cli\u003ePMP Prep: Project Management Principles – This module will provide an overview of predictive, Agile, and hybrid project management methodologies. The module will also delve into key project roles, and key concepts such as tailoring, progressive elaboration, and rolling wave planning.\u2028\u003c/li\u003e\r\n\u003cli\u003ePMP Prep: Managing People with Power Skills – Linked to the Leadership skill area of the PMI Talent Triangle®, this module will place focus on managing the expectations and relationships of the people involved in projects. Participants will need to demonstrate the knowledge, skills and behaviors to guide, motivate and/or direct others to achieve a goal. Key skills related to people include planning resource needs, managing stakeholder expectations, and communications planning and execution. This module will also delve into “power skills” such as negotiations, active listening, emotional intelligence, and servant leadership.\u2028\u003c/li\u003e\r\n\u003cli\u003ePMP Prep: Determining Ways of Working for Technical Project Management – Linked to the Technical skill area of the PMI Talent Triangle®, this module focuses on the technical aspects of successfully managing projects. Topics will delve into the core skills of scope, cost, and schedule management and integrating these concepts to develop a master project plan. Participants will also need to demonstrate an understanding of quality, risk, and procurement management and use techniques such as earned value, critical path methodology, and general data gathering and analysis techniques.\u003c/li\u003e\r\n\u003cli\u003ePMP Prep: Gaining Business Acumen for Project Managers– Linked to the Strategic and Business Management skill area of the PMI Talent Triangle®, this module will highlight the connection between projects and organizational strategy. Participants will need to demonstrate knowledge of and expertise in the industry/organization, so as to align the project goals and objectives to the organizational goals and enhance performance to better deliver business outcomes. Additional topics in this module will include compliance management and an understanding of how internal and external factors impact project outcomes.\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"454:T429,\u003cp\u003eIdentifying effective policies is a process of trial and error, innovation and experimentation, success and failure. This course provides the basic scientific and statistical tools needed to identify whether a policy or program is generating impact. Organized into modules, the course covers topics ranging from the attribution problem to what is meant by statistical significance (margin of error) to the analysis of data generated by a randomized control trial. The course also helps answer practical questions related to impact evaluation, such as how large of a sample is needed and what can be done when compliance with an experimental design is imperfect or when data is missing for part of the sample.\u003c/p\u003e\n\u003cp\u003eThis course was created collaboratively by Georgetown University and the World Bank's Strategic Impact Evaluation Fund with support from the Georgetown Center for New Designs in Learning and Scholarship, Georgetown University Initiative of Innovation, Development and Evaluation (gui2de), and The Open Learning Campus of the World Bank Group.\u003c/p\u003e455:Tdc0,"])</script><script>self.__next_f.push([1,"\u003cp\u003eSkilled project managers are adaptable and versatile. They are experts at working in a variety of ways, so they can adapt their methodology to best suit the situation. This allows them to successfully complete projects on time and on budget and achieve successful results.\u003c/p\u003e\n\u003cp\u003eLinked to the Technical skill area of the PMI Talent Triangle®, this course focuses on the technical aspects of successfully managing projects. Topics will delve into the core skills of scope, cost, and schedule management and integrating these concepts to develop a master project plan. Participants will also need to demonstrate an understanding of quality, risk, and procurement management and use techniques such as earned value, critical path methodology, and general data gathering and analysis techniques. \u003c/p\u003e\n\u003cp\u003eThis course focuses on the technical skills of successfully managing projects. Topics will include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eEstimating\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eReviewing project management methods\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePlanning for scope, schedule and cost throughout a project\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDemonstrating some understanding of quality improvement, risk management, and procurement management\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eApplying the concepts of earned value analysis and critical path scheduling technique.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eRegardless of the type of project you're working on, staying up to date with proven methods to manage your projects will allow you to get work done as effectively and efficiently as possible. The flexibility demonstrated through implementation of these tools allows project managers to switch between traditional methods when needed or gain new ways of working for different projects.\u003c/p\u003e\n\u003cp\u003eMaking the switch from “traditional” project management to agile is not always straightforward, and it can be particularly challenging for organizations who are accustomed to a predictive environment. However, with the right guidance and support, the transition doesn't have to be overwhelming. This course will help learners gain valuable insight into applying the adaptive techniques (iterative, incremental, or agile) that can make the transition smoother should you choose to do so in your real world projects. Adaptive techniques may feel unfamiliar and cumbersome at first, but with an open mind and some perseverance, the rewards can be great.\u003c/p\u003e\n\u003cp\u003eBy the end of the course, you will be exposed to the tools and techniques to ensure your projects are utilizing the most appropriate methods, tools, and techniques that meet the needs of your project–and meet the expectations of your stakeholders. Learners will discover that project managers can quickly adapt to any new situation they face by focusing on the goals they want to achieve and the value they expect to deliver. They should embrace change and recognize that there will always be new ideas, methods, and technology available for them to adopt.\u003c/p\u003e\n\u003cp\u003eAdditionally, upon successful completion of this course, learners can earn 8 contact hours of project management education which are recognized by the Project Management Institute (PMI). A total of 35 contact hours in project management education are a requirement to those looking to achieve the Project Management Professional (PMP®)certification. Learners will finish this course with increased knowledge of the better practice tips to engage stakeholders and be more than ready to continue their project management and PMP® journey, which we hope completes your certificate with us.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"456:T788,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMapping\u003c/span\u003e and geotechnologies are an exciting way for you to put your interest and passion for all things about the Earth and the Environment into action in ways that are in demand in the workplace by nonprofit organizations, government agencies, academia, and private industry and incredibly relevant to our 21st Century world! \u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eAll 21st Century issues—water quality, inequalities, human health, habitat loss, energy, climate, natural hazards, invasive species, and many more—take place \u003cspan lang=\"EN-US\"\u003esomewhere\u003c/span\u003e. \u003cspan lang=\"EN-US\"\u003eThese issues, \u003c/span\u003eincluding the United Nations Sustainable Development Goals (SDGs) are global issues that increasingly affect our everyday lives, our society, and our environment. These issues often exhibit spatial patterns that can be mapped and analyzed and require the analysis of data in the form of 2D and 3D maps, aircraft and satellite imagery, real time data feeds from the Internet of Things, and much more. We have a dynamic planet with natural forces shaping what is on, under, and above the surface, along with 8 billion humans as change agents, and thus key to understanding our world as it was, is, and should be in the future is mapping and analyzing change over space and time!\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIn this course, through a series of readings, videos, and hands-on exercises covering a variety of social and environmental themes, issues, and scales, you will learn the fundamentals of exciting and dynamic mapping tools, including projections, symbology, classification, and analysis. You will build your own web mapping applications such as dashboards and multimedia story maps. You will collect and map your own field data. You will gain skills and confidence to empower you to be able to use maps as analytical tools to build a brighter, more sustainable, more resilient tomorrow!\u003c/p\u003e457:T52d,\u003cp\u003eQuantitative strategies are popular among hedge funds and institutional investors. As demonstrated "])</script><script>self.__next_f.push([1,"in this course, they are also readily accessible to individual traders.\u003c/p\u003e\n\u003cp\u003eFundamentals principles of technical analysis are incorporated into complete trading strategies during the course. Those strategies are objectively defined with an eight-step process that is used to create entry and exit rules. Less well-known aspects of system design including position sizing are also explored.\u003c/p\u003e\n\u003cp\u003eDiscussion of strategy implementation includes risk management, system optimization and ongoing performance evaluation. Advantages and disadvantages of multiple methods of evaluating performance and risk are covered.\u003c/p\u003e\n\u003cp\u003eStrategies for equities and futures markets are developed using the design principles. \u003c/p\u003e\n\u003cp\u003eWhile systems are based on technical analysis, a process for incorporating fundamental data is also discussed. A complete strategy combining fundamental data and technical analysis is developed and as with other systems, the rules and historical returns are detailed.\u003c/p\u003e\n\u003cp\u003eIn fact, complete rules for several systems are provided and detailed historic results of returns and risks are analyzed. Steps for improving performance or tailoring the strategy to suit an individual’s needs are reviewed.\u003c/p\u003e458:Tb48,"])</script><script>self.__next_f.push([1,"\u003cp\u003eDemand for sustainable supply chains is on the rise. Booming growth in e-commerce, fast-shipping consumer expectations, Covid-19, and many other developments have created an ever more competitive business environment for companies from all sectors and industries. Companies face increasing pressure to become more sustainable from all stakeholders, such as policymakers, shareholders, investors, and NGOs. At the same time, consumers demand more sustainable products and services. The critical challenge for people working within organizations is becoming more sustainable while continuing to meet ambitious business goals to remain competitive in the market.\u003c/p\u003e\n\u003cp\u003eWhile many organizations have established goals to reduce emissions or become carbon neutral in the next 10 to 20 years, they struggle to translate these goals into specific actions and strategies. People in organizations may ask questions like:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eHow do we scope the analysis of our supply chains?\u003c/li\u003e\n\u003cli\u003eWhat is the proper methodology to estimate emissions?\u003c/li\u003e\n\u003cli\u003eHow do we implement actionable measurement strategies?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWhether you have a sustainability background or are new to the effort, Sustainable Supply Chain Management is an online course that will illuminate pathways for how you might build sustainable supply chains for your organization to achieve your sustainability goals while meeting—and even exceeding—business expectations.\u003c/p\u003e\n\u003cp\u003eIn this course, you will study practical alternatives for optimizing carbon emissions using geospatial analysis and data analytics. Examine the \"fast\" and \"green\" delivery trade-offs in the new digital era, consumer relationships to sustainable products and services, and environmental costs of fast-shipping e-commerce. You will learn key concepts in supply chain sustainability, including supply chain carbon footprint, sustainable transportation, green vehicle routing, fleet assignment, truck consolidation, circular supply chains, sustainable sourcing, supply chain transparency, and green inventory management.\u003c/p\u003e\n\u003cp\u003eHere are some of the key questions covered in the course:\u003c/p\u003e\n\u003cp\u003eHow might you…\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIdentify environmental hotspots in your supply chain?\u003c/li\u003e\n\u003cli\u003eUnderstand transportation's effects on supply chains?\u003c/li\u003e\n\u003cli\u003eImplement environmental measurements into logistics decisions?\u003c/li\u003e\n\u003cli\u003eBuild a green network distribution in your supply chain?\u003c/li\u003e\n\u003cli\u003eDesign a circular supply chain?\u003c/li\u003e\n\u003cli\u003eBuild sustainable sourcing operations and green replenishment strategies?\u003c/li\u003e\n\u003cli\u003eLeverage supply chain transparency to achieve economic, social, and environmental sustainability?\u003c/li\u003e\n\u003cli\u003eLeverage consumer preferences into sustainable supply chain strategies?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis course combines case studies, applied projects, and business-case simulations for a dynamic, interactive learning experience.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"459:T999,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis economics course provides an introduction to the field of cybersecurity through the lens of economic principles. Delivered by four leading research teams, it will provide you with the economic concepts, measurement approaches and data analytics to make better security and IT decisions, as well as understand the forces that shape the security decisions of other actors in the ecosystem of information goods and services.\u003c/p\u003e\r\n\u003cp\u003eSystems often fail because the organizations that defend them do not bear the full costs of failure. In order to solve the problems of growing vulnerability to computer hackers and increasing crime, solutions must coherently allocate responsibilities and liabilities so that the parties in a position to fix problems have an incentive to do so. This requires a technical comprehension of security threats combined with an economic perspective to uncover the strategies employed by cyber hackers, attackers and defenders.\u003c/p\u003e\r\n\u003cp\u003eThe course covers five main areas:\u003c/p\u003e\r\n\u003col\u003e\r\n\u003cli\u003eIntroduction to key concepts in security economics. Here, we provide an overview of how information security is shaped by economic mechanisms, such as misaligned incentives, information asymmetry, and externalities.\u003c/li\u003e\r\n\u003cli\u003eMeasuring cybersecurity. We introduce state of the art security and IT metrics and conceptualize the characteristics of a security metric, its challenges and advantages.\u003c/li\u003e\r\n\u003cli\u003eEconomics of information security investment. We discuss and apply different economic models that help determine the costs and benefits of security investments in network security.\u003c/li\u003e\r\n\u003cli\u003eSecurity market failures. We discuss market failures that may lead to cybersecurity investment levels that are insufficient from society\u0026rsquo;s perspective and other forms of unsafe behaviour in cyber space.\u003c/li\u003e\r\n\u003cli\u003eBehavioural economics for information security, policy and regulation. We discuss available economic tools to better align the incentives for cybersecurity, including better security metrics, cyber insurance/risk transfer, information sharing, and liability assignment.\u003c/li\u003e\r\n\u003c/ol\u003e\r\n\u003cp\u003eAfter finishing this course, you will be able to apply economic analysis and data analytics to cybersecurity. You will understand the role played by incentives on the adoption and effectiveness of security mechanisms, and on the design of technical, market-based, and regulatory solutions to different security threats.\u003c/p\u003e\r\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"])</script><script>self.__next_f.push([1,"45a:T5bc,\u003cp\u003eThis module is currently offered in the Lyles School of Civil Engineering as part of the CE57200 “Prestressed Concrete Design” 3-Credits (CR) course in the area of structural engineering available to senior undergraduate/graduate students. It integrates science and engineering principles to design prestressed concrete members and structural systems. The application of scientific and engineering knowledge is demonstrated in solving engineering problems associated with the design of precast prestressed building members both composite and non-composite for superimposed loads, and one-way post-tensioned floor slabs systems bonded and unbonded also composite and non-composite for superimposed loads. Design of pretensioned Hollow-Core slabs, Double-Tee and I-Beam members, and one-way post-tensioned floor slabs is exercised using current building code requirements to provide experiences in realistic design practice. The following subjects are used to solve engineering problems: calculus and differential equations; use of computer tools, data manipulation, statistical analysis, numerical calculation, and reinforced concrete design principles.\u003c/p\u003e\n\u003cp\u003eThe course is developed in three modules each of 1-CR. Module 2 (this module) is focused on the essentials of design of pretensioned concrete structures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis course is available to practicing engineers for 1.5 CEUs for learners completing the course on the verified track.\u003c/strong\u003e\u003c/p\u003e45b:T5be,\u003cp\u003eThis module is currently offered in the Lyles School of Civil Engineering as part of the CE57200 “Prestressed Concrete Design” 3-Credits (CR) course in the area of structural engineering available to senior undergraduate/graduate students. It integrates science and engineering principles to design prestressed concrete members and structural systems. The application of scientific and engineering knowledge is demonstrated in solving engineering problems associated with the design of precast prestressed building members both composite and non-composite"])</script><script>self.__next_f.push([1," for superimposed loads, and one-way post-tensioned floor slabs systems bonded and unbonded also composite and non-composite for superimposed loads. Design of pretensioned Hollow-Core slabs, Double-Tee and I-Beam members, and one-way post-tensioned floor slabs is exercised using current building code requirements to provide experiences in realistic design practice. The following subjects are used to solve engineering problems: calculus and differential equations; use of computer tools, data manipulation, statistical analysis, numerical calculation, and reinforced concrete design principles.\u003c/p\u003e\n\u003cp\u003eThe course is developed in three modules each of 1-CR. Module 3 (this module) is focused on the essentials of design of post-tensioned concrete structures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis course is available to practicing engineers for 1.5 CEUs for learners completing the course on the verified track.\u003c/strong\u003e\u003c/p\u003e45c:T5fe,\u003cp\u003eThis module is currently offered in the Lyles School of Civil Engineering as part of the CE57200 “Prestressed Concrete Design” 3-Credits (CR) course in the area of structural engineering available to senior undergraduate/graduate students. It integrates science and engineering principles to design prestressed concrete members and structural systems. The application of scientific and engineering knowledge is demonstrated in solving engineering problems associated with the design of precast prestressed building members both composite and non-composite for superimposed loads, and one-way post-tensioned floor slabs systems bonded and unbonded also composite and non-composite for superimposed loads. Design of pretensioned Hollow-Core slabs, Double-Tee and I-Beam members, and one-way post-tensioned floor slabs is exercised using current building code requirements to provide experiences in realistic design practice. The following subjects are used to solve engineering problems: calculus and differential equations; use of computer tools, data manipulation, statistical analysis, numerical calculation, and reinforced"])</script><script>self.__next_f.push([1," concrete design principles.\u003c/p\u003e\n\u003cp\u003eIn the edX platform, the course is developed in three modules each of 1-credit. \u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFundamentals of Prestressed Concrete (this course)\u003c/li\u003e\n\u003cli\u003ePretensioned Structures\u003c/li\u003e\n\u003cli\u003ePost-Tensioned Structures\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eThis course is available to practicing engineers for 1.5 CEUs for learners completing the course on the verified track.\u003c/strong\u003e\u003c/p\u003e45d:T5f7,\u003cp\u003eOpen justice is a growing movement to use new technologies, including big data, digital platforms, blockchain and more, to improve our legal system by making the workings of our legal institutions easier to understand, scrutinize and, we hope, improve. Thanks to new tools the opportunity to make improvements has never been greater. This course is designed to help the public entrepreneur – passionate individuals like you. Whether you are a lawyer, a judge, a technologist or just a concerned citizen – the course will help you to use new technologies to take action to increase efficiency, improve equity, fight corruption and enhance legitimacy in the third branch of government.\u003c/p\u003e\n\u003cp\u003eInstead of long lectures, this online course consists of ten short modules which will serve as brief introductions to different aspects of open justice. These ‘mini-lectures’ of ten minutes each are combined with interviews with leading practitioners from around the world who are collecting the data, conducting the analysis, creating the apps, and building the movement that helps us all to better know and assert our rights.\u003c/p\u003e\n\u003cp\u003eWe wish to acknowledge the \u003ca href=\"https://urldefense.proofpoint.com/v2/url?u=https-3A__www.te.gob.mx_\u0026amp;d=DwMFaQ\u0026amp;c=slrrB7dE8n7gBJbeO0g-IQ\u0026amp;r=Av2Uer0Sse6QfMPClpgMJNA5qD3HhcqEOVamTfZzYys\u0026amp;m=5IlWWXYRr_q-MNEhYZCLvmr5oSZQRF3dWJE-Z3-EMw0\u0026amp;s=srqEW_cjJceo3RzqW1fU--7M0hWRYiQzbUWN5OAFenI\u0026amp;e=\"\u003eCommission of the Mexican Federal Electoral Court\u003c/a\u003e as the founder of this MOOC.\u003c/p\u003e45e:T582,\u003cp\u003e\u003cstrong\u003eThis course will be retired.\u003c/strong\u003e\u003c/p\u003e\r\n\u003cp\u003eHigh-quality information is the key to succ"])</script><script>self.__next_f.push([1,"essful management of businesses. Despite the large quantity of data that is collected by organizations, managers struggle to obtain information that helps them make decisions. While \u003cem\u003eoperational processing\u003c/em\u003e systems help capture, store, and manipulate data to support day-to-day operations of organizations, \u003cem\u003ereconciled systems\u003c/em\u003e -- sometimes referred to as data warehouses or business intelligence (BI) systems -- support the analysis of data, thus, enabling decision making.\u003c/p\u003e\r\n\u003cp\u003eWith the advent of big data systems, organizations have turned to enterprise data management frameworks to manage and gain insights from the vast amount of data collected. While storage costs themselves are relatively affordable, the bigger challenge has been finding an appropriate mechanism to manage the data as many technologies (e.g., relational databases, data warehouses) have limitations on the amount of data that can be stored.\u003c/p\u003e\r\n\u003cp\u003eThis course focuses on realizing the business advantage and business potential of operational, reconciled, and big data systems as well as data assets in supporting enterprise data management strategies and enterprise data analytics.\u003c/p\u003e\r\n\u003cp\u003eVerified Learners will need to purchase a textbook in order to successfully complete the course. See the FAQ for details.\u003c/p\u003e45f:T520,\u003cp\u003eIn today’s data-driven landscape, proficiency in data analytics and visualization is indispensable for professionals across industries. These skills equip employees with the ability to meaningfully contribute to an organization’s insights by empowering them to make well-informed decisions and identify key trends. Data-driven insights can streamline operations by identifying bottlenecks, optimizing processes, and reducing inefficiencies.\u003c/p\u003e\r\n\r\n\u003cp\u003eData isn’t only about specific insights though. Looking at data sparks innovation and explores areas of untapped potential. Using a systematic approach to problem-solving, employees can more readily identify root causes of issues in order to enhance transparency and ac"])</script><script>self.__next_f.push([1,"countability. When decisions are based on data, employees understand the rationale behind choices and can take ownership of their roles.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis self-paced, online certificate consists of four courses that position professionals to understand and present meaningful data. Each course features interactive videos to help you understand both the analytical concepts and the software utilized. You will apply the concepts taught using a separate data source in order to practice and gain the confidence necessary to connect, explore, and analyze data sources into the future.\u003c/p\u003e460:T609,\u003cp\u003eBuilding on the concepts from the first course in the Six Sigma Program, Define and Measure, in this course, you will learn how to statistically analyze data with the Six Sigma methodology using inferential statistical techniques to determine confidence intervals and to test hypotheses based on sample data. You will also review cause and effect techniques for root cause analysis.\u003c/p\u003e\n\u003cp\u003eYou will learn how to perform correlation and regression analyses in order to confirm the root cause and understand how to improve your process and plan designed experiments.\u003c/p\u003e\n\u003cp\u003eYou will learn how to implement statistical process control using control charts and quality management tools, including the 8 Disciplines and the 5 Whys to reduce risk and manage process deviations.\u003c/p\u003e\n\u003cp\u003eTo complement the lectures, learners are provided with interactive exercises, which allow learners to see the statistics \"in action.\" Learners then master statistical concepts by completing practice problems. These are then reinforced using interactive case studies, which illustrate the application of the statistics in quality improvement situations.\u003c/p\u003e\n\u003cp\u003eUpon successful completion of this program, learners will earn the TUM Lean and Six Sigma Yellow Belt certification, confirming mastery of Lean Six Sigma fundamentals to a Green Belt level. The material is based on the American Society for Quality (www.asq.org) Body of Knowledge up to a Green Belt Level. The Prof"])</script><script>self.__next_f.push([1,"essional Certificate is designed as preparation for a Lean Six Sigma Green Belt exam.\u003c/p\u003e461:Td7a,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cstrong\u003eCitizens \u0026amp; scientists join forces\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCitizen science has become an essential approach to address urgent sustainability challenges. It is critical that science and society work together. Science becomes more relevant as scientists and citizens upscale data collection, co-create knowledge and collectively act on outcomes.\u003c/p\u003e\n\u003cp\u003e\" \u003cem\u003eTransformative citizen science goes a step further. It is not just about knowing and 'doing things better' but also about 'doing better things' altogether\u003c/em\u003e.\"\u003c/p\u003e\n\u003cp\u003eFor example, the \u003cstrong\u003eBigO\u003c/strong\u003e project ‘Big data against childhood Obesity‘ massively increased data collection, reaching out to over twenty thousand children from different countries to contribute over 107.000 pictures and other data on the impact of lifestyle and living conditions on health and well-being. It led to more awareness of schools on promotion of healthy behaviours across different income groups, and sparked changes on public health policy.\u003c/p\u003e\n\u003cp\u003eIn this course you will explore several such successful examples of citizen science, including projects on:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ewater and air quality\u003c/li\u003e\n\u003cli\u003edisease\u003c/li\u003e\n\u003cli\u003eclimate change and\u003c/li\u003e\n\u003cli\u003eloss of green spaces and biodiversity\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eTransforming society together\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWell-designed citizen science enables citizens to collect, interpret and share data. Transformative citizen science goes a step further. It allows citizens to contribute to change on issues that matter to them. It is a process that challenges our assumptions and values, to co-create innovative solutions that can tackle sustainability challenges. It is not just about knowing and doing things better but also about doing better things all together\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eYour project and WUR's Citizen Science Hub\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCitizen science can be used in a range of contexts, involving multiple stakeholders representing the world of civic society, governance and education. In this course you will develop a deep understanding of CS, the diverse ways of using it in practice, and the principles that make it work.\u003c/p\u003e\n\u003cp\u003eAs course participant you gain access to a community of practice established by the Wageningen University \u0026amp; Research’s Citizen Science Hub, in which practitioners engage and support the development of new projects from across the globe.\u003c/p\u003e\n\u003cp\u003eHaving this expertise will help you in gaining a position in governmental and non-governmental organisations as well as in institutions of higher education who are exploring ways to develop and utilize CS in an ambition to contribute to the transformation of the society at large. In other words, organisations and institutions that go beyond self-gain, with an intrinsic drive to help communities in becoming more sustainable, including the well-being of the collective and the planet. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFor Whom\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAre you:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ea policy maker interested in developing broadly supported and informed solutions to sustainability issues?\u003c/li\u003e\n\u003cli\u003ea concerned citizen or professional working in an NGO or activist organization wishing to support citizens in working towards healthier, greener and more sustainable communities?\u003c/li\u003e\n\u003cli\u003eor an academic wishing to have more societal impact with your research by working more closely with citizens?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis course will help you take the next steps in making citizen science work for you.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"462:T6cc,\u003cp\u003eIn this course you will have the ability to:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eDevelop a plan for initiating and sustaining a citizen science project, including strategies and skills for communication, data collection and management, and ethics.\u003c/li\u003e\n\u003cli\u003eGain insight in the advantages of different types of citizen science projects, ranging from science-oriented to action-oriented, and the challenges of balancing citizen participation and scientific rigour.\u003c/li\u003e\n\u003cli\u003eUnderstand trade-offs in engaging citizens in each project phase from goal formulation to project evaluation. Practical examples and tools are given for data collection and storage, and communicating for impact.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eThe course is composed of theoretical and practical assignments that will enable you to:\u003c/strong\u003e\u003cbr /\u003e\n- Understand the basic characteristics of Citizen Science\u003cbr /\u003e\n- Identify the strategic decisions of a citizen science project and activities for citizen involvement.\u003cbr /\u003e\n- Identify requirements and methods for data monitoring, storing, sharing, analysis, interpretation.\u003cbr /\u003e\n- Assess and communicate the process, outcomes and impact of a citizen science project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePractical assignments:\u003c/strong\u003e\u003cbr /\u003e\nCourse staff and peers will give you feedback on your own project plan. This will help you master the iterative design-implementation cycle of goal-design-collect-evaluate. You will learn to:\u003cbr /\u003e\n- Determine your sustainability challenge as a citizen science opportunity.\u003cbr /\u003e\n- Develop a strategy for your Transformative Citizen Science Project.\u003cbr /\u003e\n- Plan the data monitoring, storing, sharing, analysis, interpretation of your project.\u003cbr /\u003e\n- Assess and communicate the process, outcomes and impact of your project.\u003c/p\u003e463:Tea6,"])</script><script>self.__next_f.push([1,"\u003cp\u003eAn analysis of nationally representative survey data on the prevalence of violence against children in 96 countries estimates that 1 billion children globally – over half of all children aged 2–17 years – have experienced emotional, physical or sexual violence in the past year. Despite its high prevalence, violence against children is often hidden, unseen or under-reported. Its hidden nature is well documented – for example, a meta-analysis of global data finds self-reported child sexual abuse 30 times higher and physical abuse 75 times higher than official reports would suggest.\u003c/p\u003e\n\u003cp\u003eThe immediate and long-term public health consequences and economic costs of violence against children undermine investments in education, health, and child well-being, and erode the productive capacity of future generations. Exposure to violence at an early age can impair brain development and damage other parts of the nervous system, as well as the endocrine, circulatory, musculoskeletal, reproductive, respiratory and immune systems, with lifelong consequences. Strong evidence shows that violence in childhood increases the risks of injury; HIV and other sexually transmitted infections; mental health problems; delayed cognitive development; poor school performance and dropout; early pregnancy; reproductive health problems; and communicable and noncommunicable disease.\u003c/p\u003e\n\u003cp\u003eINSPIRE is an evidence-based resource for everyone committed to preventing and responding to violence against children and adolescents – from government to grassroots, and from civil society to the private sector. \u003cstrong\u003eThis MOOC represents a select group of strategies based on the best available evidence to help countries and communities intensify their focus on the prevention programmes and services with the greatest potential to reduce violence against children.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe seven strategies are:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eI\u003c/strong\u003e mplementation and enforcement of laws\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eN\u003c/strong\u003e orms and values\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eS\u003c/strong\u003e afe environments\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eP\u003c/strong\u003e arent and caregiver support\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eI\u003c/strong\u003e ncome and economic strengthening\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eR\u003c/strong\u003e esponse and support services\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eE\u003c/strong\u003e ducation and life skills\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis MOOC was developed by the \u003ca href=\"http://www.cpcnetwork.org/\"\u003eCare and Protection of Children (CPC) Learning Network\u003c/a\u003e at Columbia University Mailman School of Public Health in partnership with the \u003ca href=\"https://www.who.int/\"\u003eWorld Health Organization (WHO)\u003c/a\u003e, \u003ca href=\"https://www.end-violence.org/\"\u003eEnd Violence Against Children: The Global Partnership\u003c/a\u003e; \u003ca href=\"https://www.togetherforgirls.org/\"\u003eTogether for Girls\u003c/a\u003e, the \u003ca href=\"https://www.unicef.org/\"\u003eUnited Nations Children’s Fund (UNICEF)\u003c/a\u003e, \u003ca href=\"https://www.unodc.org/\"\u003eUnited Nations Office on Drugs and Crime (UNODC)\u003c/a\u003e, \u003ca href=\"https://www.worldbank.org/en/home\"\u003ethe World Bank,\u003c/a\u003e and multiple national and international civil society organizations (agencies with a long history of galvanizing a consistent, evidence-based approach to preventing violence against children). Additional contributions were provided by the \u003ca href=\"https://urldefense.proofpoint.com/v2/url?u=https-3A__www.cdc.gov_violenceprevention_childabuseandneglect_vacs_index.html\u0026d=DwMFAg\u0026c=G2MiLlal7SXE3PeSnG8W6_JBU6FcdVjSsBSbw6gcR0VzYHa0heH42N3FCy53AEIz\u0026r=xgiQTWJ-VxdqtYr6p95CRx_DgDNCwyp5KIIhvY-5D3w\u0026m=TUlceTKWT9Ub9I7BZXBQNO1-VJxaIiNukvp9b1-cOC2oF424ff-h0JO6NbYlEB7E\u0026s=EC01NR503dYbTlvyflWfqsmBfMxUWX3iE9WW7rct7CM\u0026e=\"\u003eU.S. Centers for Disease Control and Prevention (CDC)\u003c/a\u003e and the \u003ca href=\"https://www.usaid.gov/\"\u003eU.S. Agency for International Development (USAID)\u003c/a\u003e.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"464:T716,\u003cp\u003eThe logit model is the workhorse of choice modelers. But it has some limitations. In particular, some assumptions used to derive it may not be consistent with the behavioral reality. It may lead to erroneous forecast. We illustrated using the so-called \"red bud-blue bus\" paradox, and Multivariate Extre Value models, addressing some of these issues, are introduced.\u003c/p\u003e\n\u003cp\u003eThe sampling procedure used to collect choice data has a critical impact on the model estimation procedure. We introduce classical sampling procedures, and analyze in details the implications for model estimation. \u003c/p\u003e\n\u003cp\u003eIn our quest to address the limitations of the logit model, we introduce a new family of models, based on \"mixtures\". We define what mixtures are, how they can be calculated. We investigate several important modeling assumptions that they can cover.\u003c/p\u003e\n\u003cp\u003eRandom utility relies on the rationality assumption for the decision-makers. We show that human beings are not always consistent with this assumption, and may exhibit apparent irrationality. Hybrid choice models are able to capture subjective dimensions of the choice process, using variables that are called \"latent variables\". \u003c/p\u003e\n\u003cp\u003eChoices evolve over time. Individuals learn, develop habits. In order to capture that, it is necessary to observe individuals over time, and to collect so-called \"panel data\". The introduction of the time dimension into choice models has some econometrics implications, that we describe in detail. \u003c/p\u003e\n\u003cp\u003eWho needs choice models, when machine learning algorithms are so powerful and pervasive? In this last chapter, we introduce the similarities and differences between machine learning and discrete choice, and we discuss some potential limitations of machine learning in the context of the analysis of choice data.\u003c/p\u003e465:T681,\u003cp\u003eThis Professional Certificate program takes you right from the fundamentals and gradually into the powerful, fast and popular object-oriented programming language of C++. Through a mix of hands-on and highly inter"])</script><script>self.__next_f.push([1,"active activities including detailed presentations, comprehensive code demo videos, quizzes and multiple coding assignments, you will be introduced to the highly in-demand world of Data Structures \u0026 Algorithms in the C++ universe. This program accommodates both learners with some familiarity in programming but also those who are new to programming. A C++ learning experience for everyone.\u003c/p\u003e\r\n\r\n\u003cp\u003eFeaturing a number of auto-evaluated C++ coding activities that include constructors, conditional operations, iterators and recursion, this program will enable you to build strong problem-solving and logic building skills. You will also learn advanced C++ programming activities like dynamic memory management.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe coding exercises will refine your understanding of various OOP implementations like encapsulation and specialization in C++ and will ensure you are sensitive to best practices such as memory allocation and memory management while designing code efficient programs.\u003c/p\u003e\r\n\r\n\u003cp\u003eDriven learners will find that they can skill up rapidly from simple to complex coding using the right design patterns in C++ and will be able to implement linear and non-linear data structures and object oriented programming concepts in C++.\u003c/p\u003e\r\n\r\n\u003cp\u003eThese skills will help you become a much sought-after C++ developer and you will find yourself positioned to take advantage of the numerous opportunities available in the world of technology.\u003c/p\u003e466:T939,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cem\u003ePlease Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWelcome to Data Engineering Basics. This course is designed to familiarize you with data engineering concepts, ecosystem, lifecycle, processes, and tools.\u003c/p\u003e\n\u003cp\u003eThe Data Engineering Ecosystem includes several different components. It includes data, data repositories, data integration platforms, data pipelines, different types of languages, and BI and Reporting tools. Data pipelines gather raw data from disparate data sources. Data repositories, such as relational and non-relational databases, data warehouses, data marts, data lakes, and big data stores, store and process this data. Data Integration Platforms combine data into a unified view for secure and easy access by data consumers. Data consumers use BI, reporting, and analytical tools on data so they can glean insights for better decision-making. You will learn about each of these components in this course.\u003c/p\u003e\n\u003cp\u003eA typical Data Engineering lifecycle includes architecting data platforms and designing data stores. It also includes the process of gathering, importing, wrangling, cleaning, querying, and analyzing data. Systems and workflows need to be monitored and finetuned for performance at optimal levels. In this course, you will learn about the architecture of data platforms and things you need to consider in order to design and select the right data store for your needs. You will also learn about the processes and tools a data engineer employs in order to gather, import, wrangle, clean, query, and analyze data.\u003c/p\u003e\n\u003cp\u003eThrough a series of hands-on labs, you will be guided to provision a data store on IBM cloud, prepare and load data into the data store, and perform some basic operations on data.\u003c/p\u003e\n\u003cp\u003eData Engineering is recognized as one of the fastest-growing fields today. The career opportunities available, and the different paths you can take to become a data engineer, are discussed in the course. Seasoned data professionals advice you on the practical and day-to-day aspects of being a data engineer and the skills and qualities employers look for in a data engineer.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"467:T48d,\u003cp\u003eThis course, presented by the Statistics Department, introduces participants to the underlying concepts, definitions, and methodology for the compilation of Financial Soundness Indicators (FSIs). FSIs were developed by the IMF in the late 1990s and currently, more than 140 countries compile and report FSIs to the IMF. These indicators are widely used by researchers, analysts, and policymakers around the world to monitor the soundness of the financial system as a whole from a macroprudential perspective, as well as by IMF staff in financial stability analysis and surveillance. This course covers the history of the FSIs and their application in surveillance and macroprudential analysis, the conceptual framework for the FSIs, the data collection process for FSI compilation and its underlying aggregation and consolidation methodologies as well as the application of core and additional FSIs in macroprudential analysis. An important reference throughout this course is the Financial Soundness Indicators Compilation Guide revised in 2019. The 2019 FSIs Guide is the ultimate authority on FSI concepts and methods and is the foundation of this course.\u003c/p\u003e468:T6c8,\u003cp\u003eData structures play a central role in computer science and are the cornerstones of efficient algorithms. Knowledge in this area has been at the kernel of related curriculums. This course aims at exploring the principles and methods in the design and implementation of various data structures and providing students with main tools and skills for algorithm design and performance analysis. Topics covered by this course range from fundamental data structures to recent research results. \"Data Structures and Algorithm Design Part I\" is an introductory course focusing on basic data structures, including vectors, lists, stacks, queues, binary trees, and graphs. They are important in programming practice, as well as fundamental to our advanced course: \"Part II.\"\u003c/p\u003e\n\u003cp\u003eData structures are a key component of computer science and a necessary foundation for buil"])</script><script>self.__next_f.push([1,"ding efficient algorithms. The knowledge it covers has always been at the core of the curriculum system of related majors. This course aims to focus on the design and implementation of various data structures and reveal the regular principles and methods and techniques; at the same time, it aims to enable students to understand and master the main routines and techniques for algorithm design and performance analysis. The topics taught range from basic data structures to recent research results. This semester's Data Structure (Part 1) is an introductory course on data structures, focusing on basic data structures such as vectors, lists, stacks, queues, binary trees, graphs, etc. Structure (Part 2)\" provides the basis. For more detailed introduction, please see: http://dsa.cs.tsinghua.edu.cn/~deng/ds/mooc/, or check the FAQ column after joining this course.\u003c/p\u003e469:T49b,\u003cp\u003eEveryone involved in higher education has questions. Students want to know how they’re doing and which classes they should take. Faculty members want to understand their students’ backgrounds and to learn whether their teaching techniques are effective. Staff members want to be sure the advice they provide is appropriate and find out whether college requirements accomplish their goals. Administrators want to explore how all of their students and faculty are doing and to anticipate emerging changes. The public wants to know what happens in college and why.\u003c/p\u003e\n\u003cp\u003eEveryone has questions. We have the chance to help them find answers.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePractical Learning Analytics\u003c/em\u003e has a specific goal: to help us collectively ponder learning analytics in a concrete way. To keep it practical, we will focus on using traditional student record data, the kinds of data every campus already has. To make it interesting, we will address questions raised by an array of different stakeholders, including campus leaders, faculty, staff, and especially students. To provide analytic teeth, each analysis we discuss will be supported by both realistic data and sampl"])</script><script>self.__next_f.push([1,"e code.\u003c/p\u003e46a:T47a,\u003cp\u003eUpon completion, learners will be able to \u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eExplain how audit plays a role in enforcing compliance.\u003c/li\u003e\n\u003cli\u003eContrast the different types of treatments available to manage compliance. \u003c/li\u003e\n\u003cli\u003eIdentify the legislative provisions needed to support an audit and verification program.\u003c/li\u003e\n\u003cli\u003eDistinguish the key elements of effective organization and governance for an audit and verification program.\u003c/li\u003e\n\u003cli\u003eAssess the staffing requirements for an audit and verification program, including the number of audit staff, training needs, skillsets, and how to evaluate individual performance. \u003c/li\u003e\n\u003cli\u003eUnderstand why segregation of duties is important and the role of headquarters in managing the audit and verification program. \u003c/li\u003e\n\u003cli\u003eRecognize the tools and systems required for the implementation of an audit program, including case selection, audit types and methods, audit case management and audit quality assurance.\u003c/li\u003e\n\u003cli\u003eEstablish the different data requirements for performance measurement and analysis. \u003c/li\u003e\n\u003cli\u003eDefine the audit process, including planning, conducting, recording, and finalizing an audit.\u003c/li\u003e\n\u003c/ul\u003e46b:T824,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIn this free online course you will learn fundamentals of third dimension 3D GIS mapping concepts and tools to visualize, analyze, and interpret geospatial data in 3D, enabling you to reveal patterns and relationships for more effective problem-solving and decision making. Short video tutorials and applications will move you through concepts in a manageable way.\u003c/p\u003e\n\u003cp\u003eMaps are graphic representations of reality and help us understand and navigate the world around us. Maps can incorporate 3D content through contours, hillshading (3D modeling), and profile views.\u003c/p\u003e\n\u003cp\u003eWhen the data include the third dimension, ArcGIS Scene mapping software allows us to navigate through the data in 3D space. This makes the data and the problem more understandable and reveals new visual insights. Data visualization and processing of the data sets in 3D allows us to include real-world elements in the analysis such as undulation of terrain and 3D extent of trees, building, and subsurface geology.\u003c/p\u003e\n\u003cp\u003eOur world is three dimensional and many GIS applications now require 3D analysis. Esri’s ArcGIS Scene allows us to visualize GIS data on a world sphere. It provides a variety of tools for orientation, navigation, search, data preparation, building workflows and spatial analysis.\u003c/p\u003e\n\u003cp\u003e3D mapping and 3D visualization is used across a variety of industries, stakeholders, and academic disciplines. Use cases and concentrations include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eMonitoring forest stand height and age\u003c/li\u003e\n\u003cli\u003eUrban planning and 3D visualization\u003c/li\u003e\n\u003cli\u003eVolumetric analysis of resource extraction from a mine or water level in a reservoir\u003c/li\u003e\n\u003cli\u003eWatershed delineation to assess inundation risk, land management, environmental monitoring\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eVerified track learners will receive one-year ArcGIS Pro Desktop (Windows) license with support only for tools used in the course in addition to unlimited course access and a verified certificate.\u003c/p\u003e\n\u003cp\u003eSign up for this online course or full GIS Essentials certificate today to expand your skillset in geographic information systems.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"46c:T4d0,\u003cp\u003eIn this course you will learn to use some mathematical tools that can help predict and analyze sporting performances and outcomes. This course will help coaches, players, and enthusiasts to make educated decisions about strategy, training, and execution. We will discuss topics such as the myth of the Hot Hand and the curse of the Sports Illustrated cover; how understanding data can improve athletic performance; and how best to pick your Fantasy Football team. We will also see how elementary Calculus provides insight into the biomechanics of sports and how game theory can help improve an athlete’s strategy on the field. \u003c/p\u003e\n\u003cp\u003eIn this course you will learn:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eHow a basic understanding of probability and statistics can be used to analyze sports and other real life situations.\u003c/li\u003e\n\u003cli\u003eHow to model physical systems, such as a golf swing or a high jump, using basic equations of motion.\u003c/li\u003e\n\u003cli\u003eHow to best pick your Fantasy Football, March Madness, and World Cup winners by using ranking theory to help you determine athletic and team performance. \u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBy the end of the course, you will have a better understanding of math, how math is used in the sports we love, and in our everyday lives.\u003c/p\u003e46d:T618,\u003cp\u003eOrganizations have more data at their disposal today than ever before. The vast amount of data that organizations are capturing, along with their desire to extract meaningful insights is driving an urgent demand for Data Engineers.\u003c/p\u003e\r\n \r\n\u003cp\u003eData Engineers play a fundamental role in harnessing data that enable organizations to apply business intelligence for making informed decisions. Today’s Data Engineers require a broad set of skills to develop and optimize data systems and make data available to the organization for analysis.\u003c/p\u003e\r\n \r\n\u003cp\u003eThis Professional Certificate provides you the job-ready skills you will need to launch your career as an entry level data engineer.\u003c/p\u003e \r\n\r\n\u003cp\u003eUpon completing this Professional Certificate, you will have extensive knowledge and practical experi"])</script><script>self.__next_f.push([1,"ence with cloud-based relational databases (RDBMS) and NoSQL data repositories, working with Python, Bash and SQL, processing big data with Apache Hadoop and Apache Spark, using ETL (extract, transform and load) tools, creating data pipelines, using Apache Kafka and Airflow, designing, populating, and querying data warehouses and utilizing business intelligence tools.\u003c/p\u003e\r\n \r\n\u003cp\u003eWithin each course, you’ll gain practical experience with hands-on labs and projects for building your portfolio. In the final Capstone project, you’ll apply your knowledge and skills attained throughout this program and demonstrate your ability to perform as a Data Engineer.\u003c/p\u003e \r\n \r\n\u003cp\u003eThis program does not require any prior data engineering or programming experience.\u003c/P\u003e46e:Tcc1,"])</script><script>self.__next_f.push([1,"\u003cp dir=\"ltr\"\u003eThis course provides research-based and on-the-ground tools for students, community planners, decision-makers, energy professionals, and interested citizens to improve and implement stronger and more resilient renewable energy systems in Arctic communities. Through a framework combining renewable energy in microgrids, and Food, Energy, and Water (FEW) security and infrastructure, this course synthesizes concepts into a holistic approach to community planning, improvement, and resiliency.\u003cbr\u003e\u003c/br\u003e\u003cbr\u003e\u003c/br\u003e\u003c/p\u003e\n\n\u003cul\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eLearn about existing and emerging types of renewable energy and clean energy sources and technologies and explore examples from Alaska, including solar energy, wind energy, geothermal, biomass, and hydropower facilities. Explore sustainable energy and alternative energy concepts.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eExamine underlying causes of food, energy, and water insecurity in Arctic, subarctic, and northern rural communities as it relates to energy use.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eGain insights into Arctic and subarctic lifestyles, including the roles and impacts of wild harvests, plant-based foods, and health disparities.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eLearn about food, energy, and water security and analyze the interactions among food, energy, and water usage, including for example: energy and water use in the production, transportation, and storage of food; energy usage in treating drinking water and wastewater for human health; water demands and fuel costs for electricity generation; appropriate food systems, sources of energy, and water resource usage and allocation; climate change impacts, fossil fuels and environmental impacts.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eGain specialized expertise on a variety of Arctic energy generation issues affecting its residents and Indigenous peoples, from engineering to social science to traditional community knowledge.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cp dir=\"ltr\"\u003eLearn the key concepts with practical, Alaska-focused examples.\u003c/p\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cp dir=\"ltr\"\u003eUse real wind speed data from wind turbines and photovoltaic data from solar panels with various analysis tools to make community energy assessments related to energy production.\u003c/p\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eApply a Food-Energy-Water (FEW) nexus approach to guide decisions about renewable power and selection of an energy mix from hydroelectric power to solar power and wind power.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eGain skills that enable a move toward a new energy future that minimizes greenhouse gas emissions such as carbon dioxide and carbon emissions through a focus on renewable energy sources that maximize energy efficiency and clean electricity production.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eLearn from National Science Foundation-funded researchers and staff from a variety of disciplines at the University of Alaska Fairbanks, the University of Alaska Anchorage, the University of Calgary, Stanford, and the private sector. Connections with United Nations Sustainable Development Goals. \u003c/p\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"ltr\"\u003e\n\nThis project is funded by the National Science Foundation, Award #1740075 INFEWS/T3: Coupling infrastructure improvements to food-energy-water system dynamics in small cold region communities: MicroFEWs.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"46f:T4bf,\u003cul\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eExplore current states of food, energy, and water systems in rural Alaska, with broader applications to the Arctic.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eCompare mature and emerging renewable energy technologies such as hydro, photovoltaics, and geothermal energy, with examples from Alaska.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eDefine how food, energy, and water impacts community well-being in the Arctic and beyond.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eAnalyze the feedback between renewable energy power generation and the local drivers of food, energy, and water security. \u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eExplore and discuss scientific and social issues that arise when utilizing food, energy, and water resources. \u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eOrganize and quantify food and water security data.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eUse renewable energy resource data to create energy assessments.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eLearn how modular food and water applications can optimize renewable energy inputs in the Arctic and beyond.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eApply decision making methodologies to develop community level recommendations based on resource energy assessments combined with food and water security information.\u003c/p\u003e\u003c/li\u003e\n\u003c/ul\u003e470:T786,\u003cp\u003eThe Data Structures \u0026amp; Algorithms course begins with a review of some important Java techniques and nuances in programming. The course requires some prior knowledge of Java and object-oriented programming, but not in data structures or algorithms. This course introduces you to time complexity, and threads this concept throughout all data structures and algorithms presented in the course. You will work with the principles of data storage in Arrays and LinkedList nodes. You will program the low-level data structures: Singly, Circular and Doubly LinkedLists; and explore edge cases and efficiencies. LinkedLists and Arrays are used to implement Abstract Data Types, ADTs: Stacks, Queues and Deques. Harnessing the power of recursion to move through these data structures is necessary. As the size changes i"])</script><script>self.__next_f.push([1,"n your data structures, it becomes important to examine amortized analysis of the operations.\u003c/p\u003e\n\u003cp\u003eThe course design has several components and is built around modules. A module consists of a series of short (3-5 minute) instructional videos. In between the videos, there are textual frames with additional content information for clarification, as well as video errata dropdown boxes. All modules include an Exploratory Lab that incorporates a Visualization Tool specifically designed for this course. The lab includes discovery questions that lead you towards delving deeper into the efficiency of the data structures and examining the edge cases. This is followed by a set of comprehension questions on topics covered in the module that count for 10% of your grade. The modules end with Java coding assignments which are 60% of your grade. Lastly, you'll complete a course exam, which counts for the remaining 30% of your grade.\u003c/p\u003e\n\u003cp\u003eThis is a great course that has been derived from the on-campus version of CS1332 at the Georgia Institute of Technology, and is backed with an impressive reputation.\u003c/p\u003e471:T6d9,\u003cp\u003eMachine learning methods have revolutionized many aspects of healthcare, from new models that help clinicians make more informed decisions to new technologies that enable individual patients to better manage their own health. Since the 1950s with Kaiser’s first computerized records for chest X-ray reports and blood test results, and the introduction of the pacemaker, clinicians have realized the potential of algorithms to save lives. This rich history of machine learning for healthcare informs groundbreaking research today, as new advances in image processing, deep learning, and natural language processing are transforming the healthcare industry.\u003c/p\u003e\n\u003cp\u003eUsing machine learning to improve patient outcomes requires that we understand the human consequences of machine learning, such as transparency, fairness, regulation, ease of deployment, and integration into clinical workflows. Throughout this course, we retur"])</script><script>self.__next_f.push([1,"n to the question: how can machine learning improve healthcare for all?\u003c/p\u003e\n\u003cp\u003eThe course begins with an introduction to clinical care and data, and then explores the use of machine learning for risk stratification and diagnosis, disease progression modeling, improving clinical workflows, and precision medicine. For each of these topics we dive into methodological details typically not covered in introductory machine learning courses, such as the foundations of deep learning on imaging and natural language, interpretability of ML models, algorithmic fairness, causal inference and off-policy reinforcement learning.\u003c/p\u003e\n\u003cp\u003eGuest lectures by clinicians and course programming projects with real clinical data emphasize subtleties of working with clinical data and translating machine learning into clinical practice.\u003c/p\u003e472:T41a,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMongoDB is a popular non-relational database that supports the modeling and manipulation of almost any data structure. Because MongoDB stores data in documents rather than relationally, MongoDB is flexible and well-suited to real-world business situations. Users can access data in any language as long as the data structure is native to that language.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIn this hands-on guided project, learn the fundamentals of MongoDB. You’ll use create-read-update-delete (CRUD) operations by creating and making changes to documents with MongoDB. By the end of the project, you will have a solid foundation in MongoDB to continue learning more complex database operations.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis lab provides access to a Cloud-based IDE \u003cstrong\u003ethat\u003c/strong\u003e \u003cstrong\u003e_ _has all of the required software,__ including MongoDB\u003c/strong\u003e , preinstalled. All you need is a recent version of a modern web browser to complete this project.\u003c/p\u003e473:T567,\u003cp\u003eAre you one of those professionals who is curious and wants to learn about financial statements, but is intimidated by financial numbers and jargon? The"])</script><script>self.__next_f.push([1,"n this course is for you!\u003c/p\u003e\n\u003cp\u003eIn this course, we will demystify accounting jargon, help you understand financial statements and analyse them for better decisions. Whatever be your background – marketing, operations, supply chain, strategy, engineering or others, in today’s competitive world, you need to use and interpret crucial financial data for making informed decisions.\u003c/p\u003e\n\u003cp\u003eThis course will enable you to:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eUnderstand the various elements of financial statements\u003c/li\u003e\n\u003cli\u003eApply accounting principles related to its preparation\u003c/li\u003e\n\u003cli\u003eUse tools and techniques to analyse and interpret the key parameters of financial performance\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe course has direct application and high relevance in every professional’s life. Concepts learnt in the course can be applied in day to day management for improving operations and creating value for the organization. You will also be able to assess financial implications of your decisions.\u003c/p\u003e\n\u003cp\u003eThe course will be covered in an easy, simple and interactive manner through various hands-on activities, short cases and easy-to-understand examples.\u003c/p\u003e\n\u003cp\u003eNo previous finance knowledge is needed. Come armed with enthusiasm and curiosity to learn.\u003c/p\u003e474:T75f,\u003cp\u003eIn order to apply technical analysis with confidence, it is important to understand the theory of technical analysis, why it’s a rational approach to market analysis and how this discipline relates to fundamental analysis and investor psychology.\u003c/p\u003e\n\u003cp\u003eTo build that confidence, we begin with an explanation of how technical analysts view the market in terms of supply and demand. We detail how the analyst develops information about the relative strength of the bulls and the bears through price charts and other tools.\u003c/p\u003e\n\u003cp\u003ePrice charts are one of the primary tools of technicians. Charts provide a history of market action and analysts can observe patterns in the chart. As early as the 1930s, analysts determined that certain patterns tended to precede certain price moves. This course reviews those"])</script><script>self.__next_f.push([1," patterns, discusses how to identify the patterns and supplements this with concepts form behavioral finance to explain why the patterns are predictive. Different chart types are presented and important charting concepts are explained from a practical perspective.\u003c/p\u003e\n\u003cp\u003eIn addition to charts, technical analysts also use indicators and various theories to forecast the direction of prices through the study of past market data.\u003c/p\u003e\n\u003cp\u003eWe focus on defining and applying momentum indicators to make buy and sell decisions. Instead of simply explaining and illustrating popular indicators like moving averages, RSI, MACD and stochastics, we review historical back tested results of each indicator so that you can objectively evaluate their performance.\u003c/p\u003e\n\u003cp\u003eIndicators based on the sentiment of various groups are analyzed and breadth indicators are explained.\u003c/p\u003e\n\u003cp\u003eIn addition to indicators, theories applicable to trading are studied.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFree Preview! Access Module 1, 2, and 3 for free. For full course access, upgrade to a verified certificate.\u003c/strong\u003e\u003c/p\u003e475:T629,\u003cp\u003eWelcome to the self paced course, \u003cem\u003eAlgorithms: Design and Analysis\u003c/em\u003e! Algorithms are the heart of computer science, and the subject has countless practical applications as well as intellectual depth.\u003c/p\u003e\n\u003cp\u003eThis specialization is an introduction to algorithms for learners with at least a little programming experience. The specialization is rigorous but emphasizes the big picture and conceptual understanding over low-level implementation and mathematical details. After completing this specialization, you will be well-positioned to ace your technical interviews and speak fluently about algorithms with other programmers and computer scientists.\u003c/p\u003e\n\u003cp\u003eSpecific topics in the course include: \"Big-oh\" notation, sorting and searching, divide and conquer (master method, integer and matrix multiplication, closest pair), randomized algorithms (QuickSort, contraction algorithm for min cuts), data structures (heaps, balanced search trees, hash tables, "])</script><script>self.__next_f.push([1,"bloom filters), graph primitives (applications of BFS and DFS, connectivity, shortest paths).\u003c/p\u003e\n\u003cp\u003eLearners will practice and master the fundamentals of algorithms through several types of assessments. There are 6 multiple choice quizzes to test your understanding of the most important concepts. There are also 6 programming assignments, where you implement one of the algorithms covered in lecture in a programming language of your choosing. The course concludes with a multiple-choice final. There are no assignment due dates and you can work through the course materials and assignments at your own pace.\u003c/p\u003e476:T602,\u003cp\u003eIn this IMFx course you will learn, from hands-on demonstrations, how to price different types of bonds, how to calculate different measures of bond yields and how to compare them across different types of instruments. You will become familiar with the term structure of interest rates, a key ingredient in establishing benchmark rates used to price securities in the markets and a valuable tool for monetary policy design and diagnosis.\u003c/p\u003e\n\u003cp\u003eYou’ll gain an understanding of the firm fundamentals that can explain why a stock price may go up or down, or why it might be higher for one company in comparison to another, you will be able to apply these fundamentals at the economy-wide level to analyze valuations of the stock market as a whole.\u003c/p\u003e\n\u003cp\u003eFinally, you will gain insight into investors’ decisions. You’ll explore the main criteria that an investor uses to determine how to construct the best possible portfolio of risky assets. You will also adopt the perspective of a policymaker interested in understanding how monetary policy affects the risk and return properties of financial investments.\u003c/p\u003e\n\u003cp\u003eIn short, the FMAx course is designed to provide a common language in finance, thus allowing you to interpret and analyze financial data. It will also provide you with a foundation upon which you can proceed to more advanced or policy-oriented training in areas in which macroeconomics and finance meet"])</script><script>self.__next_f.push([1,".\u003c/p\u003e\n\u003cp\u003eFinancial Market Analysis is offered by the IMF with financial support from the Government of Japan.\u003c/p\u003e477:T755,\u003cp\u003eHow healthy is the state of the economy? How can economic policy help support or restore health to the economy? These questions are at the heart of financial programming. In our FPP courses you will learn the building blocks of how to answer to these questions.\u003c/p\u003e\n\u003cp\u003eFinancial programming is a framework to analyze the current state of the economy, forecast where the economy is headed, and identify economic policies that can change the course of the economy.\u003c/p\u003e\n\u003cp\u003eIn Part 1 of the FPP sequence, presented by IMF's Institute for Capacity Development, you will learn the basic skills required to conduct financial programming. The course presents the principal features of the four main sectors that comprise the macroeconomy (real, fiscal, external, and monetary); demonstrates how to read, interpret, and analyze the accounts for these sectors; and illustrates how these sectors are interlinked. (Part 2 of the FPP sequence will cover preparation of a baseline forecast and design of an adjustment program.)\u003c/p\u003e\n\u003cp\u003eDuring the course, economists from the IMF will lead you through the accounts and analysis of an economy. Besides engaging with lecture videos, you will answer questions on the concepts explained, solve short numerical exercises, discuss with fellow participants economic developments in your country, and work with data for a hypothetical country. The reading material will be provided to you.\u003c/p\u003e\n\u003cp\u003eWhether you are a civil servant working on economic issues for your country, a professional working with economic data, or simply interested in better understanding the developments of an economy, this course will provide hands-on training on macroeconomic analysis. We hope that you will join us in this exciting journey!\u003c/p\u003e\n\u003cp\u003eFinancial Programming and Policies, Part 1 is offered by the IMF with financial support from the Government of Japan.\u003c/p\u003e478:T597,\u003cp dir=\"ltr\"\u003eImage analysis is the extra"])</script><script>self.__next_f.push([1,"ction of meaningful information from an image to accomplish a simple task like understanding the landscape or to solve a complex problem like face recognition.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\n\nThis course is focused on image analysis and applications in the area of natural resource management, geography, geology, and environmental studies. Participants will use a variety of remotely sensed data including high resolution aerial photo of earth, Landsat and Terra MODIS satellite data to solve specific problems.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\n\nLearners in this course will learn fundamentals of remote sensing, image processing, and image analysis. Learn image analysis in Esri ArcGIS Pro through hands-on exercises including image display, image interpretation, change detection using spectral indices, supervised and unsupervised image classification for land cover mapping, and effect of arctic warming on vegetation productivity.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\n\nThe course features lessons covering GIS image analysis applications on real-world problems.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\n\nVerified track learners will receive one-year ArcGIS Pro Desktop (Windows) license with support only for tools used in the course in addition to unlimited course access and a verified certificate.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\n\nSign up for this online course or full GIS Essentials certificate today to expand your skill set in geographic information systems.\u003c/p\u003e479:T96e,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis Data Structures \u0026amp; Algorithms course extends beyond linear data structures in CS1332xI to the nonlinear and hierarchical data structures here in CS1332xII. A short Java review is presented on topics relevant to new data structures covered in this course. The course does require prior knowledge of Java, object-oriented programming and linear data structures. Time complexity is threaded throughout the course within all the nonlinear data structures and algorithms.\u003c/p\u003e\n\u003cp\u003eYou will explore the hierarchical data structure of trees. Trees have important properties such as shape and order which are used to categorize trees into different groups and define their functionality. The course begins by explaining Binary Trees and two subgroups: Binary Search Trees (BSTs) and Binary Heaps. You will program BSTs, their operations and traversal algorithms. BSTs are an important structure when wanting to access information quickly. Heaps approach access differently and prioritize what data is accessed. Heaps also employ the concept of up-heap and down-heap operations not found in other structures.\u003c/p\u003e\n\u003cp\u003eHashMaps and SkipLists are the last data structures discussed in the course. The HashMap ADT is a collection of key-value pairs. The key-value pairs are stored in an unordered manner based on hash codes and compression functions that translate keys into integers. You will investigate different collision strategies and implement one. SkipLists are a probabilistic data structures where data is placed in the structure based on a randomization procedure.\u003c/p\u003e\n\u003cp\u003eThe course design has several components and is built around modules. A module consists of a series of short (3-5 minute) instructional videos. In between the videos, there are textual frames with additional content information for clarification, as well as video errata dropdown boxes. All modules include an Exploratory Lab that incorporates a Visualization Tool specifically designed for this course. The lab includes discovery questions that lead you towards delving deeper into the efficiency of the data structures and examining the edge cases. This is followed by a set of comprehension questions on topics covered in the module that count for 10% of your grade. The modules end with Java coding assignments which are 60% of your grade. Lastly, you'll complete a course exam, which counts for the remaining 30% of your grade.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"47a:T583,\u003cp\u003eThis is the second genetics course in a three-part series. Building upon the concepts from biochemistry, genetics, and molecular biology from our \u003ca href=\"http://bit.ly/700xBio\"\u003e7.00x Introductory Biology MOOC\u003c/a\u003e, these genetics courses go to a new level of depth. What are different types of genetic changes? How do nature and scientists create these genetic variations? How do we design and perform analyses to identify the genetic basis of a trait or disease?\u003c/p\u003e\n\u003cp\u003eProfessors Peter Reddien and Mary Gehring will challenge you to expand your understanding of genetics. You will study the variety of genetic changes, different approaches to genetic analyses, and the impact of genetics on development and behavior.\u003c/p\u003e\n\u003cp\u003eWe developed the 7.03x Genetics series with an emphasis on:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDeveloping your scientific thinking skills including articulating hypotheses, designing experiments, performing thought experiments, and interpreting data.\u003c/li\u003e\n\u003cli\u003eUsing data based on real scientific experiments and highlighting the scientific process in assessments.\u003c/li\u003e\n\u003cli\u003eAsserting that biology is an active field that changes daily through examples of research and relevance to medicine, not static information in a textbook.\u003c/li\u003e\n\u003cli\u003eUniting themes and principles that inform how scientists conduct and interpret research.\u003c/li\u003e\n\u003cli\u003eImplementing the science of learning in the course design.\u003c/li\u003e\n\u003c/ul\u003e47b:Tbfc,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis course aims at acquainting you with the modeling and simulation of complex articulated mechanical systems, denoted as multibody systems, such as vehicles, merry-go-rounds, motorbikes, cranes, human bodies, suspensions, robot manipulators, mechanical transmissions, etc.\u003c/p\u003e\n\u003cp\u003eThis course is based on (1) video clips focusing on the main theoretical background and concepts, (2) well-illustrated written sections giving more details about the mathematical formulation, and (3) questions, exercises and modeling projects.\u003c/p\u003e\n\u003cp\u003eDespite the intrinsic complexity of such systems in terms of morphology and motions, basic skills in Newtonian mechanics, linear algebra and numerical methods are sufficient to model them, provided that the endless and tedious computation related to their internal kinematics and dynamics are at our disposal. This is the purpose of the symbolic program ROBOTRAN*, which can be used with this course and can automatically generate the full set of equations of motion of MBS, in a symbolic manner, i.e. exactly as if you were writing them by hand, whatever the size and the morphological complexity of the application. Hence, this course will instead teach you how to intervene upstream and downstream this generation step.\u003c/p\u003e\n\u003cp\u003eUpstream the latter, you will learn how to translate a real system, e.g. a car suspension, into a virtual multibody model comprising bodies, joints, internal or external forces and torques and imposed motion… with a level of refinement that will be dictated by the original issue. For example, what is the minimum tire ground force when the car suspension is excited by a shaker?\u003c/p\u003e\n\u003cp\u003eDownstream the symbolic generation, your intervention will consist in:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCompleting the symbolic model with features that are specific to your system, e.g. a tire force model or the tuning of a motion controller, among other things;\u003c/li\u003e\n\u003cli\u003eImplementing under the form of a program (in Python, Matlab, or C) a time simulation to solve the differential equations of motion, given the original question: e.g. find the transient motion of the system submitted to forces and torques and compute a specific force time history or the maximal acceleration of a particular point.\u003c/li\u003e\n\u003cli\u003eSelecting the most suitable results, including self-explanatory - and sometimes funny - video animations of your multibody system in motion.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn sum, this course, based on the use of the ROBOTRAN* symbolic generator, will allow you to focus on the most interesting aspects of the multibody modeling process, by entirely mastering your computer model from the input data to the results, instead of using a black-box multibody software that clearly goes against the educational objective of this course. \u003c/p\u003e\n\u003cp\u003eEnjoy Multibody Dynamics! \u003c/p\u003e\n\u003cp\u003e*Note: The course was built to teach modeling and simulation of multibody systems, and not to teach any specific software. However, we suggest that you use the symbolic ROBOTRAN program to model and study the various multibody systems proposed in this course.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"47c:T56c,\u003cp\u003eWell-designed and automated data pipelines and ETL processes are the foundation of a successful Business Intelligence platform. Defining your data workflows, pipelines and processes early in the platform design ensures the right raw data is collected, transformed and loaded into desired storage layers and available for processing and analysis as and when required.\u003c/p\u003e\n\u003cp\u003eThis course is designed to provide you the critical knowledge and skills needed by Data Engineers and Data Warehousing specialists to create and manage ETL, ELT, and data pipeline processes.\u003c/p\u003e\n\u003cp\u003eUpon completing this course you’ll gain a solid understanding of Extract, Transform, Load (ETL), and Extract, Load, and Transform (ELT) processes; practice extracting data, transforming data, and loading transformed data into a staging area; create an ETL data pipeline using Bash shell-scripting, build a batch ETL workflow using Apache Airflow and build a streaming data pipeline using Apache Kafka.\u003c/p\u003e\n\u003cp\u003eYou’ll gain hands-on experience with practice labs throughout the course and work on a real-world inspired project to build data pipelines using several technologies that can be added to your portfolio and demonstrate your ability to perform as a Data Engineer.\u003c/p\u003e\n\u003cp\u003eThis course pre-requisites that you have prior skills to work with datasets, SQL, relational databases, and Bash shell scripts.\u003c/p\u003e47d:T511,\u003cp\u003eMaster Data Engineering on Databricks Lakehouse Platform\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLearn Databricks architecture, cluster management \u0026amp; notebook analysis\u003c/li\u003e\n\u003cli\u003eBuild reliable ETL pipelines with Delta Lake for data transformation\u003c/li\u003e\n\u003cli\u003eImplement advanced data processing techniques with Apache Spark\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCourse Highlights:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCreate \u0026amp; scale Databricks clusters for workloads\u003c/li\u003e\n\u003cli\u003eLoad data from diverse sources into notebooks\u003c/li\u003e\n\u003cli\u003eExplore, visualize \u0026amp; profile datasets with notebooks\u003c/li\u003e\n\u003cli\u003eVersion control \u0026amp; share notebooks via Git integration\u003c/li\u003e\n\u003cli\u003eRead \u0026amp; ingest data in various file formats\u003c/"])</script><script>self.__next_f.push([1,"li\u003e\n\u003cli\u003eTransform data with SQL \u0026amp; DataFrame operations\u003c/li\u003e\n\u003cli\u003eHandle complex data types like arrays, structs, timestamps\u003c/li\u003e\n\u003cli\u003eDeduplicate, join \u0026amp; flatten nested data structures\u003c/li\u003e\n\u003cli\u003eIdentify \u0026amp; fix data quality issues with UDFs\u003c/li\u003e\n\u003cli\u003eLoad cleansed data into Delta Lake for reliability\u003c/li\u003e\n\u003cli\u003eBuild production-ready pipelines with Delta Live Tables\u003c/li\u003e\n\u003cli\u003eSchedule \u0026amp; monitor workloads using Databricks Jobs\u003c/li\u003e\n\u003cli\u003eSecure data access with Unity Catalog\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eGain comprehensive skills in data engineering on Databricks through hands-on labs, real-world projects and best practices for the modern data lakehouse.\u003c/p\u003e47e:T45e,\u003cp\u003eTravelers in large cities experience significant congestion during their everyday trips. Expanding roads and infrastructure is not a long-lasting remedy to urban congestion. This course will focus on understanding traffic congestion and will explore ways to improve mobility through advanced traffic management schemes. As we all experience, congestion is a highly complex process without a single explanation. An interesting question is: “Can we describe these complex interactions with simple and elegant ways? “ \u003c/p\u003e\n\u003cp\u003eThe course will introduce the fundamentals of traffic flow theory and will describe different traffic flow models in micro- and macroscopic level. Network-level aggregated modeling and control approaches are introduced based on the concept of the Macroscopic Fundamental Diagram (MFD). Advanced traffic management schemes (such as adaptive traffic signal control, ramp metering, variable speed limits) are discussed. User equilibrium analysis is introduced through applications of route and departure time choices. Relative reading material, exercises and real data sets are provided.\u003c/p\u003e47f:T7fc,\u003cp\u003eWant to gain a solid understanding of the unique analysis methods needed to assess the financial strength and operating performance of insurance companies in the US and Europe? \u003c/p\u003e\n\u003cp\u003eWe’ll begin this course with a look at the main lines of business a"])</script><script>self.__next_f.push([1,"nd the current operating environment of the Life and Health sector of the industry. We will also discuss the impact of changes in interest rates on investment earnings and sales of annuity products. Next, we’ll turn our attention to the property and casualty sector and its main lines of business. We will then look at the trends in premium and investment income for this sector. Next, we’ll look at other sources of financial data and at the different accounting methods and standards that you will encounter when evaluating insurance companies. \u003c/p\u003e\n\u003cp\u003eLearners will also be introduced to the rules of SAP and how it differs from GAAP and IFRS accounting. We’ll also look at the mandate and organization of the U.S insurance regulators and the information that insurance companies are required to report each period in order to document their ongoing solvency. Next, we’ll examine the operating cycle of a typical insurance company starting with issuing a policy and ending with reporting financial results. We’ll also look at some key terms that you’ll need to understand and important issues that you’ll need to consider in your analysis.\u003c/p\u003e\n\u003cp\u003eWe’ll wrap up this course with a deep dive into how investments are categorized according to risk, valued, and carried differently on the books of a Life versus a P\u0026amp;C insurance company. We’ll talk about some of the red flags you should look for in the mix of assets a company invests in and in the behavior of its asset managers \u003cem\u003e(internal and external).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNote:\u003c/strong\u003e This course is part of the Financial Analysis of Insurance Companies Professional Certificate. \u003c/p\u003e\n\u003cp\u003e*Audit the first module on Life and Health sector for free and upgrade to verified to unlock the remaining modules.\u003c/p\u003e480:Td6d,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis course aims at acquainting you with the modeling and simulation of constrained multibody systems, and especially mechanical systems with kinematic loops, such as real vehicle or bicycle suspensions, parallel manipulators or robots, musculoskeletal systems, etc. \u003c/p\u003e\n\u003cp\u003eYou will also learn to deal with more advanced numerical analyses: \u003cbr /\u003e\n• Direct kinematics;\u003cbr /\u003e\n• Inverse kinematics;\u003cbr /\u003e\n• Equilibrium;\u003cbr /\u003e\n• Modal analysis;\u003cbr /\u003e\n• Direct Dynamics;\u003cbr /\u003e\n• Inverse Dynamics. \u003c/p\u003e\n\u003cp\u003eThis course is based on (1) video clips focusing on the main theoretical background and concepts, (2) well-illustrated written sections given more details about the mathematical formulation, and (3) questions, exercises and modeling projects. \u003c/p\u003e\n\u003cp\u003eDespite the intrinsic complexity of such systems in terms of morphology and motions, basic skills in Newtonian mechanics, linear algebra and numerical methods are sufficient to model them, provided that the endless and tedious computation related to their internal kinematics and dynamics are at our disposal. This is the purpose of the symbolic program ROBOTRAN, which can be used with this course and can automatically generate the full set of equations of motion of a constrained MBS, in a symbolic manner, i.e. exactly as if you were writing them by hand, whatever the size and their morphological complexity of the application. Hence, this course will instead teach you how to intervene upstream and downstream this generation step. \u003c/p\u003e\n\u003cp\u003eUpstream the latter, you will learn how to translate a real system, e.g. a car suspension, into a virtual multibody model comprising algebraic constraints between joints, kinematic loops, etc. \u003c/p\u003e\n\u003cp\u003eDownstream the symbolic generation, your intervention will consist in: \u003c/p\u003e\n\u003cp\u003e• Completing the symbolic model with features that are specific for your system, e.g. a tire force model or the tuning of a motion controller, among other things; \u003c/p\u003e\n\u003cp\u003e• Selecting and implementing under the form of a program (in Python, Matlab, or C) the suitable numerical method to solve the differential equations of motion, given the original question; (1) an equilibrium solution can give you the static forces and the system deflection, (2) a time simulation can compute any transient motion of the system submitted to forces and torques, (3) a modal analysis will provide you with the eigenmodes that inform you about the system stability and damping characteristics, (4) an inverse dynamics study can provide you with the necessary forces and torques for any prescribed motion of the system, (5) etc. \u003c/p\u003e\n\u003cp\u003e• Selecting the most suitable results, including self-explanatory - and sometimes funny - video animations of your multibody system in motion. \u003c/p\u003e\n\u003cp\u003eIn sum, this course, based on the use of the ROBOTRAN* symbolic generator, will allow you to focus on the most interesting aspects of the multibody modeling process, by entirely mastering your computer model from the input data to the results, instead of using a black-box multibody program that clearly goes against the educational objective of such a course. \u003c/p\u003e\n\u003cp\u003eEnjoy Multibody Dynamics! \u003c/p\u003e\n\u003cp\u003e*Note: The course was built to teach modeling and simulation of multibody systems, and not to teach any specific software. However, we suggest that you use the symbolic ROBOTRAN program to model and study the various multibody systems proposed in this course.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"481:T4d4,\u003cp\u003eOnline and software-based learning tools have been used increasingly in education. This movement has resulted in an explosion of data, which can now be used to improve educational effectiveness and support basic research on learning.\u003c/p\u003e\n\u003cp\u003eIn this course, you will learn how and when to use key methods for educational data mining and learning analytics on this data. You will examine the methods being developed by researchers in the educational data mining, learning analytics, learning-at-scale, student modeling, and artificial intelligence communities. You'll also gain experience with standard data mining methods frequently applied to educational data. You will learn how to apply these methods and when to apply them, as well as their strengths and weaknesses for different applications.\u003c/p\u003e\n\u003cp\u003eThe course will discuss how to use each method to answer education research questions, and to drive intervention and improvement in educational software and systems. Methods will be covered at a theoretical level, and in terms of learning how to apply them in Python or using software tools like RapidMiner. We will also discuss validity and generalizability; establishing how trustworthy and applicable the analysis results.\u003c/p\u003e482:T486,\u003cp\u003eThis interactive text used in this course was written with the intention of teaching Computer Science students about various data structures as well as the applications in which each data structure would be appropriate to use. It is currently beingtaught at the University of California, San Diego (UCSD), the University of San Diego (USD), and the University of Puerto Rico (UPR). \u003c/p\u003e\n\u003cp\u003eThiscoursework utilizes the Active Learning approach to instruction, meaning it has various activities embedded throughout to help stimulate your learning and improve your understanding of the materials we will cover. You will encounter \"STOP and Think\" questions that will help you reflect on the material, \"Exercise Breaks\" that will test your knowledge and understanding of the concepts discussed, "])</script><script>self.__next_f.push([1,"and \"Code Challenges\" that will allow you to actually implement some of the algorithms we will cover. \u003c/p\u003e\n\u003cp\u003eCurrently, all code challenges are in C++ or Python, but the vast majority of the content is language-agnostic theory of complexity and algorithm analysis. In other words, even without C++ or Python knowledge, the key takeaways can still be obtained.\u003c/p\u003e483:T439,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eRice University’s online business courses provide a convenient, yet interactive and hands-on, way to learn or brush up on the practical skills needed to analyze corporate financial statements.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eTaught by an industry professional from Rice Business, learners will explore the financial analysis journey, from extracting firm specific financial information to conducting an advanced DuPont analysis. Through a combination of engaging lectures, practical case studies, and hands-on exercises, students will gain invaluable insights into the principles and best practices that will allow them to confidently examine firm financial information and use it to analyze the firm’s position and performance.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eRecommendation:\u003cspan lang=\"EN-US\"\u003e As a next step in \u003c/span\u003ebuilding your financial statement analysis skills, we recommend completing the second course in this series, Financial Statement Analysis – Company Forecasting and Valuation, after completing Fundamental Ratio Analysis.\u003c/p\u003e484:T539,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eS\u003c/span\u003eustainability is a priority across industries, making data-driven decisions more critical than ever. This introductory course will equip you with foundational concepts of time-series modeling and the forecasting skills necessary to tackle sustainability challenges. Whether you are an environmental scientist, urban planner, or engineer, you will be empowered to leverage your data effectively for sustainable development.\u003c/p\u003e\n\u003cp\u003eThe course offers a hands-on project centered around solar irradiance data, where you will:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cspan lang=\"EN-U"])</script><script>self.__next_f.push([1,"S\"\u003eIdentify\u003c/span\u003e trends and seasonal patterns to make informed predictions in sustainable energy.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eM\u003c/span\u003eodel and forecast individual data components to apply to broader sustainability challenges.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis practical experience will give you a comprehensive understanding of how to leverage time-series modeling for real-world applications.\u003c/p\u003e\n\u003cp\u003eBy the end of the course, you will have the crucial foundational skills in time-series forecasting and the ability to make impactful and informed decisions. You will be able to identify patterns, analyze trends, and forecast future outcomes.\u003c/p\u003e\n\u003cp\u003eJoin us to work towards a more sustainable future, using data as a powerful tool to drive change.\u003c/p\u003e485:Tc7a,"])</script><script>self.__next_f.push([1,"\u003cp\u003eAs global energy demand grows, to mitigate climate change we must drive a swift transition to clean energy resources and enhanced electric power grid infrastructure.\u003c/p\u003e\n\u003cp\u003eIn this course, you will explore systemic principles of future electric power system management, such as the role of smart grids, data-enabled machine learning, power electronics-control, and data-driven decision-making. You will learn how energy technologies, including intermittent renewable energy technologies, can be modeled and controlled at both the component and system level to achieve sustainable, well-functioning, and economically sound results.\u003c/p\u003e\n\u003cp\u003eYou will also learn about assumptions underlying today’s hierarchical control and the innovations needed to support end-to-end flexible efficient electricity services by conventional and new resources. A particular emphasis is on data-enabled distributed cooperative systems solutions.\u003c/p\u003e\n\u003cp\u003eThroughout, you will examine examples of real-world industry problems and solutions, such as methods for achieving stable integration of diverse power resources, demand response, and fast storage at reasonable cost. Modeling can be used for developing the next generation software needed to operate these systems, and for implementing incentives for new technologies in electric energy markets.\u003c/p\u003e\n\u003cp\u003eThis course is designed for people engaging the energy transition across disciplines and professions. It introduces fundamental concepts for those interested in working in power systems planning, operations, and management. Researchers with a background in dynamical systems and control will learn how to model the dynamics and objectives of enabling clean and resilient electricity services as systems problems while making physically meaningful assumptions and using these models as the basis for introducing their own novel data-enabled methods. Experienced professionals, including utility and energy industry executives, will gain insights into cutting-edge research, concepts, and software. Policymakers in government and leaders in non-governmental organizations (NGOs) will find strategies for building resilience in grid infrastructure, driving more equitable access to energy, and driving higher renewable energy penetration in local markets.\u003c/p\u003e\n\u003cp\u003ePlease note: edX Inc. has recently entered into an \u003ca href=\"https://press.edx.org/2u-inc.-and-edx-to-join-together-in-industry-redefining-combination\"\u003eagreement to transfer the edX platform to 2U, Inc.\u003c/a\u003e, which will continue to run the platform thereafter. The sale will not affect your course enrollment, course fees or change your course experience for this offering. It is possible that the closing of the sale and the transfer of the edX platform may be effectuated sometime in the Fall while this course is running. Please be aware that there could be changes to the edX platform Privacy Policy or Terms of Service after the closing of the sale. However, 2U has committed to preserving robust privacy of individual data for all learners who use the platform. For more information see the \u003ca href=\"https://support.edx.org/hc/en-us/articles/4403415754007-edX-and-2U\"\u003eedX Help Center\u003c/a\u003e.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"486:T505,\u003cp\u003eWhat causes a country\u0026rsquo;s debt to become unsustainable? How do we assess debt sustainability of public and external debt? How can countries manage their debt portfolio?\u003c/p\u003e\r\n\u003cp\u003eThis online course aims to provide a comprehensive overview of debt sustainability analysis (DSA) and a medium-term debt management strategy framework adopted by the IMF and the World Bank.\u003c/p\u003e\r\n\u003cp\u003eSpecifically, the course will: (i) introduce the main principles of debt sustainability; (ii) cover recently updated DSA frameworks \u0026mdash; both for advanced and emerging markets and for low-income countries \u0026mdash; with an emphasis on country data; (iii) present a medium-term debt management strategy (MTDS) framework; and (iv) illustrate debt sustainability analysis under uncertainty.\u003c/p\u003e\r\n\u003cp\u003eWhether you have a professional interest in debt sustainability or debt management or you are simply curious about these issues, we hope that you will join us in this in-depth study of one of the most critical and current issues in economic policy today!\u003c/p\u003e\r\n\u003cp\u003eDebt Sustainability Analysis is offered by the IMF with financial support from the Debt Management Facility (DMF).\u003cbr /\u003e\u003cbr /\u003e\u003cstrong\u003eThe IMF\u0026rsquo;s online learning program receives financial support from the Government of Japan.\u003c/strong\u003e\u003c/p\u003e487:T941,"])</script><script>self.__next_f.push([1,"\u003cp\u003eFinancial decisions and financial management are drivers of business performance and success. Financial data plays a vital role in business strategy, planning, and positioning. Learn the basics of financial statements, the conceptual framework of financial information and its relationship to firms’ strategic positioning and strategy execution.\u003c/p\u003e\n\u003cp\u003eBy completing this course, you will learn how to assess and use financial information to make informed business decisions.\u003c/p\u003e\n\u003cp\u003e这门课程用财务语言解构企业的价值创造过程，从而帮助学习者理解影响价值创造的各种因素，建立财务思维，并具备将其应用于商业决策的能力。我们将从认识财务报表开始，逐步了解财务信息的架构体系，讨论财务数据与行业、战略定位与战略执行的关系，剖析企业的价值创造过程，在此基础上，讨论如何运用财务数据进行商业决策。\u003c/p\u003e\n\u003ch3\u003eWhat you'll learn\u003c/h3\u003e\n\u003cp\u003e· - How to think with a financial mindset\u003c/p\u003e\n\u003cp\u003e· - How to use financial knowledge when making business decisions\u003c/p\u003e\n\u003cp\u003e· - The basics of financial systems architecture\u003c/p\u003e\n\u003cp\u003e· - How financial data plays a role in strategic execution and positioning\u003c/p\u003e\n\u003cp\u003e· - 用财务语言解构企业的价值创造过程\u003c/p\u003e\n\u003cp\u003e· - 帮助学习者理解影响价值创造的各种因素\u003c/p\u003e\n\u003cp\u003e· - 培养将其应用于商业决策的能力\u003c/p\u003e\n\u003cp\u003eFinancial decisions and financial management are drivers of business performance and success. Financial data plays a vital role in business strategy, planning, and positioning. Learn the basics of financial statements, the conceptual framework of financial information and its relationship to firms’ strategic positioning and strategy execution.\u003c/p\u003e\n\u003cp\u003eBy completing this course, you will learn how to assess and use financial information to make informed business decisions.\u003c/p\u003e\n\u003cp\u003e这门课程用财务语言解构企业的价值创造过程，从而帮助学习者理解影响价值创造的各种因素，建立财务思维，并具备将其应用于商业决策的能力。我们将从认识财务报表开始，逐步了解财务信息的架构体系，讨论财务数据与行业、战略定位与战略执行的关系，剖析企业的价值创造过程，在此基础上，讨论如何运用财务数据进行商业决策。\u003c/p\u003e\n\u003ch3\u003e\u003c/h3\u003e"])</script><script>self.__next_f.push([1,"488:T40c,\u003cp\u003eBusinesses today have access to an increasingly large amount of detailed customer data, and this influx of data is only going to continue. Combined with a detailed history of marketing actions, there is a newfound potential for deriving actionable insights, but you need the tools to do so. Using real-world applications from various industries, this course will help you understand the tools and strategies used to make data-driven decisions that you can put to use in your own company or business. This valuable data may include in-store and online customer transactions, customer surveys as well as prices and advertising. You\u0026rsquo;ll also learn how to assess critical managerial problems, develop relevant hypotheses, analyze data and, most importantly, draw inferences to create convincing narratives which yield actionable results.\u003c/p\u003e\r\n\u003cp\u003e\u003cem\u003eThis course is part of Wharton's Digital Marketing Professional Certificate. For more information, \u003ca href=\"https://www.edx.org/digital-marketing-professional\"\u003esee here\u003c/a\u003e.\u003c/em\u003e\u003c/p\u003e489:T69a,\u003cp\u003e\u003cspan lang=\"EN\"\u003eUnderstanding various data structures and algorithms is the foundation of modern programming.\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIn this self-paced course you will learn about the characteristics of commonly used data structures and algorithms and how to implement them to be able to conduct efficiency analyses in C++ from scratch. \u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eTo solve real-world problems efficiently, advanced C++ programs are developed using pointers, dynamic storage, and linear and non-linear data structures. You will gain experience with a variety of algorithm types like recursion, searching, sorting, dynamic programming, greedy, and divide and conquer, which are required to build efficient programs. You will also learn how to measure the efficiency of the program you have written. After completing the course, you will be able to systematically approach coding problems in a step-by-step manner.\u003cspan lang=\"EN\"\u003e"])</script><script>self.__next_f.push([1,"\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThere are several implementations that are presented in the development of each data structure. As you solve problems ranging from easy to difficult that address different data structures, you will learn how to select and program various data structures and apply the most suitable algorithms to solve a particular problem.\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eSolving \u003cspan lang=\"EN\"\u003eproblems that require different data structures will help you understand the strengths and weaknesses of common data structures and teach you how to choose the right combinations of data structures and algorithms for efficiently solving problems.\u003c/span\u003e\u003c/p\u003e48a:T919,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis course covers state-of-the-art object-centric process mining methods and tools to enable participants to get a comprehensive understanding of the capabilities and use cases for object-centric process mining. The content covers how process mining can be used to understand a process, check its correctness, and apply machine learning methods to improve all types of processes. \u003c/p\u003e\n\u003cp\u003eTraditional process mining is often limited to analyzing processes centered on a single case identifier. Object-Centric Process Mining (OCPM) supports the analysis of processes involving multiple interacting objects (e.g., customers, orders, products, invoices) within a single model. As a result, data need to be extracted only once, distortions are avoided, and performance problems involving multiple processes or organizational units can be identified.\u003c/p\u003e\n\u003cp\u003eFirst, sources of event data are discussed. With the rise of digitalization, more and more events of every process are tracked digitally. Object-centric event logs store this data, which enables the computation of various process insights. After covering the most important process modeling notations (including state-of-the-art object-centric process model notations), process discovery approaches are presented. They can automatically learn a process model from event data. Then, the course describes conformance-checking methods that can identify behavioral differences between the desired process and the behavior observed in reality. The course also covers approaches and tools to analyze the performance and organizational structure of processes. Finally, the connection between process mining and machine learning is discussed, by describing how process mining can identify relevant problems in processes and transform them into machine learning problems.\u003c/p\u003e\n\u003cp\u003eThroughout the course, the concepts explained in the videos are accompanied by hands-on quizzes and optional coding and tool practices. These practical experiences foster a better understanding of algorithms and provide a guided introduction to state-of-the-art process mining tools. \u003c/p\u003e\n\u003cp\u003eAfter taking the course, students should have a great understanding of the different process mining techniques and should be comfortable applying them to object-centric event data to improve their processes.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"48b:Ta00,"])</script><script>self.__next_f.push([1,"\u003cp\u003eAccording to Indeed, machine learning engineer salaries currently start at USD 100,809 and top out at just over USD 254,000. \u003c/p\u003e\n\u003cp\u003eGain advanced Keras and TensorFlow 2.x techniques you need to build and optimize machine learning models. In this course, practice techniques for deep learning, reinforcement learning, generative models, and sequential data handling that will prepare you to tackle complex real-world challenges. \u003c/p\u003e\n\u003cp\u003eYou’ll begin by learning about Keras's advanced features, including its functional API used to design complex models. You’ll then learn how to create custom layers and models to tailor solutions to unique challenges and seamlessly integrate Keras with TensorFlow 2.x for enhanced functionality. \u003c/p\u003e\n\u003cp\u003eNext, you’ll use Keras to develop advanced convolutional neural networks (CNNs) that can solve complex computer vision tasks. You’ll apply data augmentation to improve model generalization, implement transfer learning with pre-trained models, and leverage TensorFlow for advanced image processing. You’ll also explore transpose convolution \u003c/p\u003e\n\u003cp\u003eThen, learn how to build and train advanced Transformers using Keras for sequential data tasks, including time series prediction. You’ll gain hands-on experience developing Transformer-based models for text generation and explore how to utilize TensorFlow to manage sequential data effectively. \u003c/p\u003e\n\u003cp\u003eThen you’ll dive into unsupervised learning with Keras. You’ll build and train autoencoders, experiment with cutting-edge diffusion models, and develop generative adversarial networks (GANs). You’ll also learn to integrate TensorFlow for advanced unsupervised learning tasks and expand your expertise in generative modeling techniques. \u003c/p\u003e\n\u003cp\u003eYou’ll master advanced Keras techniques for model development by creating custom training loops and optimizing model performance. You’ll explore hyperparameter tuning using Keras Tuner and leverage TensorFlow for enhanced model optimization and custom training workflows. \u003c/p\u003e\n\u003cp\u003eIn the final module, you’ll explore reinforcement learning and its applications in Keras. You’ll implement Q-Learning algorithms and develop deep Q-networks (DQNs) to tackle advanced reinforcement learning tasks, gaining practical experience with this powerful AI technique. \u003c/p\u003e\n\u003cp\u003eBy the end of this course, you’ll have the knowledge and skills to build and optimize advanced models using Keras and TensorFlow 2.x, tackling challenges in computer vision, NLP, reinforcement learning, and generative modeling.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"48c:T9f1,"])</script><script>self.__next_f.push([1,"\u003cp\u003eEveryone has an opinion on parenting – where babies should sleep, what they should eat, and whether parents should spank, scold, or praise. What’s more, the media often offers support for whichever opinions appear most popular at any given time. This leavesthose of us who like to base our decisions on firm, provablefacts feeling dizzy. \u003c/p\u003e\n\u003cp\u003e“The Science of Parenting” addresses this confusion by moving beyond the chatter and opinion surrounding parenting, and by \u003cstrong\u003elooking directly at the science\u003c/strong\u003e. Parenting itself is far from a science. Nevertheless, scientists have conducted thousands of studies that can help parents – or future parents – make sensible, informed decisions. \u003c/p\u003e\n\u003cp\u003eOne goal of this coursewill be to provide a \u003cstrong\u003esurvey of important scientific findings\u003c/strong\u003e spanning a range of topics that are central to the lives of parents: \u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ediet\u003c/li\u003e\n\u003cli\u003esleep\u003c/li\u003e\n\u003cli\u003ediscipline\u003c/li\u003e\n\u003cli\u003elearning\u003c/li\u003e\n\u003cli\u003escreen time\u003c/li\u003e\n\u003cli\u003eimpulse control\u003c/li\u003e\n\u003cli\u003evaccination\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe’ll also \u003cstrong\u003eexplore ongoing mysteries\u003c/strong\u003e , like what causes autism, and why so many children are allergic to peanuts. \u003c/p\u003e\n\u003cp\u003ePerhaps more important, this coursewill not only dig into existing science, but will also \u003cstrong\u003eexplore the underlying nature of parenting science itself\u003c/strong\u003e. Often, scientists measure correlations: They ask how different parenting practices are related to different behaviors in children. But the claims they make from 'correlational data are often much, much stronger. For example, from correlational data, scientists often claim that parents cause the behaviors of their kids. This coursewill show how this type of error – common in the scientific literature – can \u003cstrong\u003eexplain a significant amount of the confusion present in the media\u003c/strong\u003e and general public. Wewill discuss how to avoid the same error when evaluating science, and how to use the sum of available evidence to inform decision making. \u003c/p\u003e\n\u003cp\u003eThe course’s instructor, David Barner, is a leading authority on cognitive development. He is joined by leading experts on behavior genetics, vaccination, autism, lying, and spanking, as well as by real live parents who try to use science to inform their decisions. This class is suitable not only for parents, future parents, and grandparents, but also for \u003cstrong\u003eprofessionals interested in health care, social work, and early childhood education\u003c/strong\u003e who want to increase their knowledge and analysis skills.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"48d:T83e,"])</script><script>self.__next_f.push([1,"\u003cp\u003eWe are all getting familiar with the image of a drone in the sky. Although flying a drone is fun, drones are not toys. More and more UAVs or drones are used by governments and companies to gain answers and insights on nature, agricultural and metropolitan challenges among other fields. For example, small Drones/UAVs (Unmanned Aerial Systems) are employed in agriculture for crop observation, crop monitoring, field analysis and map generation through aerial surveys. And with the available software and 'mission planning tools' market growing, so is the demand for knowledge and understanding about its usage and limitations.\u003c/p\u003e\n\u003cp\u003eOur top professors of the 'Information Technology Group' and the 'Laboratory of Geo-Information Science and Remote Sensing group' of Wageningen University \u0026amp; Research will teach you whether it makes sense to use drones for your application, challenge or question. You will learn how to plan an end-to-end mission (from image acquisition to data visualization) for your specific drone application and how to execute a drone mission safely. Afther finishing the MOOC Drones for Agriculture: Prepare and Design Your Drone (UAV) Mission, you will have gained full understanding of the aerial mapping workflow and how to implement it in a programmable small drone. You will know which steps you need to take to gain the valuable insights you are looking for.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDo I need a drone to complete this course?\u003c/strong\u003e\u003cbr /\u003e\nNo! You do not need a drone to complete this course. For all the assignments and exercises we will provide the necessary material (in case it is needed). But, we expect to pick your curiosity with this course and hopefully nudge you into buying one and joining the community ;).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFor whom\u003c/strong\u003e\u003cbr /\u003e\nAlthough the course is initially made for agriculture technicians, researchers or graduate students from multidisciplinary technical fields, everyone that aims to learn how to use an off-the-shelf small drone for generating a high-resolution image from a field, or has a general high interest in drones, is very welcome.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"48e:T863,"])</script><script>self.__next_f.push([1,"\u003cp\u003eCybersecurity risk management guides a growing number of IT decisions. Cybersecurity risks continue to have critical impacts on overall IT risk modeling, assessment and mitigation.\u003c/p\u003e\n\u003cp\u003eIn this course, you will learn about the general information security risk management framework and its practices and how to identify and model information security risks and apply both qualitative and quantitative risk assessment methods. Understanding this framework will enable you to articulate the business consequences of identified information security risks. These skills are essential for any successful information security professional.\u003c/p\u003e\n\u003cp\u003eThe goal of this course is to teach students the risk management framework with both qualitative and quantitative assessment methods that concentrate on the information security (IS) aspect of IT risks. The relationship between the IT risk and business value will be discussed through several industry case studies.\u003c/p\u003e\n\u003cp\u003eFirst, you will learn about the principles of risk management and its three key elements: risk analysis, risk assessment and risk mitigation. You will learn to identify information security related threats, vulnerability, determine the risk level, define controls and safeguards, and conduct cost-benefit analysis or business impact analysis.\u003c/p\u003e\n\u003cp\u003eSecond, we will introduce the qualitative and quantitative frameworks and discuss the differences between these two frameworks. You will learn the details of how to apply these frameworks in assessing information security risk.\u003c/p\u003e\n\u003cp\u003eThird, we will extend the quantitative framework with data mining and machine learning approaches that are applicable for data-driven risk analytics. You will explore the intersection of information security, big data and artificial intelligence.\u003c/p\u003e\n\u003cp\u003eFinally, you will analyze a series of extended case studies, which will help you to comprehend and generalize the principles, frameworks and analytical methods in actual examples.\u003c/p\u003e\n\u003cp\u003eThis offering is part of the RITx Cybersecurity MicroMasters Program that prepares students to enter and advance in the field of computing security.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"48f:Tac6,"])</script><script>self.__next_f.push([1,"\u003cp\u003eAs a response to the unavoidable impacts of climate change and to become climate resilient by 2050, the European Union adopted its new EU strategy on adaptation to climate change on 24 February 2021. The principal objectives of the Strategy are to achieve smarter, swifter and more systemic adaptation, and to step up international action on climate change adaptation. The Strategy is part of the broader set of policy initiatives, the European Green Deal, which aims to make the EU the first climate-neutral continent by 2050. The related initiatives in Europe, such as the IMPETUS project, may provide inspiration for other countries in designing their pathways to resilience. \u003c/p\u003e\n\u003cp\u003eThis course presents an overview of the pertinent topics in climate change, with a focus on the innovative approaches to climate adaptation in Europe. It introduces the learners to the basics of climate change science and the multifaceted challenges posed by it. It presents the implementation of the Paris Agreement. The course also examines a broad spectrum of climate sustainability concerns and needs, related to the integration of science-based modelling for developing holistic pathways for both mitigation and adaptation pathways. It explores the development of the necessary socio-economic narrative for the just and equitable implementation of those pathways, as well as the development and implementation of relevant valuation and financial instruments. It also outlays the contribution of data and digital technologies to the net zero transformation. Finally, this course concludes with a look at innovative solutions to address climate change at local and regional levels.\u003c/p\u003e\n\u003cp\u003eLearners will develop a thorough understanding of the impacts of climate change and the complexities of climate change adaptation. They will examine the synergies and tradeoffs between climate change adaptation and biodiversity protection, water management or social marginalisation. They will learn about adaptation solutions developed at the local and regional level in Europe and will analyse the potential for replicability of such solutions in different contexts, including outside Europe.\u003c/p\u003e\n\u003cp\u003eThe course aims at engaging a broad audience and making them familiar with potential roles of different public and private actors at both local, regional, and national levels in tackling climate adaptation challenges. By the end of the course, a student will be able to understand and explain the complexities of innovative climate adaptation solutions and their relevance for the achievement of the goals of the Paris Agreement. \u003c/p\u003e\n\u003cp\u003eThe target audience is students at graduate or advanced undergraduate level and professionals in the sustainable development sector.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"490:T94a,"])</script><script>self.__next_f.push([1,"\u003cp\u003eBy the end of this course, participants will:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEvaluate the impact of human activities on Earth's climate system and the role of renewable energy in mitigation, emphasising collaborative efforts among policymakers, scientists, and society.\u003c/li\u003e\n\u003cli\u003eAnalyse the effectiveness of innovative climate adaptation solutions across Europe, assessing their scalability and alignment with the Paris Agreement and EU climate strategy.\u003c/li\u003e\n\u003cli\u003eUnderstand the evolution of European climate policy since 2015, its alignment with SDGs, and contributions to sustainable development and climate action.\u003c/li\u003e\n\u003cli\u003eAssess key EU climate policies such as the Green Deal and Fit for 55 Package, and analyse the EU's initiatives for achieving SDGs, focusing on climate neutrality, renewable energy, green finance, and sustainable agriculture, while evaluating progress and challenges.\u003c/li\u003e\n\u003cli\u003eUnderstand energy transition pathways and their impact on climate through scenario modelling and policy analysis. Learn about pathways modelling to assess climate change impacts on human health and designing health-centric adaptation strategies.\u003c/li\u003e\n\u003cli\u003eComprehend the role of Climate Data Platforms in integrating diverse climate data sources for evidence-based decision-making and policy formulation.\u003c/li\u003e\n\u003cli\u003eUnderstand the concept of the Blue Economy, its challenges, opportunities, and its role in sustainable development goals, including policy implications and stakeholder engagement.\u003c/li\u003e\n\u003cli\u003eEvaluate socio-economic narratives and fiscal policies for effective climate adaptation, emphasising economic valuation and financial instruments.\u003c/li\u003e\n\u003cli\u003eAnalyse European labour market occupations based on their \"Greenness\" and \"Digitalization\" scores using ESCO hierarchy and data-driven models.\u003c/li\u003e\n\u003cli\u003eApply Systems Innovation Approach to engage stakeholders in sustainable innovation pathways aligned with SDGs. Understand climate innovation at the community level, including stakeholder engagement and sustainable infrastructure for effective climate adaptation.\u003c/li\u003e\n\u003cli\u003eIntegrate SDGs into corporate sustainability reporting using the AE4RIA framework to enhance transparency and long-term value creation.\u003c/li\u003e\n\u003cli\u003eUnderstand the diverse ecosystem of innovative solutions implemented across Europe through the Horizon 2020 program with a series of practical examples.\u003c/li\u003e\n\u003c/ul\u003e"])</script><script>self.__next_f.push([1,"491:T9a7,"])</script><script>self.__next_f.push([1,"\u003cp\u003eClimate change, biodiversity loss, pan-syndemics, and energy dependencies are some of today's most pressing complex challenges. Much of our economies are exhaustive, vulnerable, and unfair. We must actively restore and regenerate ecosystems while transforming our economies to become more circular and just. We require new knowledge systems and cultures leading to transformative action as the human impact on earth needs to be fundamentally redesigned.\u003c/p\u003e\n\u003cp\u003eScientific knowledge and reasoning are fundamental tools to guide policy decisions, especially in times of crises. Limitations of reductionist science are evident due to the lack of widespread action in addressing today's highly complex challenges, which are self-emergent, unpredictable, span across nested scales, depend on societal behavioral transitions, and lack data.\u003c/p\u003e\n\u003cp\u003eDesign offers creative ways of intervening iteratively, responding to a current problem by prototyping future pathways. Designerly praxis benefits from science, for example, by directing interventions and leveraging relationships based on quantitative data. Neither science's analytical and descriptive tools nor the iterative design process alone are adequate for addressing complex challenges. Combining both cultures and methods of reasoning as a fluid, intervention-based, and synergistic process is beneficial for fostering the urgently required regenerative, transformative action.\u003c/p\u003e\n\u003cp\u003eThis MOOC series, \"Designing Resilient Regenerative Systems” offers four consecutive MOOCs that address these urgent and complex challenges. Participants emerge on a learning journey including an emphasis on holistic worldviews, concepts like regeneration and resilience, befriending complexity and uncertainty, methods and hybrid practices of science and design, connecting more with our inner self, and becoming bio-regional weavers within communities of learning and praxis.\u003c/p\u003e\n\u003cp\u003eThis second MOOC focuses on scientific and designerly ways of dealing with complexity. By developing a critical perspective on systems thinking, participants embody their practice of navigating in complexity by continuously zooming out and in as a view from above. A functional understanding of transformative resilience is complemented with an introduction to social network analysis. We learn about circularities and how to design for circularity, leading us to the final theme of how to innovate in complex systems - systemic innovation.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"492:T9b6,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis MOOC series is about creating positive impact on complex systems. It is about navigating complexity and uncertainty with new tools and practices, such as “organic emergence:” flexible ways to engage with unpredictable complexity based on tools and social trust, to cope with sudden challenges, and to reveal hidden opportunities. Extending your supportive social networks in your region and a global context is one significant benefit of this program.\u003c/p\u003e\n\u003cp\u003eIn this second MOOC, “Beyond Systems Thinking,” you acquire a critical understanding of systems thinking and develop your toolset for dealing with complexity. You learn to embody “view from above” practices, a continuous zoom-in-zoom-out technique to navigate in complexity - physically and theoretically. You deepen a functional, transformative perspective on resilience and learn the basics of social network analysis, a powerful method to design in complexity. You extend the current notion of a circular economy to multiple types of circular flows and their applicability across spatial and governance scales. Finally, you explore different examples of systemic innovation and relate such illustrations with your own “Quest,” your endeavour, in your regional context.\u003c/p\u003e\n\u003cp\u003eExciting real-world illustrations will take you to living systems labs in Hemsedal, Norway; Annecy, France; Ostana, Italy; and Mallorca, Spain. This offers a comparative understanding of communities and regions undergoing sustainability transitions across different contexts, cultures, climates, and geographies.\u003c/p\u003e\n\u003cp\u003eThe MOOCs’ didactics are designed to combine time- and place-independent virtual learning through pre-recorded conversations, both accessible as movies and audio files, readings, and practical engagement in nature. Virtual content stimulates physical and social interaction in the participants' bio-region. Systemic Cycles takes us on a conscious exploration of place and circularities on a bicycle to playfully learn systemic design methods, weave together local and regional networks, and explore the inner self through physical activity. An accompanying visual mapping process called Gigamapping is a designerly way to co-create your learning journey and connect across the MOOC series to your final transformative design project. Your personal QUEST guides you through your learning journey. At the end of this course, you will be a better leader for transformative change towards regeneration.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"493:T874,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThe gen AI market is projected to grow by 42% CAGR by 2033 (Bloomberg). And with natural language processing (NLP) being an integral part of this gen AI revolution, data scientists and AI professionals with the right skills are in high demand! \u003c/p\u003e\n\u003cp\u003eIf you’re an aspiring AI professional or data scientist, this IBM course on Generative AI - Model Foundations and NLP gives you highly sought-after skills employers are looking for. \u003c/p\u003e\n\u003cp\u003eAI professionals use NLP to help generative AI applications understand and generate human language and enable tasks like text generation, summarization, translation, and conversational interactions. \u003c/p\u003e\n\u003cp\u003eDuring this course, you’ll learn how to implement, train, and evaluate gen AI models for NLP. You’ll explore document classification, language modeling, language translation, and develop a fundamental understanding of how to build small and large language models. \u003c/p\u003e\n\u003cp\u003eYou’ll learn how to convert words to features. You’ll discover one-hot encoding, bag-of-words, embedding, and embedding bags. Plus, you’ll implement PyTorch to embed models using word2vec for feature representation in text data. \u003c/p\u003e\n\u003cp\u003eYou’ll also build, train, and optimize neural networks for document categorization. You’ll learn about concepts such as N-gram language model and sequence-to-sequence models. And you’ll evaluate the quality of generated text using metrics, such as BLEU. \u003c/p\u003e\n\u003cp\u003eImportantly, you’ll get hands-on in labs, where you’ll gain practical experience in tasks such as implementing document classification using torchtext in PyTorch, and building and training a simple language model with a neural network to generate text. You’ll also integrate pre-trained embedding models, such as word2vec, for text analysis and classification. \u003c/p\u003e\n\u003cp\u003eIf you’re an aspiring AI professional or data scientist looking to power up your resume with in-demand gen AI skillso, ENROLL TODAY and prepare to take your career to the next level! \u003c/p\u003e\n\u003cp\u003ePrerequisites: To enroll for this course, a basic knowledge of Python and familiarity with machine learning and neural network concepts is recommended.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"494:T4c8,\u003cp\u003eReady to power up your portfolio with real-world experience in cybersecurity analysis that employers look for? This capstone course will have you analyzing a cybersecurity challenge like a pro and giving you hands-on experience dealing with a breach response you can talk about in interviews. \u003c/p\u003e\n\u003cp\u003eDuring this course, you’ll be immersed in the core principles and practices of breach response and cybersecurity management. You’ll dive into phishing, vishing, point-of-sale (PoS) breaches, insider threats, AI-related breaches, ransomware attacks, and how attackers exploit vulnerabilities. You’ll also explore how organizations respond to these incidents and implement strategies to prevent future threats. \u003c/p\u003e\n\u003cp\u003eGuided by industry experts, you’ll then get hands-on working on incident response, digital forensics, penetration testing, and compliance. Through engaging case studies and structured templates, you’ll develop critical skills in breach management, plus you’ll propose enhancements to real-world response strategies. \u003c/p\u003e\n\u003cp\u003eIf you’re looking to gain the practical experience employers look for and expand your expertise, ENROLL TODAY and get ready to boost your resume in just 6 weeks.\u003c/p\u003e495:T5bf,\u003cp\u003eVulnerabilities can occur at any stage of software development, making it critical for developers to write secure code and maintain a secured development environment and the platform it runs on. In this course, you will learn to identify security vulnerabilities in applications and implement secure code practices to prevent events like data breaches and leaks which can significantly impact an organization’s reputation and financial condition. This course provides a comprehensive overview of security best practices that developers should follow when developing applications. You’ll gain extensive knowledge on various practices, concepts, and processes for maintaining a secure environment, including DevSecOps practices that automate security integration across the software development lifecy"])</script><script>self.__next_f.push([1,"cle (SDLC), Static Application Security Testing (SAST) for identifying security flaws, Dynamic Analysis, and Dynamic Testing, and creating a Secure Development Environment, an ongoing process for securing a network, computing resources, and storage devices both on-premise and in the cloud. This course familiarizes you with the top Open Web Application Security Project (OWASP) application security risks such as broken access controls and SQL injections and teaches you how to prevent and mitigate these threats. This course includes multiple hands-on labs to develop and demonstrate your skills and knowledge for maintaining a secure development environment.\u003c/p\u003e496:T6c8,\u003cp\u003eThis comprehensive course offers an in-depth exploration of geotechnologies and their role in addressing social and ecological challenges. Geotechnologies encompass a wide array of tools and techniques, including Geographic Information Systems (GIS), remote sensing through imagery from drones, aircraft, and satellites, and precise positioning using GPS or Global Navigation Satellite Systems.\u003c/p\u003e\n\u003cp\u003eStructured around a rich blend of academic readings, instructional videos, and practical exercises, the curriculum delves into various social and environmental topics across multiple scales. Participants will acquire a thorough understanding of cutting-edge mapping tools and techniques. This includes learning about map projections, the art and science of map symbology, the principles of classification, and sophisticated spatial analysis methods.\u003c/p\u003e\n\u003cp\u003eA significant focus of the course is on experiential learning. You will have the opportunity to develop your web mapping applications, including interactive dashboards and multimedia story maps. Moreover, the program emphasizes fieldwork, allowing you to gather and integrate your field data into mapping projects.\u003c/p\u003e\n\u003cp\u003eBy the end of the course, you will have honed your skills in utilizing maps as powerful analytical instruments. This expertise will not only enhance your ability to interpret and present dat"])</script><script>self.__next_f.push([1,"a effectively but also empower you to contribute to creating a brighter, more sustainable, and resilient future. The course is designed to instill confidence and proficiency in using geotechnologies, equipping you with the knowledge and tools to make informed decisions and to advocate for meaningful change in addressing the pressing challenges of our time.\u003c/p\u003e497:T53b,\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eArticulate the relationship between maps and geotechnologies, what geotechnologies are, and why they are relevant to 21st-century issues and decision-making. \u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExplain and illustrate how maps are not simply reference documents but are analytical tools.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eExplain how maps help us to understand how things are \u003cstrong\u003eand\u003c/strong\u003e plan how things ought to be in the future (that is, for planning a more resilient, equitable, sustainable future).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDescribe the main types of maps, understand map scale, map projections, and selected map elements (legends, orientation, source).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDescribe the major societal implications of geographic information and why they matter; that is, how society influences mapping and how mapping influences society.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate maps from spreadsheets and existing geographic information.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eSymbolize point, line, area, and image information on maps and classify information using various methods.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFilter, generalize, and select geographic information on maps.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate a web map from a field survey.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUnderstand the major types of web mapping applications and communication methods: Dashboards, infographics, instant apps, and story maps.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e498:T6ea,\u003cp\u003eThis 2nd part of the Digital Earth series continues the journey of an in-depth exploration of geotechnologies and their role in addressing social and ecological challenges. Geotechnologies encompass a wide array of tools and techniques, including Geographic Information Systems (GIS), remote sensing through imagery from drone"])</script><script>self.__next_f.push([1,"s, aircraft, and satellites, and precise positioning using GPS or Global Navigation Satellite Systems.\u003c/p\u003e\n\u003cp\u003eStructured around a rich blend of academic readings, instructional videos, and practical exercises, the curriculum delves into various social and environmental topics across multiple scales. Participants will acquire a thorough understanding of cutting-edge mapping tools and techniques. This includes learning about map projections, the art and science of map symbology, the principles of classification, and sophisticated spatial analysis methods.\u003c/p\u003e\n\u003cp\u003eA significant focus of the course is on experiential learning. You will have the opportunity to develop your web mapping applications, including interactive dashboards and multimedia story maps. Moreover, the program emphasizes fieldwork, allowing you to gather and integrate your field data into mapping projects.\u003c/p\u003e\n\u003cp\u003eBy the end of the course, you will have honed your skills in utilizing maps as powerful analytical instruments. This expertise will not only enhance your ability to interpret and present data effectively but also empower you to contribute to creating a brighter, more sustainable, and resilient future. The course is designed to instill confidence and proficiency in using geotechnologies, equipping you with the knowledge and tools to make informed decisions and to advocate for meaningful change in addressing the pressing challenges of our time.\u003c/p\u003e499:T6c8,\u003cp\u003eThis comprehensive course offers an in-depth exploration of geotechnologies and their role in addressing social and ecological challenges. Geotechnologies encompass a wide array of tools and techniques, including Geographic Information Systems (GIS), remote sensing through imagery from drones, aircraft, and satellites, and precise positioning using GPS or Global Navigation Satellite Systems.\u003c/p\u003e\n\u003cp\u003eStructured around a rich blend of academic readings, instructional videos, and practical exercises, the curriculum delves into various social and environmental topics across multiple scales. Pa"])</script><script>self.__next_f.push([1,"rticipants will acquire a thorough understanding of cutting-edge mapping tools and techniques. This includes learning about map projections, the art and science of map symbology, the principles of classification, and sophisticated spatial analysis methods.\u003c/p\u003e\n\u003cp\u003eA significant focus of the course is on experiential learning. You will have the opportunity to develop your web mapping applications, including interactive dashboards and multimedia story maps. Moreover, the program emphasizes fieldwork, allowing you to gather and integrate your field data into mapping projects.\u003c/p\u003e\n\u003cp\u003eBy the end of the course, you will have honed your skills in utilizing maps as powerful analytical instruments. This expertise will not only enhance your ability to interpret and present data effectively but also empower you to contribute to creating a brighter, more sustainable, and resilient future. The course is designed to instill confidence and proficiency in using geotechnologies, equipping you with the knowledge and tools to make informed decisions and to advocate for meaningful change in addressing the pressing challenges of our time.\u003c/p\u003e49a:Tb7e,"])</script><script>self.__next_f.push([1,"\u003cp\u003eMeeting growing global energy demand, while mitigating climate change and environmental impacts, requires a large-scale transition to clean, sustainable energy systems. Students and professionals around the world must prepare for careers in this future energy landscape, gaining relevant skills and knowledge to expedite the transformation in industry, government and nongovernmental organizations, academia, and nonprofits.\u003c/p\u003e\n\u003cp\u003eThe building sector represents a large percentage of overall energy consumption, and contributes 40% of the carbon emissions driving climate change. Yet buildings also offer opportunities for substantial, economical energy efficiency gains. From retrofit projects to new construction, buildings require a context-specific design process that integrates efficiency strategies and technologies.\u003c/p\u003e\n\u003cp\u003eIn this course, you'll be introduced to a range of technologies and analysis techniques for designing comfortable, resource-efficient buildings.\u003c/p\u003e\n\u003cp\u003eThe primary focus of this course is the study of the thermal and luminous behavior of buildings. You'll examine the basic scientific principles underlying these phenomena, and use computer-aided design software and climate data to explore the role light and energy can play in shaping architecture.\u003c/p\u003e\n\u003cp\u003eThese efficiency design elements are critical to the larger challenge of producing energy for a growing population while reducing carbon emissions.\u003c/p\u003e\n\u003cp\u003e\u003cimg alt=\"AIA Logo\" src=\"https://images.ctfassets.net/ii9ehdcj88bc/vrN9upQ7PmUIlLfFiOK3T/5f865380c279439d9713dc3c5aec61ef/AIA.jpg?h=250\" /\u003e\u003c/p\u003e\n\u003cp\u003eSustainable Building Design on edX offers the opportunity for learners who are American Institute of Architecture (AIA) members to earn 22 learning units (LUs/Elective) if they purchase and earn the edX verified certificate for this course.\u003c/p\u003e\n\u003cp\u003eThe MIT Energy Initiative is a registered provider of AIA-approved continuing education under Provider Number 10009794. All registered AIA CES Providers must comply with the AIA Standards for Continuing Education Programs. Any questions or concerns about this provider or this learning program may be sent to AIA CES (cessupport@aia.org or (800) AIA 3837, Option 3).\u003c/p\u003e\n\u003cp\u003eThis learning program is registered with AIA CES for continuing professional education. As such, it does not include content that may be deemed or construed to be an approval or endorsement by the AIA of any material of construction or any method or manner of handling, using, distributing, or dealing in any material or product.\u003c/p\u003e\n\u003cp\u003eAIA continuing education credit has been reviewed and approved by AIA CES. Learners must complete the entire learning program to receive continuing education credit. AIA continuing education Learning Units earned upon completion of this course will be reported to AIA CES for AIA members. Certificates of Completion for both AIA members and non-AIA members are available upon request.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"49b:Tba8,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIn today's competitive business environment, the ability to create and present a compelling business case is critical for securing stakeholder buy-in, driving innovation or strategic initiatives. This course guides you through the fundamental elements of business case development, from initial concept to persuasive presentation. \u003c/p\u003e\n\u003cp\u003eYou’ll also explore the role of the project charter—a foundational document that aligns project objectives with organizational strategy and sets the stage for successful project execution. \u003c/p\u003e\n\u003cp\u003eThrough engaging lessons, you will explore the key components of a business case, including objectives, scope, risks, and return on investment. You will also learn how to conduct market analysis and risk assessments to support decision-making. With practical examples and proven frameworks like SWOT analysis and the PESTLE framework, you’ll gain confidence in presenting your proposal to stakeholders. \u003c/p\u003e\n\u003cp\u003eThe course also covers the fundamentals of a project charter, highlighting its role in aligning project goals with organizational strategy. You’ll define S.M.A.R.T. goals, understand business value, and establish the authority needed to lead a successful project. \u003c/p\u003e\n\u003cp\u003eThis course equips you with the tools and techniques needed to communicate project value and ensure stakeholder alignment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat You’ll Learn\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnderstanding Business Cases\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eGrasp the purpose, importance, and key components of a business case\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eResearch and Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIdentify problems or opportunities, conduct research, and analyze data to support your case\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eCrafting the Business Case\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDevelop viable options, make informed recommendations, and build a persuasive narrative\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003ePresenting the Business Case\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePrepare and deliver effective presentations tailored to diverse audiences, enhancing your communication and persuasion skills\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eUnderstanding Project Charters\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eExplore how projects drive innovation and growth in today’s world\u003c/li\u003e\n\u003cli\u003eLearn the core principles of project management and life cycle methodologies\u003c/li\u003e\n\u003cli\u003eGrasp the fundamentals of crafting a project charter, from defining project goals to aligning them with business objectives\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eBy the end of this course, you’ll possess the foundational skills to:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDevelop robust business cases that facilitate informed decision-making\u003c/li\u003e\n\u003cli\u003eAlign project goals with organizational strategies through effective project charters\u003c/li\u003e\n\u003cli\u003eCommunicate project objectives, scope, and value to stakeholders with confidence\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWhether you're new to project management or looking to refine your skills, this course provides the practical knowledge to enhance your career and contribute to organizational success.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"49c:Tde9,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis course covers the physics, concepts, theories, and models underlying the discipline of aerodynamics. A general theme is the technique of velocity field representation and modeling via source and vorticity fields, and via their sheet, filament, or point-singularity idealizations.\u003c/p\u003e\n\u003cp\u003eThe intent is to instill an intuitive feel for aerodynamic flowfield behavior, and to provide the basis of aerodynamic force analysis, drag decomposition, flow interference estimation, and many other important applications. A few computational methods are covered, primarily to give additional insight into flow behavior, and to identify the primary aerodynamic forces on maneuvering aircraft. A short overview of flight dynamics is also presented.\u003c/p\u003e\n\u003cp\u003eBefore your course starts, try the new edX Demo where you can explore the fun, interactive learning environment and virtual labs. \u003ca href=\"https://www.edx.org/course/edx/edxdemo101/edx-demo/1038\"\u003eLearn more\u003c/a\u003e.\u003c/p\u003e\n\u003ch3\u003e\u003c/h3\u003e\n\u003ch3\u003e\u003cstrong\u003eFAQ\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eIs there a required textbook?\u003c/strong\u003e\u003cbr /\u003e\nYou do not need to buy a textbook. All material is included in the edX course and is viewable online. This includes a full textbook in PDF form. If you would like to buy a print copy of the textbook, a mail-order service will be provided.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCan I still register after the start date?\u003c/strong\u003e\u003cbr /\u003e\nYou can register at any time, but you will not get credit for any assignments that are past due.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHow are grades assigned?\u003c/strong\u003e\u003cbr /\u003e\nGrades are made out of four parts: simple, multiple-choice \"Concept Questions \" completed during lectures; weekly homework assignments; and two exams, one at the midpoint and one at the end of the course.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHow does this course use video? Do I need to watch the lectures live?\u003c/strong\u003e\u003cbr /\u003e\nVideo lectures as well as worked problems will be available and you can watch these at your leisure. Homework assignments and exams, however, will have due dates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWill the text of the lectures be available?\u003c/strong\u003e\u003cbr /\u003e\nYes, transcripts of the course will be made available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWill the material be made available to anyone registered for this course?\u003c/strong\u003e\u003cbr /\u003e\nYes, all the material will be made available to all students.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat are the prerequisites?\u003c/strong\u003e\u003cbr /\u003e\nThe student is expected to be well-versed in basic mechanics, vector calculus, and basic differential equations. Good familiarity with basic fluid mechanics concepts (pressure, density, velocity, stress, etc.) is expected, similar to the content in 16.101x (however, 16.101x is not a requirement). If you do not know these subjects beforehand, following the class material will be extremely difficult. We do not check students for prerequisites, so you are certainly allowed to try.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWho can register for this course?\u003c/strong\u003e\u003cbr /\u003e\nUnfortunately, learners from Iran, Sudan, Cuba and the Crimea region of Ukraine will not be able to register for this course at the present time. While edX has received a license from the U.S. Office of Foreign Assets Control (OFAC) to offer courses to learners from Iran and Sudan our license does not cover this course. Separately, EdX has applied for a license to offer courses to learners in the Crimea region of Ukraine, but we are awaiting a determination from OFAC on that application. We are deeply sorry the U.S. government has determined that we have to block these learners, and we are working diligently to rectify this situation as soon as possible.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"49d:T76b,\u003cp\u003eWhat if you could detect species without observing them? What if nature sends out a reliable signal disclosing the presence of species that we can capture and interpret with great accuracy? How can we improve our efforts to monitor and conserve species habitats and ecosystems with such information in our hands?\u003c/p\u003e\n\u003cp\u003eOur comprehensive course offers a unique opportunity to delve into the world of eDNA and its groundbreaking role in understanding and safeguarding our natural ecosystems. Guided by our team of ecologists in the field, you'll learn about hands-on skills in sampling techniques, laboratory methodologies, and data interpretation. You will investigate ecological questions hidden within rivers, oceans, forests, and soils as you learn about analysing DNA samples present in the environment. Through the eDNA analysis approach, you'll gain the power to identify elusive species without disturbing their habitats. Imagine being able to monitor the presence of endangered creatures, invasive species, and even those on the brink of discovery, all while contributing to critical conservation efforts.\u003c/p\u003e\n\u003cp\u003eFollow our researchers as they take you along on their fieldwork in the tropical rainforests of Colombia, the coastal waters of Brittany, France and the forest reserves of Switzerland. Their research case studies will offer you the opportunity to dive into real-life environmental issues where scientific questions are intertwined with societal challenges. Through the different stages of their eDNA research, we can generate new insights about the diversity of species and the health of ecosystems for these cases.\u003c/p\u003e\n\u003cp\u003eWhether you're a conservationist, ecologist, an environmental policy expert, citizen scientist, or simply a nature enthusiast, this course promises to ignite your curiosity and equip you with tools to address today's pressing ecological challenges.\u003c/p\u003e49e:T412,\u003cp\u003eAcross organizations, managers are expected to have sound knowledge of finance and accounting. As part of their job, managers us"])</script><script>self.__next_f.push([1,"e large volumes of information produced by accounting systems to make business decisions every day.\u003c/p\u003e\n\u003cp\u003eThis business and management course will show you how accounting information is relevant to managers, and how it can be processed and analyzed for effective managerial decision-making. By examining accounting information that is extensively used across three key managerial functions of planning, decision-making and controlling, the course equips non-finance managers with basic accounting and finance skills. This course also discusses activity based costing, which provides insight on the cost structure of products and services.\u003c/p\u003e\n\u003cp\u003eWhat sets this course apart is the practicing manager-centric approach that is a part of each week of the course. Whether you are a student or a practicing manager, this course will allow you to easily follow all topics and directly apply concepts in practice.\u003c/p\u003e49f:T77f,\u003cp\u003eThis course begins by exploring the factors that set the Earth's temperature, considering the basic equation, Energy in = Energy out. We focus on the role of astronomical factors (sunspots and the eccentricity, obliquity, and precession of Earth in its orbit around the Sun), the reflectivity of Earth's surface, and the composition of the Earth's atmosphere in setting the Earth’s climate. \u003c/p\u003e\n\u003cp\u003eThe temperature record, based on instrumental measurements, strongly indicates that the Earth has been warming over the last several decades, and dramatically so since 1975 when Wally Broecker, a former lecturer in Frontiers of Science, first coined the term \"global warming.\" At the same time, the concentration of greenhouse gasses in the atmosphere, most prominently carbon dioxide, has been rapidly increasing. To put these recent changes in context of past data, we learn about paleoclimate proxies (e.g. tree rings and ice cores) and how it is that scientists can learn about the temperature and atmospheric content going back thousands and even millions of years ago. \u003c/p\u003e\n\u003cp\u003eWith this knowledge, we confront past dat"])</script><script>self.__next_f.push([1,"a, climate models, and fictions that lack scientific basis. We consider various tools used in climate science that allow scientists to compare contemporary climate change with natural changes that have occurred in the past, as well as to generate future climate forecasts. By investigating carbon isotope content of carbon dioxide in the atmosphere, we learn about the origin of the extra carbon and the role that humans have played in its release into the atmosphere. Finally, we explore the role of positive and negative feedback loops and why they make climate modeling particularly challenging. Feedback loops play an important role not only in climate, but in various biological processes, economics and more, and represent a critical scientific habit of mind taught in this course.\u003c/p\u003e4a0:T713,\u003cp\u003e\u003cem\u003ePlease Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!\u003c/em\u003e\u003c/p\u003e\r\n\u003cp\u003eBy enrolling in this course, chances are you've studied artifical intelligence, built chatbots and have perhaps even used Watson Assistant along the way. But did you know that you can turbocharge your chatbot's IQ with IBM Watson Discovery, a service designed to reveal the hidden value in your data? Discovery specializes in taking your data--structured or unstructured--and extracting from it answers and patterns.\u003c/p\u003e\r\n\u003cp\u003eFor example, if you have a large repository, the contents of which could answer customer questions, you've got the makings of a great FAQ chatbot.\u003c/p\u003e\r\n\u003cp\u003eIn this course, you'll learn how to build queries in Discovery, which allows you to surface answers and patterns from large repositories of data. You'll next learn to use Discovery to extract insights from a set of hotel reviews. Then, to make that data come to life, you'll integrate Discovery with other Watson services to create a chatbot that can tell you about the best"])</script><script>self.__next_f.push([1," hotels in a certain US city. By using these Watson services, you'll add more layers of analysis to help you find the best hotel.\u003c/p\u003e\r\n\u003cp\u003eYou'll build your chatbot application with the following Watson services:\u003c/p\u003e\r\n\u003cul\u003e\r\n\u003cli\u003eAssistant\u003c/li\u003e\r\n\u003cli\u003eDiscovery\u003c/li\u003e\r\n\u003cli\u003eTone Analyzer\u003c/li\u003e\r\n\u003cli\u003ePersonality Insights\u003c/li\u003e\r\n\u003c/ul\u003e\r\n\u003cp\u003eBy the end of this course, you will have built a fully functioning AI-powered chatbot. Moreover, you should be able to apply the services taught here to your own data sets, enabling you to create sophisticated chatbots of your own.\u003c/p\u003e4a1:T894,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIn this course, we go beyond the calculus textbook, working with practitioners in social, life and physical sciences to understand how calculus and mathematical models play a role in their work.\u003c/p\u003e\n\u003cp\u003eThrough a series of case studies, you’ll learn:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eHow standardized test makers use functions to analyze the difficulty of test questions;\u003c/li\u003e\n\u003cli\u003eHow economists model interaction of price and demand using rates of change, in a historical case of subway ridership;\u003c/li\u003e\n\u003cli\u003eHow an x-ray is different from a CT-scan, and what this has to do with integrals;\u003c/li\u003e\n\u003cli\u003eHow biologists use differential equation models to predict when populations will experience dramatic changes, such as extinction or outbreaks;\u003c/li\u003e\n\u003cli\u003eHow the Lotka-Volterra predator-prey model was created to answer a biological puzzle;\u003c/li\u003e\n\u003cli\u003eHow statisticians use functions to model data, like income distributions, and how integrals measure chance;\u003c/li\u003e\n\u003cli\u003eHow Einstein’s Energy Equation, E=mc2 is an approximation to a more complicated equation.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWith real practitioners as your guide, you’ll explore these situations in a hands-on way: looking at data and graphs, writing equations, doing calculus computations, and making educated guesses and predictions.\u003c/p\u003e\n\u003cp\u003eThis course provides a unique supplement to a course in single-variable calculus. Key topics include application of derivatives, integrals and differential equations, mathematical models and parameters.\u003c/p\u003e\n\u003cp\u003eThis course is for anyone who has completed or is currently taking a single-variable calculus course (differential and integral), at the high school (AP or IB) or college/university level. You will need to be familiar with the basics of derivatives, integrals, and differential equations, as well as functions involving polynomials, exponentials, and logarithms.\u003c/p\u003e\n\u003cp\u003eThis is a course to learn applications of calculus to other fields, and NOT a course to learn the basics of calculus. Whether you’re a student who has just finished an introductory Calculus course or a teacher looking for more authentic examples for your classroom, there is something for you to learn here, and we hope you’ll join us!\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4a2:T5c0,\u003cp\u003eAs primary sources of information are more frequently digitized and available online than ever before, how can we use those sources to ask new questions? How did Chinese families organize themselves and their landscapes in China’s past? How did African slaves from different cultures form communities in the Americas? What influences informed the creation and evolution of Broadway musicals? How can I understand or interpret 1,000 books all at once? How can I create a visualization that my students can interact with? The answers to these questions can be explored using a wide variety of digital tools, methods, and sources. \u003c/p\u003e\n\u003cp\u003eAs museums, libraries, archives and other institutions have digitized collections and artifacts, new tools and standards have been developed that turn those materials into machine-readable data. Optical Character Recognition (OCR) and the Text Encoding Initiative (TEI), for example, have enabled humanities researchers to processvastamounts of textual data. However, these advances are not limited just to text. Sound, images, and video have all been subject to these new forms of research. \u003c/p\u003e\n\u003cp\u003eThis course will show you how to manage the many aspects of digital humanities research and scholarship. Whether you are a student or scholar, librarian or archivist, museum curator or public historian — or just plain curious — this course will help you bring your area of study or interest to new life using digital tools.\u003c/p\u003e4a3:T6e2,\u003cp\u003eClimate change is arguably the greatest challenge of our time. Human activity has already warmed the planet by one degree Celsius relative to pre-industrial times, and we are feeling the effects through record heat waves, droughts, wildfires and flooding. If we continue to burn fossil fuels at the current rate, the planet will reach two degrees of warming by 2050—the threshold that many scientists have identified as a dangerous tipping point. What is the science behind these projections?\u003c/p\u003e\n\u003cp\u003eJoin climate science expert Michael Mann to learn abo"])</script><script>self.__next_f.push([1,"ut the basic scientific principles behind climate change and global warming. We need to understand the science in order to solve the broader environmental, societal and economic changes that climate change is bringing.\u003c/p\u003e\n\u003cp\u003eBy the end of this course, you will:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDevelop a deep scientific understanding of HOW the climate system has been changing;\u003c/li\u003e\n\u003cli\u003eArticulate WHY the climate system is changing;\u003c/li\u003e\n\u003cli\u003eUnderstand the nature of these changes;\u003c/li\u003e\n\u003cli\u003eDevelop a systems thinking approach to analyzing the impacts of climate change on both natural and human systems.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe course covers the basic principles of atmospheric science, methods of climate data collection and tracking of greenhouse gas emissions. It introduces basic climate modeling and explores the impact of various greenhouse gas emissions scenarios. Finally, it outlines the impacts of climate change on environmental, social, economic and human systems, from coral reefs and sea level rise to urban infrastructure. The course follows the general outline of the \u003ca href=\"https://www.ipcc.ch/report/ar5/syr/\"\u003e5th Assessement Report of the United Nations Intergovernmental Panel on Climate Change\u003c/a\u003e.\u003c/p\u003e4a4:T8a4,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cspan lang=\"EN\"\u003eThe rapid evolution of computers has brought technical devices as an active weapon to criminals. Cybercriminals have enjoyed the pleasure of being able to combine a large array of complex technologies to be successful in their mission. Due to the complexity of the attack, investigating a crime in the cyber world has become increasingly difficult to do.\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eComputer forensics is the process of detecting hacking attacks and properly extracting evidence to report the crime and conducting audits to prevent the future attacks. It is used in different types of investigations like crime and civil investigation, corporate litigation, cybercrime etc. It plays a vital role in the investigation and prosecution of cybercriminals. It refers to a set of methodological procedures and techniques to identify, gather, preserve, extract, interpret, document, and present evidence from computing equipment so that the discovered evidence can be used during a legal and/or administrative proceeding in a court of law. Evidence might be sought in a wide range of computer crime or misuse, including but not limited to theft of trade secrets, theft of or destruction of intellectual property, and fraud.\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eDigital Forensics Essentials (DFE) is a security program covering the fundamental concepts of computer forensics. It equips students with the skills required to identify an intruder’s footprints and to properly gather the necessary evidence to prosecute in a court of law.\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis program will give a holistic overview of the key components of computer forensics. It provides a solid fundamental knowledge required for a career in computer forensics.\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eWhy is DFE Important?\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e§\u003cspan lang=\"EN\"\u003e It facilitates your entry into the world of computer forensics\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e§\u003cspan lang=\"EN\"\u003e It provides a professional understanding of the concepts of computer forensics\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e§\u003cspan lang=\"EN\"\u003e It enhances your skills as a Computer Forensics Specialist and increases your employability\u003c/span\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4a5:T541,\u003cp\u003e\u003cspan lang=\"EN\"\u003eStudents going through DFE training will learn:\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e§\u003cspan lang=\"EN\"\u003e Key issues plaguing the computer forensics\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e§\u003cspan lang=\"EN\"\u003e Different types of digital evidence\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e§\u003cspan lang=\"EN\"\u003e Computer forensic investigation process and its phases\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e§\u003cspan lang=\"EN\"\u003e Different types of disk drives and file systems\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e§\u003cspan lang=\"EN\"\u003e Data acquisition methods and data acquisition methodology\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e§\u003cspan lang=\"EN\"\u003e Anti-forensics techniques and countermeasures\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e§\u003cspan lang=\"EN\"\u003e Volatile and non-volatile information gathering from Windows, Linux, and Mac Systems\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e§\u003cspan lang=\"EN\"\u003e Network forensics fundamentals, event correlation, and network traffic investigation\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e§\u003cspan lang=\"EN\"\u003e Web server logs and web applications forensics\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e§\u003cspan lang=\"EN\"\u003e Dark web forensics\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e§\u003cspan lang=\"EN\"\u003e Email crime investigation\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e§ Malware forensics fundamentals and different types of malware analysis\u003c/p\u003e4a6:T7b6,\u003cp\u003eBy the end of the course, you will be able to…\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eMaster your critical thinking style. Identify the key components of successful critical thinking, and how to identify and gather information using the critical thinking loop.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRecognize your patterns. Identify the different types of critical thinking - reactive, unconscious, inflexible and “conscious creative” - including your own.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eBecome Self Aware. Become conscious of accountability and bias when building clear and strategic solutions.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eAbout CQ: Communication Quotient™\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis course draws on \u003ca href=\"https://www.communicationq"])</script><script>self.__next_f.push([1,"uotient.com/\"\u003eCQ: Communication Quotient™ strategies and models\u003c/a\u003e. Based on cutting edge psychology, neuroscience and communication research, CQ assesses your habits and tendencies to help you better understand who you are and how you communicate with the world around you.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHollywood storytelling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData scientist Dr. Beck and psychologist Dr. Faye have been training sentient robot, E.I., in how to develop CQ skills. Now, E.I. wants to understand what it means to be a Critical Thinker. Share in E.I.’s journey as she interacts with different types of thinkers and reaps the rewards that come from applying critical thinking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExpert knowledge\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBenefit from the combined wisdom of behavioral and communication experts and neuroscientists, with accompanying statistics and relatable examples. Created by BoxPlay, an Emmy award-winning company on a mission to democratize career success.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e‍Visual learning and actionable content\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur innovative, multimedia, step-by-step approach walks you through the entire process of becoming a Conscious Creative Critical Thinker. Beautiful infographics and engaging gifs help explain the strategies behind the CQ: Communication Quotient™ methodology.\u003c/p\u003e4a7:T54b,\u003cp\u003eIn this undergraduate-level biostatistics course, the learners will be introduced to the use of statistics and study designs in biology. Upon successful completion of this course, learners will be able to design experimental, quasi-experimental and observational studies that will meet regulatory guidelines; collect, analyze, and interpret data using appropriate statistical tools. These are skills utilized in bio-statistical research in healthcare as well as other biology related fields. The importance of having these skills is recognized within public health sectors relating to analysis of drug effectiveness and risk factors for different illnesses, effectiveness of heath care interventions as well as helping explain biological phenomena"])</script><script>self.__next_f.push([1,".\u003c/p\u003e\n\u003cp\u003eIn addition to fulfilling requirements in undergraduate college programs, this undergraduate level course also provides future healthcare professionals with foundational coursework required for successful entry into a health professions graduate program or medical school. This course may also spark further interest for the learner towards a more advanced degree in biostatistics. The field of biostatistics provides an increasing opportunity for employment. According to the United States Bureau of Labor Statistics, this field will experience a 14% employment growth between 2010 and 2020.\u003c/p\u003e4a8:T56e,\u003cp\u003eMarketing for small businesses and start-ups is essential. Do you have the know-how and tools to position your venture for growth? Rice University’s online business courses for entrepreneurs provide a convenient, yet interactive and hands-on, way to learn or brush up on practical entrepreneurship skills.\u003c/p\u003e\n\u003cp\u003eTaught by industry professionals from Rice Business, the \u003ca href=\"https://business.rice.edu/rice-mba/entrepreneurial-mindset\"\u003e#1-ranked Entrepreneurship Graduate Program\u003c/a\u003e in the U.S. (Princeton Review, Entrepreneur Magazine), learners will focus on core competencies like strategic thinking, audience analysis, engagement techniques, and practical marketing planning. Although 100% online, the course is hands-on and will enable you to develop marketing plans, customer journey maps, and even more tools to immediately implement as a busy entrepreneur or small business owner. Through a combination of engaging lectures, practical case studies, group discussions, interviews, and hands-on exercises, students will gain invaluable insights into the principles and best practices that define successful small business ventures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRecommendation:\u003c/strong\u003e To gain the foundations of your business venture, we recommend completing the first course in this series, Small Business Entrepreneurship - Starting a Business, before starting Marketing a Business.\u003c/p\u003e4a9:T54a,\u003cp\u003e\u003cstrong\u003eYou will be able to:\u003c/st"])</script><script>self.__next_f.push([1,"rong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDevelop a comprehensive business positioning statement that accurately reflects the brand identity and unique value proposition of their entrepreneurial venture.\u003c/li\u003e\n\u003cli\u003eIdentify and analyze target market segments, utilizing demographic and psychographic data to effectively engage potential customers.\u003c/li\u003e\n\u003cli\u003eCreate detailed customer journey maps that outline the touchpoints and experiences customers have with the business, optimizing these for greater satisfaction and retention.\u003c/li\u003e\n\u003cli\u003eFamiliarize yourself with the legal and regulatory aspects of launching and operating a small business, including business structures, permits, licenses, and intellectual property protection.\u003c/li\u003e\n\u003cli\u003eMaster the art of selling, customer acquisition, and retention strategies to ensure long-term business success.\u003c/li\u003e\n\u003cli\u003eScaling and Growth: Explore techniques to scale and expand your business while maintaining its core values and objectives.\u003c/li\u003e\n\u003cli\u003eUtilize various marketing tools and techniques to design and implement engagement strategies that build strong, lasting relationships with customers.\u003c/li\u003e\n\u003cli\u003eConstruct a robust marketing plan that includes goals, strategies, and metrics for success, ensuring alignment with overall business objectives and facilitating sustainable growth.\u003c/li\u003e\n\u003c/ul\u003e4aa:Td77,"])</script><script>self.__next_f.push([1,"\u003cp\u003eEvery employee is involved in business processes to create products or services. The causes of decreasing customer satisfaction and increasing quality costs are often unknown, so derived solutions often only address symptoms. Six Sigma methods and tools enable a systematic solution of typical process problems and lead to sustainable operational excellence.\u003c/p\u003e\n\u003cp\u003eGo from Yellow to Green Belt in this project-based Lean Six Sigma course. With the TUM Yellow Belt, you have mastered the body of knowledge of our Green Belt (according to the American Society for Quality standards). Our Green Belt certification requires the implementation of a Six Sigma project (as recommended by the International Society of Six Sigma Professionals), just as the driving experience is necessary to obtain a driver’s license.\u003c/p\u003e\n\u003cp\u003eTo earn the TUM Lean Six Sigma Green Belt certification, you will implement a predefined standard project on environmental littering. The goal: “Improve the cleanliness of areas around selected places in your hometown and control the sustainability of your measures.” This project topic supports the \u003ca href=\"https://sdgs.un.org/goals\"\u003eUnited Nations Sustainability Goal #11: Sustainable Cities and Communities\u003c/a\u003e. To reach this goal you will drive along the DMAIC, accompanied by a Master Black Belt as co-pilot. The route is determined by our navigation software (sigma guide). We will stop at every important sigma tool, which you will then apply in practice and document in a project storybook. This storybook will demonstrate the operational excellence of and in your work. (Please note: The implementation of company-specific, individually supported business projects for certification are not included in this edX/TUM course)\u003c/p\u003e\n\u003cp\u003eWe will guide you through your improvement project. Each DMAIC phase concludes with a project review by a Lean Six Sigma Master Black Belt. With the feedback on your achieved project results, we keep you on track before you start the next DMAIC phase. You will also participate in weekly live sessions online, where we will discuss the tools and logic of Six Sigma in-depth and answer any questions. The e-book for the course, \u003ca href=\"https://link.springer.com/book/10.1007/978-3-030-31915-1\"\u003eSix Sigma Green Belt Certification Project\u003c/a\u003e, is included in the course price.\u003c/p\u003e\n\u003cp\u003eGreen Belt Certification: Learners will be awarded the TUM Lean Six Sigma Green Belt Certification after completing this course and all of its requirements, including:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePrevious acquisition of the TUM Lean Six Sigma Yellow Belt certificate,\u003c/li\u003e\n\u003cli\u003eApplication of the sigma tools (sigma guide),\u003c/li\u003e\n\u003cli\u003eDocumentation of the results in a project storybook,\u003c/li\u003e\n\u003cli\u003eFive graded reviews of the project storybook along with the DMAIC phases,\u003c/li\u003e\n\u003cli\u003eParticipation in at least 10 open-online-sessions,\u003c/li\u003e\n\u003cli\u003eDelivery of the completed project storybook and the collected data. \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOnce a year we present the \u003cstrong\u003eEnvironment Green Belt Award\u003c/strong\u003e for the best Lean Six Sigma certification project. In addition to the basic certification criteria - a correctly implemented and documented project - an excellent project is required, with a project sponsor and a strong, sustainable reduction of litter at a local hotspot.\u003c/p\u003e\n\u003cp\u003eThis course won the 2021 Runner-Up Award - Blended Learning - Academic Division 2021 of the International E-Learning Association (IELA)\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4ab:T40c,\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eTo identify\u003c/strong\u003e and define suitable Six Sigma project topics.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eTo implement\u003c/strong\u003e a Six Sigma project along with its DMAIC phases with all relevant qualitative and quantitative tools.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eTo evaluate\u003c/strong\u003e the success of a Six Sigma project with process performance indicators and the derived financial benefits.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eTo apply\u003c/strong\u003e all relevant DMAIC tools, for example, Project Definition, Voice of Customer/CtQ's, Project Charter, Process Mapping, Cause \u0026amp; Effect Matrix, Hypotheses, and their Statistical Tests, Root-Cause-Analysis, etc.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cspan lang=\"EN-GB\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIELA Award 2021 -\u003cspan lang=\"EN-GB\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis course won the 2021 \u003cem\u003eRunner-Up Award - Blended Learning - Academic Division 2021\u003c/em\u003e of the International E-Learning Association (IELA) { \u003cspan lang=\"EN-GB\"\u003e\u003ca href=\"http://www.ielassoc.org/awards_program/past_winners.html\" title=\"Award\"\u003ehttp://www.ielassoc.org/awards_program/past_winners.html }\u003c/a\u003e\u003c/span\u003e\u003c/p\u003e4ac:T7f9,\u003cp\u003eIn this course you will learn the basics of several machine learning topics to help you solve real life challenges. Unsupervised learning techniques such as clustering and dimensionality reduction are useful to make sense of large and/or high dimensional datasets that are not annotated. Deep learning is a supervised learning technique that is useful to train neural networks to solve more complicated classification and regression tasks. Finally, reinforcement learning techniques can be used to train AI agents that interact with an environment.\u003c/p\u003e\n\u003cp\u003eUsing hands-on and interactive exercises you will get insight into the fundamental algorithms and basic concepts of:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClustering\u003c/strong\u003e is used to identify similar data/objects and patterns from your engineering datasets. It is a technique that is especially useful if you don’t have labeled or annotated data. We explain various approaches to clustering and cover how similarity and dissimilarity measures are used.\u003c"])</script><script>self.__next_f.push([1,"/p\u003e\n\u003cp\u003e\u003cstrong\u003eDimensionality reduction techniques\u003c/strong\u003e are used to reduce the number of features representing a given dataset, while retaining the structure of the dataset. We discuss feature selection and feature extraction techniques such as Principal Component Analysis (PCA), and how and when to apply it.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeep Learning\u003c/strong\u003e is a family of machine learning methods based on artificial neural networks. You will learn how to build and train deep neural networks consisting of fully connected neural networks of multiple hidden layers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReinforcement learning\u003c/strong\u003e teaches an AI to interact with an environment. We cover basic reinforcement learning concepts and techniques, such as how to model the system using a Markov Decision Process, and how to train an optimal policy using tabular Q-learning using the Bellman equation.\u003c/p\u003e\n\u003cp\u003eThis course is designed by a team of TU Delft machine learning experts from various backgrounds, highlighting the various topics from their individual perspectives.\u003c/p\u003e4ad:T4b2,\u003cp\u003e\u003cspan lang=\"ES\"\u003e\u003c/span\u003eThis course will allow you to use mathematical equations to describe and analyze certain problems that appear in the areas of business and finance. For example, it analyzes how the performance of an asset or distributing a product is modeled, how an optimization process is done in a portfolio or how aversion to risk can be described to an investor. Additionally, it will help you understand how certain algorithms are solved to analyze large amounts of data, make forecasts and describe trends. The course uses programming and simulation tools to convey the main concepts.\u003c/p\u003e\n\u003cp\u003eThe course starts from the concept of function, that is the basis of mathematical modeling and allows identifying the relationship between a group of variables of interest. Different types of functions are analyzed , such as linear, polynomial and exponential functions, providing specific examples to the areas of finance and business. The concepts are supported with the help of pac"])</script><script>self.__next_f.push([1,"kages such as R / Python or Matlab. In this way, interactive and visual learning is motivated, which in addition to transmitting mathematical concepts, establishes the bases to develop computational skills.\u003c/p\u003e4ae:T4a4,\u003cp\u003eThis course, presented by the Statistics Department, provides participants with an introduction to the compilation of monetary statistics covering the central bank (CB), other depository corporations (ODCs) and other financial corporations (OFCs) in accordance with international statistical standards. Course materials are based on the 2016 Monetary and Financial Statistics Manual and Compilation Guide (MFSMCG). The course discusses the principles of residency and sectoring of institutional units, the characteristics and types of financial instruments, valuation principles, and other accounting issues that are relevant to the compilation of monetary statistics. Participants will also become familiar with the defining characteristics of depository corporations (DCs), notably their role as money issuers, and with the main principles on which analysis of monetary and credit aggregates is based. They will also gain a deeper understanding of the OFCs sector and their relevance for compiling a broader and often more reliable measure of the liquidity available in an economy and financing extended to the nonfinancial sectors and nonresidents by the financial corporations.\u003c/p\u003e4af:T6e7,\u003cp\u003eWith the cost of cyber breaches skyrocketing, robust cyber defense architecture is more critical than ever. Businesses need talented professionals with the right cyber defense skills! This course provides a comprehensive guide to detecting, responding to, and preventing cyberattacks across multiple security domains. \u003c/p\u003e\n\u003cp\u003eDuring the course, you’ll learn how to detect cyber threats using SIEM and XDR systems and utilizing advanced monitoring, analysis, and threat hunting techniques. You’ll gain insights into SOAR systems, automation, and orchestration, as well as breach notifications. You’ll build your understandin"])</script><script>self.__next_f.push([1,"g of IAM as the new perimeter of security and explore concepts like multifactor authentication (MFA) and privileged access management (PAM). \u003c/p\u003e\n\u003cp\u003eAdditionally, you’ll delve into network security, looking at firewalls, VPNs, and SASE, alongside application security practices such as secure coding, vulnerability testing, and DevSecOps. Plus, you’ll learn about data security through governance, compliance, detection, and response strategies. \u003c/p\u003e\n\u003cp\u003eThe course culminates in a hands-on final project where you'll design a secure network architecture and analyze real-world cybersecurity tools. Plus, a case study featuring IBM’s security solutions will deepen your understanding before you complete a final exam that will test your grasp of the fundamentals and key security concepts. \u003c/p\u003e\n\u003cp\u003eIf you’re looking to build the job-ready skills in cyber defense architecture employers need, ENROLL TODAY and get ready to add in-demand skills to your resume in just 5 weeks! \u003c/p\u003e\n\u003cp\u003ePrerequisites: To enroll for this course you need to have a basic knowledge of networks, cloud computing, security concepts, and terminology.\u003c/p\u003e4b0:T522,\u003cp\u003eThis course is one of the 5 courses of an introductory business information systems series, designed to introduce you to the amazing world of Information Technology. \u003c/p\u003e\n\u003cp\u003eThe series of courses is designed so that a professional in a field outside the information technologies (financial, administrative or managerial) acquires the basic knowledge in Information Technology to be able to interact more profitably with the computer and telecommunications specialists of your company or other corporations with which you are related, being able to specify requirements, evaluate workloads and monitor results in a much more effective way. \u003c/p\u003e\n\u003cp\u003eThe syllabus of the series is based on the CLEP Information Systems and Computer Applications exam. \u003c/p\u003e\n\u003cp\u003eIn this course you will learn the basics of software programming. We will address the basic logic behind any computer program, what types and stru"])</script><script>self.__next_f.push([1,"ctures of data and files are used, how object-oriented programming works, database management and SQL language and various concepts and guidelines in web development such as HTML, XML, CSS or javascript, among others. \u003c/p\u003e\n\u003cp\u003eBy completing the series of 5 courses, you will be prepared to interact effectively with specialists in the Information Technology sector (and pass the CLEP ISCA exam if you wish).\u003c/p\u003e4b1:T71a,\u003cp\u003eIn this online course, you will learn the theory behind the design of the Value Added Tax (VAT) gap estimation model of the International Monetary Fund’s Revenue Administration Gap Analysis Program (RA-GAP), and you will also learn how to use the model to produce your own VAT gap estimates. You will become familiarized with the overall structure of the model, and how each of its components interacts. You will learn what the inputs needed for the model are, how to prepare them, and how the model uses these inputs to compute the potential VAT, which is compared to the actual VAT to determine the VAT gap. \u003c/p\u003e\n\u003cp\u003eThe online course comprises five main components, or modules. The first module starts by providing some general background on the concept of tax gaps, and then covers the theory behind the design of the VAT gap estimation model. The second module moves on to looking at the various policy structures of a VAT are to be input into the model. The third module provides instructions on how various measures of actual VAT are to be prepared for use in the model, and the reason for why these measures are needed. The fourth module focusses on the compilation of the statistical data needed to construct the potential VAT base, and how this potential VAT base is input into the model. The final module then returns to the model and demonstrates how to execute the model to obtain your results, and, more importantly, how to review and interpret those results. \u003c/p\u003e\n\u003cp\u003eIn short, this course is designed to enable countries to produce VAT gap estimates on a regular and consistent basis, using the VAT gap estimat"])</script><script>self.__next_f.push([1,"ion model of the IMF’s RA-GAP program; which is a well-established tax gap model. \u003c/p\u003e\n\u003cp\u003eThis online course is offered by the IMF with financial support from the Government of Japan.\u003c/p\u003e4b2:T627,\u003cp\u003ePer fare questo occorre capire l'importanza della metodologia della ricerca, non sempre immediatamente evidente a chi si trova a leggere un saggio sociologico, né tanto meno ai giovani che intraprendono un percorso di studio nel campo delle scienze sociali. Il corso, dunque, affronta il tema della logica del metodo scientifico e della sua applicazione nelle scienze sociali. L'obiettivo è di consentire agli studenti di impostare e condurre correttamente il lavoro di indagine empirica, nonché di orientare la scelta degli strumenti di raccolta dati in relazione ai diversi tipi di ricerca, fornendo indicazioni circa la loro costruzione e somministrazione. Il corso comprende anche una introduzione alle tecniche di analisi statistiche con cui è possibile trovare risposte agli interrogativi iniziali formulati in sede di disegno\u003cbr /\u003e\ndella ricerca. \u003c/p\u003e\n\u003cp\u003eTo achieve this, we need to understand the importance of research methodology, and this is not immediately obvious to people reading a sociology book, or even to students starting their Social Sciences course. This MOOC, therefore, looks at logic and the scientific approach and how it is applied to the Social Sciences. The objective is to enable students to learn how to set up and implement an empirical study, including the right choice of instruments for data collection according to the specific study, and how to construct and administer them. The course also provides an introduction to statistical analysis which enables us to answer the questions that were set when the research was designed.\u003c/p\u003e4b3:Ta13,"])</script><script>self.__next_f.push([1,"\u003cp\u003eDive into the dynamic world of cartography with our comprehensive course, \"Planet Earth and Geovisualization.\" This two-part series is designed to equip students with both theoretical knowledge and practical skills in modern map-making and geospatial analysis. Throughout this course, students will explore the art and science of cartography, focusing on the principles and techniques essential for effective map communication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLearning Objectives:\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eExamine the Art and Science of Cartography\u003c/strong\u003e :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eUnderstand the fundamental principles and history of cartography.\u003c/li\u003e\n\u003cli\u003eAnalyze the key components that contribute to effective map communication, including scale, projection, and symbolization.\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eCreate Vector Map Data from Raster Maps\u003c/strong\u003e :\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLearn to convert raster map data into vector formats.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDevelop skills in organizing and managing geospatial data within a geodatabase.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDesign Choropleth Maps\u003c/strong\u003e :\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMaster the creation of choropleth maps to visually represent spatial data.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eApply appropriate data classification techniques and symbology to convey clear and accurate information.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDevelop Graduated Symbol Maps\u003c/strong\u003e :\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate graduated symbol maps to represent varying data magnitudes.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eImplement effective data classification techniques and symbology for enhanced visual interpretation.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eConstruct Isarithmic Maps\u003c/strong\u003e :\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLearn to produce isarithmic maps to illustrate continuous data surfaces.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUtilize appropriate data classification techniques and symbology for accurate representation of phenomena.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eRender 3D Digital Elevation Models (DEMs)\u003c/strong\u003e :\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTransform DEMs into 3D visualizations for advanced geospatial analysis.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePerform viewshed and line of sight calculations to assess visibility and spatial relationships in a three-dimensional context.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBy the end of this course, students will have a thorough understanding of cartographic principles and will be proficient in using various mapping techniques to create informative and visually compelling maps. Whether you're a novice or an experienced GIS professional, this course will enhance your ability to communicate spatial information effectively through innovative and precise map design.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4b4:Ta50,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIn this two-part series you will dive into the dynamic world of cartography with our comprehensive course, \"Planet Earth Geovisualization, Deeper Diver - Micromasters.\" These courses are designed to equip students with both theoretical knowledge and practical skills in modern map-making and geospatial analysis. Throughout the courses, students will explore the art and science of cartography, focusing on the principles and techniques essential for effective map communication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLearning Objectives:\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eExamine the Art and Science of Cartography\u003c/strong\u003e :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eUnderstand the fundamental principles and history of cartography.\u003c/li\u003e\n\u003cli\u003eAnalyze the key components that contribute to effective map communication, including scale, projection, and symbolization.\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eCreate Vector Map Data from Raster Maps\u003c/strong\u003e :\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLearn to convert raster map data into vector formats.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDevelop skills in organizing and managing geospatial data within a geodatabase.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDesign Choropleth Maps\u003c/strong\u003e :\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMaster the creation of choropleth maps to visually represent spatial data.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eApply appropriate data classification techniques and symbology to convey clear and accurate information.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eDevelop Graduated Symbol Maps\u003c/strong\u003e :\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCreate graduated symbol maps to represent varying data magnitudes.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eImplement effective data classification techniques and symbology for enhanced visual interpretation.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eConstruct Isarithmic Maps\u003c/strong\u003e :\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLearn to produce isarithmic maps to illustrate continuous data surfaces.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUtilize appropriate data classification techniques and symbology for accurate representation of phenomena.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cstrong\u003eRender 3D Digital Elevation Models (DEMs)\u003c/strong\u003e :\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eTransform DEMs into 3D visualizations for advanced geospatial analysis.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePerform viewshed and line of sight calculations to assess visibility and spatial relationships in a three-dimensional context.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBy the end of this two-part series, students will have a thorough understanding of cartographic principles and will be proficient in using various mapping techniques to create informative and visually compelling maps. Whether you're a novice or an experienced GIS professional, this course will enhance your ability to communicate spatial information effectively through innovative and precise map design.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4b5:T972,"])</script><script>self.__next_f.push([1,"\u003cp\u003eRandomness is inherent in all processes including manufacturing. The fundamental concepts taught in this course will help learners develop powerful statistical process control methods that are the foundation of world-class manufacturing quality.\u003c/p\u003e\n\u003cp\u003eAs part of the Principles of Manufacturing MicroMasters program, this course will introduce statistical methods that apply to any unit manufacturing process. We will cover the following topics:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRecognizing inherent variability in continuous production\u003c/li\u003e\n\u003cli\u003eIdentifying sources of process output variation\u003c/li\u003e\n\u003cli\u003eDescribing variation in a structured manner\u003c/li\u003e\n\u003cli\u003eApplying basic probability and statistics concepts to characterize process variation\u003c/li\u003e\n\u003cli\u003eDifferentiating between design specifications and process capability\u003c/li\u003e\n\u003cli\u003eSynthesizing novel approaches to unfamiliar situations by extending the core material (i.e. go beyond the “standard” uses).\u003c/li\u003e\n\u003cli\u003eAssessing the appropriateness of various statistical methods for a variety of problems\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDevelop the engineering and management skills needed for competence and competitiveness in today’s manufacturing industry with the Principles of Manufacturing MicroMasters Credential, designed and delivered by MIT’s #1-ranked Mechanical Engineering department in the world. Learners who pass the 8 courses in the program will earn the MicroMasters Credential and qualify to apply to gain credit towards MIT’s Master of Engineering in Advanced Manufacturing \u0026amp; Design program.\u003c/p\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003cp\u003ePlease note: edX Inc. has recently entered into an \u003ca href=\"https://news.mit.edu/2021/mit-harvard-transfer-edx-2u-0629\"\u003eagreement to transfer the edX platform to 2U, Inc\u003c/a\u003e., which will continue to run the platform thereafter. The sale will not affect your course enrollment, course fees or change your course experience for this offering. It is possible that the closing of the sale and the transfer of the edX platform may be effectuated sometime in the Fall while this course is running. Please be aware that there could be changes to the edX platform Privacy Policy or Terms of Service after the closing of the sale. However, 2U has committed to preserving robust privacy of individual data for all learners who use the platform. For more information see the \u003ca href=\"https://support.edx.org/hc/en-us/articles/4403415754007-edX-and-2U\"\u003eedX Help Center\u003c/a\u003e.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4b6:Tcc3,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis course focuses on early-stage biotechnology companies, with particular emphasis on understanding the underlying science, technology, and disease targets—together with the application of novel business structures and financing methods—to facilitate drug discovery, clinical development, and greater patient access to new therapies.\u003c/p\u003e\n\u003cp\u003eCurrent research is enhancing our understanding of the genetic, molecular, and cellular bases of many human diseases, and is leading to many new types of biotherapeutics that we will cover in this course, including recombinant therapeutic proteins; monoclonal antibodies and antibody drug conjugates; cancer immunotherapies, replacement cells and genetically engineered cells; and nucleic acid and gene therapies. Translating these discoveries into drugs and diagnostics increasingly requires the establishment of for-profit companies, but funding for early-stage development of novel therapies is becoming scarcer, especially for therapeutics for “rare” diseases that affect small populations. The dearth of funding for early-stage biotherapeutics companies in the so-called “Valley of Death” can be attributed to several factors, but a common thread is increasing financial risks in the biopharma industry and greater uncertainty surrounding the scientific, medical, economic, political, and academic environments within the biomedical ecosystem. Increasing risk and uncertainty inevitably leads to an outflow of capital as investors and other stakeholders seek more attractive opportunities in other industries.\u003c/p\u003e\n\u003cp\u003eBy applying financial techniques such as portfolio theory, securitization, and derivative securities to biomedical contexts, more efficient business and funding structures can be developed to reduce financial risks, lower the cost of capital, and bring more life-saving therapies to patients faster. Thus this course will also cover basic financial analysis for the life-sciences professional; the historical financial risks and returns of the biotech and pharmaceutical industries; the evaluation of the science and business potential as well as the mechanics of financing biotech startups; capital budgeting for biopharmaceutical companies; and applications of financial engineering in drug royalty investment companies, biomedical megafunds, drug approval swaps, and life sciences investment banking.\u003c/p\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003cp\u003ePlease note: edX Inc. has recently entered into an \u003ca href=\"https://news.mit.edu/2021/mit-harvard-transfer-edx-2u-0629\"\u003eagreement to transfer the edX platform to 2U, Inc\u003c/a\u003e., which will continue to run the platform thereafter. The sale will not affect your course enrollment, course fees or change your course experience for this offering. It is possible that the closing of the sale and the transfer of the edX platform may be effectuated sometime in the Fall while this course is running. Please be aware that there could be changes to the edX platform Privacy Policy or Terms of Service after the closing of the sale. However, 2U has committed to preserving robust privacy of individual data for all learners who use the platform. For more information see the \u003ca href=\"https://support.edx.org/hc/en-us/articles/4403415754007-edX-and-2U\"\u003eedX Help Center\u003c/a\u003e.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4b7:T4ff,\u003cp\u003eAre you interested in what’s happening in your global community? Explore economic, social, political, and environmental issues through the lens of geography.\u003c/p\u003e\n\u003cp\u003eBy exploring human influences and patterns, you can better understand the world around you, make predictions, and propose solutions to current issues. In this course, you will investigate geographic perspectives and analyze historical and current patterns of migration, population, political organization of space, agriculture, food production, land use, industrialization and economic development.\u003c/p\u003e\n\u003cp\u003eIn addition, you will learn helpful strategies for answering multiple-choice questions and free response essay questions on the AP Human Geography test.\u003c/p\u003e\n\u003cp\u003eEach of the seven modules in this course aligns with the concepts in the Advanced Placement* Human Geography course.\u003c/p\u003e\n\u003cp\u003eThis course is specifically designed for students who are interested in learning more about the AP Human Geography course before enrolling, supplementary support and exam review, and for use in blended learning classrooms.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e* Advanced Placement® and AP® are trademarks registered and/or owned by the College Board, which was not involved in the production of, and does not endorse, these offerings.\u003c/em\u003e\u003c/p\u003e4b8:T412,\u003cp\u003eSimulation Neuroscience is an emerging approach to integrate the knowledge dispersed throughout the field of neuroscience. \u003c/p\u003e\n\u003cp\u003eThe aim is to build a unified empirical picture of the brain, to study the biological mechanisms of brain function, behaviour and disease. This is achieved by integrating diverse data sources across the various scales of experimental neuroscience, from molecular to clinical, into computer simulations. \u003c/p\u003e\n\u003cp\u003eThis is a unique, massive open online course taught by a multi-disciplinary team of world-renowned scientists.In this first course, you will gain the knowledge and skills needed to create simulations of biological neurons and synapses. \u003c/p\u003e\n\u003cp\u003eThis course is part of a series of three courses, where you wi"])</script><script>self.__next_f.push([1,"ll learn to use\u003cbr /\u003e\nstate-of-the-art modeling tools of the HBP Brain Simulation Platform to simulate neurons, build neural networks, and perform your own simulation experiments. \u003cbr /\u003e\nWe invite you to join us and share in our passion to reconstruct, simulate and understand the brain!\u003c/p\u003e4b9:T56d,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eProfessionals operating at the intersection of IT and business face unique challenges, amplified by data proliferation and ever-evolving technology. To add business value and remain competitive, it is crucial to understand how to successfully leverage emerging technologies within a broader enterprise context.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eDesigned for leaders responsible for making technology-related business decisions, this course provides an overview of business architecture and applications, considering internal business aspects such as value streams and business goals, as well as external factors including markets, customers, and competitors.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThrough real-world case studies and application exercises, uncover best practices for business application development, building a technology architecture and implementing new technologies. Discussions will focus on cutting-edge technologies such as big data, cloud computing, Quantum, Edge computing, IOT and 5G, AI and machine learning, and how they can be strategically implemented within specific enterprise environments. \u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis is the first of three courses that make up the edX Professional Certificate \u003cem\u003eAI and Cloud Computing: Implementation Strategies for Business.\u003c/em\u003e\u003c/p\u003e4ba:T9dd,"])</script><script>self.__next_f.push([1,"\u003cp\u003eGroundwater is the water beneath the ground surface. It is a vast freshwater reservoir often overlooked because invisible, yet 1000 times greater than all lakes and rivers. The Earth is blue for its oceans, but it is green for the blankets of freshwater under our feet. Half of the world’s population relies on groundwater for drinking and almost half of the irrigated land now depends on groundwater, a ten-fold increase in the past 50 years. \u003c/p\u003e\n\u003cp\u003eThis course explores the water cycle from an underground perspective. We start with the description of groundwater as a resource: How much is there? Where is it? How do we use it? How much groundwater do plants and trees use every year? How much water do aquifers lose during droughts? How much do they gain during rain events or in a typical year?\u003c/p\u003e\n\u003cp\u003eIn the second objective of this course, we describe underground waters and the properties and classification of aquifers. What is the difference between a confined and unconfined aquifer? What is porosity and does it influence groundwater resources? We then explain and apply Darcy’s law. Darcy is a 19th century hydraulics engineer who famously worked on bringing clean fresh water to the public fountains of Dijon, France. His law describes flow in porous media and is the cornerstone of subsurface hydrology. We will review his experiments and show how he arrived at his law empirically. We even will show in a lab video how to calculate the hydraulic properties of porous media following Darcy’s steps. After this empirical overview, we will demonstrate how we can derive the same law from first principles using Newton’s force balances. \u003c/p\u003e\n\u003cp\u003eIn our third objective, we will build up from Darcy’s law to derive the other principles of groundwater motion. What are the differences between confined and unconfined flows? What happens when it rains and the aquifers recharge? What happens in soils with inhomogeneous properties? What is a water divide? Finally, we will introduce modeling principles to translate these foundations into real-life engineering. How can we solve the equations of motion in excel using finite difference? How can we design tile drains to lower the water table in a cornfield?\u003c/p\u003e\n\u003cp\u003eThis first course of our three-course series introduces groundwater cycling on Earth, from the description of stocks to fluxes and to the basics of modeling. We will use these fundamental principles in our remaining two topical courses: Wells Hydraulics and Groundwater Contamination.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4bb:T8dd,"])</script><script>self.__next_f.push([1,"\u003cul\u003e\n\u003cli\u003eDescribe the global and local water balance and quantify groundwater recharge from hydrographs\u003c/li\u003e\n\u003cli\u003e\n\u003cul\u003e\n\u003cli\u003eExplain in your own words the terms: groundwater, freshwater, water allocation, drought, water demand\u003c/li\u003e\n\u003cli\u003eProvide estimates of human water usage, global water reservoirs, water during historic droughts\u003c/li\u003e\n\u003cli\u003eSketch the water cycle\u003c/li\u003e\n\u003cli\u003eProvide a water balance for a wathershed\u003c/li\u003e\n\u003cli\u003eApply the concept of water balance to simple situations\u003c/li\u003e\n\u003cli\u003eUse the displacement and seasonal recession method to calculate groundwater recharge. \u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eIdentify underground waters, describe the properties of aquifers and recall their classification\u003cul\u003e\n\u003cli\u003eDefine: zone of saturation, zone of aeration/unsaturated zone, water table, vadose zone, capillary fringe\u003c/li\u003e\n\u003cli\u003eDefine: aquifer, aquitard/aquifuge, pore space, phreatic surface, root zone, confined/unconfined aquifer, piezometric surface, artesian aquifer, perched aquifers \u003c/li\u003e\n\u003cli\u003eDefine porosity, derive its value in packed beds, Understand Grain size distribution and porosity, Understand the porosity of rock formations\u003c/li\u003e\n\u003cli\u003eDefine storativity, elastic storage coefficient, specific yield, storage coefficient, specific retention, specific storage. \u003c/li\u003e\n\u003cli\u003eDraw piezometric maps from piezometer data\u003c/li\u003e\n\u003cli\u003eFind recharge and discharge zones from piezometric data\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eExplain and apply Darcy's law \u003cul\u003e\n\u003cli\u003eProvide and explain Newton's second law, Newton's second law applied to fluids and Stokes Equation.\u003c/li\u003e\n\u003cli\u003eExplain the difference between Darcy flux and water velocity.\u003c/li\u003e\n\u003cli\u003eDerive Darcy's law from first principles: Navier-Stokes to Stokes to Poiseuille to Darcy\u003c/li\u003e\n\u003cli\u003eProvide physical explanations of the hydraulic conductivity\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003cli\u003eDerive the groundwater flow equations, apply their solutions to solve practical problems\u003cul\u003e\n\u003cli\u003eProvide and explain Dupuit's equation.\u003c/li\u003e\n\u003cli\u003eSolve problems related to unconfined and confined aquifers.\u003c/li\u003e\n\u003cli\u003eDerive the Boussinesq equation.\u003c/li\u003e\n\u003cli\u003eDefine transmissivity\u003c/li\u003e\n\u003cli\u003eWrite the continuity equation for a leaky aquifer and provide an explanation for each parameter and variable as well as their units\u003c/li\u003e\n\u003cli\u003eDefine transmissivity\u003c/li\u003e\n\u003c/ul\u003e\n\u003c/li\u003e\n\u003c/ul\u003e"])</script><script>self.__next_f.push([1,"4bc:T61e,\u003cp\u003e\u003cstrong\u003e__\u003c/strong\u003e _ \u003cstrong\u003eVisualizing Natural Language Processing\u003c/strong\u003e _ is the second course in the \u003ca href=\"https://courses.edx.org/dashboard/programs/b8d701a5-01e2-4e28-8313-cdf889550314/\"\u003e\u003cstrong\u003e\u003cem\u003eText Analytics with Python\u003c/em\u003e\u003c/strong\u003e professional certificate\u003c/a\u003e (or you can study it as a stand-alone course). Natural language processing (NLP) is only useful when its results are meaningful to humans. This second course continues by looking at how to make sense of our results using real-world visualizations.\u003c/p\u003e\n\u003cp\u003eHow can we understand the incredible amount of knowledge that has been stored as text data? This course is a practical and scientific introduction to text analytics. That means you’ll learn how it works and why it works at the same time.\u003c/p\u003e\n\u003cp\u003eOn the practical side, you’ll learn how to visualize and interpret the output of text analytics. You’ll learn how to create visualizations ranging from word clouds, heatmaps, and line plots to distribution plots, choropleth maps, and facet grids. You’ll work through real case-studies using jupyter notebooks and to visualize the results of machine learning in Python using packages like pandas, matplotlib, and seaborn.\u003c/p\u003e\n\u003cp\u003eOn the scientific side, you’ll learn what it means to understand language computationally. How do word embeddings and topic models relate to human cognition? Artificial intelligence and humans don’t view language in the same way. You’ll see how both deep learning and human beings interact with the meaning that is encoded in language.\u003c/p\u003e4bd:T8f7,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cstrong\u003eComing Soon February 2021.\u003c/strong\u003e Technological innovations have revolutionized the way we view and interact with the world around us. Editing a photo, re-mixing a song, automatically measuring and adjusting chemical concentrations in a tank: each of these tasks requires real-world data to be captured by a computer and then manipulated digitally to extract the salient information. Ever wonder how signals from the physical world are sampled, stored, and processed without losing the information required to make predictions and extract meaning from the data?\u003c/p\u003e\r\n\u003cp\u003eStudents will find out in this rigorous mathematical introduction to the engineering field of signal processing: the study of signals and systems that extract information from the world around us. This course will teach students to analyze discrete-time signals and systems in both the time and frequency domains. Students will learn convolution, discrete Fourier transforms, the z-transform, and digital filtering. Students will apply these concepts in interactive MATLAB programming exercises (all done in browser, no download required).\u003c/p\u003e\r\n\u003cp\u003eLearners should have strong problem solving skills, the ability to understand mathematical representations of physical systems, and advanced mathematical background (one-dimensional integration, matrices, vectors, basic linear algebra, imaginary numbers, and sum and series notation). This course is an excerpt from an advanced undergraduate class at Rice University taught to all electrical and computer engineering majors.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003e\u003ca href=\"https://support.edx.org/hc/en-us/articles/360060426873-MicroBachelors-Coaching-FAQs\"\u003eCoaching\u003c/a\u003e\u003c/b\u003e\u003cbr\u003e\r\nIf you are enrolled in the verified track (paid track) in any course that is a part of a MicroBachelors program, including this course, you are eligible for \u003ci\u003ecoaching at no additional cost. Please note that coaching is only available via SMS to U.S. phone lines.\u003c/i\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eOur coaches (real humans) are ready to help you with career exploration, navigating resources, staying motivated, and solving problems along the way to your goals.\u003c/p\u003e\r\n\r\n\u003cp\u003eLearn more about the \u003ca href=\"https://www.youtube.com/watch?v=14QVCTJvHQo\u0026feature=youtu.be\"\u003evalue of coaching\u003c/a\u003e directly from one of our coaches, Erin.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4be:T63f,\u003cp\u003e“Introduction to Computing in Python” is a series of courses built from Georgia Tech’s online for-credit version of CS1301: Introduction to Computing. The series is designed to take you from no computer science background whatsoever to proficiency in the basics of computing and programming, specifically in the popular programming language Python. Rated as one of the most in-demand and beginner-friendly programming languages, Python training will give you a solid foundation not only for Python code but for further studies in computer science.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe syllabus and course material has been used at Georgia Tech for its for-credit CS1301 class for over a year. Over 400 students on campus have completed this version of the course, and our analysis shows that they exit the course with the same learning outcomes as students taking the traditional on-campus version. This Professional Certificate uses the same instructional material and assessments as learning Python on campus, giving you a Georgia Tech-caliber introduction into the field of computing at your own pace.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis Professional Certification course follows a unique design. Students will cover the general, fundamental principles of computer science—which are applicable to any programming language like javascript or R — and then rapidly transition to those same programming concepts in Python. Short videos (2-3 minutes each) are rapidly interleaved with live programming problems, real-world examples, and multiple-choice questions to give you constant feedback on your progress and understanding.\u003c/p\u003e4bf:T6c0,\u003cp\u003eThe ability to work with data and access databases is one of the most sought-after skills in our data driven economy. SQL and NoSQL skills are essential for anyone working with data and are listed as one of the top skills in job postings for professionals like Data Engineers, Database Administrators, Data Scientists, Data Analysts, Business Analysts, BI Specialists, Software / Application Developers, Data Architects, and Big"])</script><script>self.__next_f.push([1," Data Engineers.\u003c/p\u003e\r\n \r\n\u003cp\u003eThis Professional Certificate is designed to provide you with the foundational knowledge, skills, and hands-on experience required to work with relational database management systems (RDBMSes), SQL (Structured Query Language), and NoSQL databases.\u003c/p\u003e\r\n\r\n\u003cp\u003eOrganizations use relational databases (RDBMS) to manage and store data for its consistency and reliability, and SQL (Structured Query Language) to query and perform analysis for making critical and informed business decisions. Many organizations also deploy NoSQL databases for non-traditional use cases when scalability and high availability are needed.\u003c/p\u003e\r\n\r\n\u003cp\u003eUpon completing this program, you’ll have developed the skills and experience to work with relational databases like MySQL, PostgreSQL, and IBM Db2; query databases using SQL, as well as perform Create, Read, Update and Delete (CRUD) operations using NoSQL technologies and databases like MongoDB, Cassandra, and Cloudant.\u003c/p\u003e\r\n\r\n\u003cp\u003eWithin each course, you’ll practice your skills with numerous hands-on labs and complete projects to add to your portfolio for demonstrating your proficiency using RDBMSes, SQL, and NoSQL.\u003c/p\u003e \r\n\r\n\u003cp\u003eTo get started, all you need is basic computer literacy and the desire to learn and practice new skills.\u003c/p\u003e4c0:T8f7,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIt is hard to overstate the importance of UX in our relationship with the digital environment. Forrester has established that organizations that invest in UX have fared better than most in bull markets and in recessions (Forrester, 2015). This is truer than ever, now that the pandemic has broadened the adoption of remote work, online social life and entertainment.\u003c/p\u003e\r\n \r\n\u003cp\u003eLearn User Experience fundamentals from the professors of the most important UX lab in North America. In this UX MicroMasters program, you will learn to meet organizational goals and satisfy users by applying a user-centred process to the digital products and services development that solve industry-relevant, real-world problems.\u003c/p\u003e\r\n\r\n\u003cp\u003eFor each user-centred development phase (UX Research, Design, Prototyping and Evaluation), you will acquire the relevant theoretical knowledge and the applied best practices to plan, perform, analyze and communicate useful insights for the following development phase.\u003c/p\u003e\r\n \r\n\u003cp\u003eFrom personal financial services and employee-oriented software to disruptive medical applications, this MicroMasters program will help you develop the design thinking, UX evaluation and UX project management skills necessary to generate new solutions to pressing problems.\u003c/p\u003e\r\n\r\n\u003cp\u003eIn each course, you will face a series of week-long challenges based on state-of-the-art research currently underway at HEC Montréal’s Tech3lab Laboratory. Through these challenges, you will refine your understanding of core concepts and prove your abilities in the very sought-after field of UX , from user interface, heuristic evaluation, human-computer interaction, usability testing, user interaction, to user experience.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis rigorous graduate-level series represents 20% of the coursework towards a Master of Science degree in UX in a Business Context from HEC Montréal. You will have unique access to the Tech3lab, an applied laboratory in management science, specializing in the analysis of interactions between technological interfaces in organizations and their employees or customers.\u003c/p\u003e\r\n\r\n\u003chtml\u003e\r\n\u003cbody\u003e\r\n\r\n\u003cimg src=\"https://images.ctfassets.net/ii9ehdcj88bc/3kcIO09KMO9V3pLcmSgu5Z/89b3cdc9508a500d358329034d57268d/edxprize2022.png?h=250\" alt=\"edX Prize 2022\" \r\n\r\n\u003c/body\u003e\r\n\u003c/html\u003e"])</script><script>self.__next_f.push([1,"4c1:T89a,"])</script><script>self.__next_f.push([1,"\u003cp\u003eStart by recognizing AI’s multiple benefits and implications and end by making a plan for its application.\u003c/p\u003e\r\n\r\n\u003cp\u003eArtificial Intelligence (AI) is like electricity must have been 140 years ago. We may not yet be able to properly imagine how it will affect our lives, but organizations need to start thinking about how AI can be applied to and improve everyday practice.\u003c/p\u003e\r\n\r\n\u003cp\u003eWe can’t just leave this to the coding elite either. This program is therefore not about difficult algorithms and complex programming. Rather, it is for anyone, regardless of their professional background or job role, who is interested in learning how to prepare for and apply AI in their own situation, for instance:\r\n\u003cul\u003e\r\n\u003cli\u003eExperienced managers who want to know what AI can do for their own organization.\u003c/li\u003e\r\n\u003cli\u003eData analysts or business consultants who want to understand how AI can be applied in the business processes of the company for which they work.\u003c/li\u003e\r\n\u003cli\u003eStudents who want to understand how the results of AI research can be translated into practical applications.\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eAI innovative solutions, such as the use of machine learning, deep learning, computational argumentation, diagnostic image analysis, reinforcement learning, natural language processing, robotics and data analytics, can help you and your company solve specific problems, drive efficiency and improve performance and decision-making.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis two-course program provides a wide range of cases and examples of current AI applications in various organizations. It presents ideas and case studies for actual situations based on state-of-the-art AI research, and provides practical tools for integrating AI into your own organization.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program has been developed by Delft University of Technology and the Innovation Center for Artificial Intelligence Academy (ICAI) in the Netherlands. ICAI is a national initiative involving industry, universities and government in the area of AI research and applications. Case studies presented include contributions from top Dutch universities, public institutions and AI experts from companies such as ING, Ahold Delhaize, Elsevier, KPN and Thirona.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4c2:T405,Apply common operations (pre-processing, plotting, etc.) to datasets using Python.,Explain the concept of supervised, semi-supervised, unsupervised machine learning and reinforcement learning.,Explain how various supervised learning models work and recognize their limitations.,Analyze which factors impact the performance of learning algorithms.,Apply learning algorithms to datasets using Python and Scikit-learn and evaluate their performance.,Optimize a machine learning pipeline using Python and Scikit-learn.,Describe the main classes of clustering techniques.,Implement k-means and hierarchical clustering.,Motivate the need and choice of dimensionality reduction techniques.,Implement Principal Component Analysis (PCA) for feature extraction.,Explain how deep neural networks work and their advantages.,Train deep neural networks for classification and regression tasks.,Explain the basic concepts and techniques of reinforcement learning.,Describe how reinforcement learning could be applied in real world applications.4c3:T58c,\u003cp\u003eThis series of hands-on and interactive MOOCs will give learners a comprehensive overview of the basics machine learning topics. You will discover how machine learning classification and regression techniques allow you to make predictions for a category (classification) or for a number (regression) given data. This can be useful in predicting properties of objects (such as their weight or shape), or predicting qualities of people (customer satisfaction, etc.).\u003c/p\u003e\r\n\r\n\u003cp\u003eYou will learn about unsupervised learning techniques such as clustering and dimensionality reduction and how useful they are to make sense of large and/or high dimensional datasets.\u003c/p\u003e\r\n\r\n\u003cp\u003eWe will also cover more advanced supervised learning techniques such as deep learning. This is useful to train neural networks to solve more complicated classification and regression tasks. Finally, you will deep dive into the reinforcement learning techniques and understand how to use them to train AI agents that interact with "])</script><script>self.__next_f.push([1,"an environment.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe lectures feature a unique combination of videos mixed with hands-on interaction with machine learning algorithms to stimulate a deeper understanding. In the exercises you apply the algorithms in Python using scikit-learn and in the final project you will further deepen your understanding of the various concepts by building and tuning a machine learning pipeline from start to finish.\u003c/p\u003e4c4:T622,\u003cp\u003e\r\nData and technology are driving business change. Leading companies are investing in the tech, data, processes, and people to empower better decision-making and faster corrections based on what they learn. Predictive analytics, where data is used to forecast future trends and events, can help drive strategic decision-making. This type of analysis goes beyond explanations and predictions to recommend the best course of action moving forward, advancing business growth, and maintaining a competitive edge.\u003c/p\u003e \r\n\r\n\u003cp\u003eThe three month Predictive Analytics in Business professional certificate from IE University explores data-driven forecasting techniques from a business and technical perspective. Drawing on the Cross-Industry Standard Process for Data Mining (CRISP-DM), you’ll explore an iterative approach to predictive analytics and learn how to leverage this knowledge to achieve business goals. You’ll analyze real-world case studies as you develop an understanding of how data-driven models can improve your ability to make decisions in a fast-paced world. You’ll also engage with the technical aspects of predictive modeling demonstrated with activities pre-populated with Python code. Other outcomes include an exploration of regression and classification analysis for business strategy and decision-making, and the forecasting methods needed to estimate future business results. By the end of the program, you’ll learn to aid decision-making and risk management strategies in your organization using your newfound predictive analysis toolkit.\u003c/p\u003e4c5:Tb1f,"])</script><script>self.__next_f.push([1,"\u003cp\u003eEl sector de la construcción representa una de las mayores industrias en términos económicos a nivel global y se posiciona como uno de los motores de crecimiento en múltiples países de la región latinoamericana. Sin embargo, es también uno de los sectores de menor crecimiento de productividad anual debido a su limitada digitalización en comparación con otras industrias. Esto abre un espacio de oportunidades y retos para la transformación digital y su acceso a la cuarta revolución industrial a través de nuevas formas de abordar los proyectos.\u003c/p\u003e\r\n \r\n\u003cp\u003eLa transformación de la industria de la construcción ha explorado el uso de plataformas digitales y software especializado como parte de su camino hacia la transformación digital. Sin embargo, tendrá que trascender rápidamente hacia la digitalización, virtualización y automatización de procesos, el uso e implementación de tecnologías de la industria 4.0 (gemelos digitales, realidad virtual, realidad aumentada, internet de las cosas iot, robótica, inteligencia artificial, cloud computing, impresión 3D ó fabricación aditiva, análisis de datos y big data, ciberseguridad, entre otros) y la aplicación de metodologías disruptivas (Building Information Modeling BIM, Kanban, Design Thinking, SCRUM, LEAN Construction, entre otras) que agilicen el aprovechamiento de los datos en tiempo real, influyan eficazmente en la toma de decisiones en el ciclo de vida de los proyectos y las ciudades inteligentes, generen nuevos modelos de negocio, ayuden a tecnificar la mano de obra y solucionen efectivamente los problemas de interoperabilidad e intercambio de información. Como industria nos enfrentamos a un nuevo paradigma: la construcción 4.0.\u003c/p\u003e\r\n \r\n\u003cp\u003eEn este programa, aprende los conceptos básicos del impacto y la transformación digital que la cuarta revolución industrial trae para la Industria AECO (Arquitectura, Ingeniería, Construcción y Operación) a lo largo de todo el ciclo de los procesos de producción de un proyecto amable con el medio ambiente, desde su planificación hasta el control del mismo, involucrando la perspectiva de los distintos actores interconectados y con un punto de vista crítico y prospectivo.\u003c/p\u003e\r\n \r\n\u003cp\u003eEl programa de Certificación Profesional Fundamentos de la construcción 4.0 busca introducir y reconocer los conceptos básicos, metodologías y nuevas tecnologías digitales asociadas y utilizadas en el desarrollo tecnológico de la industria de la construcción en la actualidad y las tendencias emergentes que transformarán en el corto, mediano y largo plazo toda la cadena de valor. Exploraremos los conceptos de tecnologías de la información y la industria 4.0 aplicadas al sector construcción, sistemas ciberfísicos, ecosistemas digitales, metodologías disruptivas y ágiles, entre otros.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4c6:Ta40,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIn this program, you will learn about more advanced attacks in the space of side-channel security: transient-execution attacks and fault attacks. In the first course. we will focus on transient execution (and speculative execution) and how it can introduce data (not meta-data!) leakage. We will use side channels to exfiltrate data and transmit it to an attacker-controlled application. We will learn about the most prominent of transient-execution attacks: Meltdown, Spectre, Foreshadow, and ZombieLoad. These attacks are so powerful that they can leak arbitrary secret data, including cryptographic keys, all without physical access. In a set of small exercises, you will implement some of these attacks. You will understand the connection between these attacks and side-channel attacks. You will gain deep understanding of the microarchitecture of modern processors, out-of-order execution pipelines, transient-execution attacks and potential mitigations against them.\u003c/p\u003e\r\n\r\n\u003cP\u003eIn the second course, we will then focus more on fault attacks, in particular Rowhammer and Plundervolt. These attacks go beyond leaking information but instead we will manipulate data. These fault injection mechanisms are triggered purely from software and allows us to manipulate control flow, secret keys, and system security mechanisms, to fully subvert systems and bring them under our control. You will understand how these attacks can be mounted, and how they can be mitigated to allow you to develop hardware and software resilient to transient-execution and fault attacks. As an advanced topic in this block, we will also mount software-based differential power analysis attacks (DPA), following a similar methodology as for the physical side-channel attacks, leaking cryptographic keys. Again we will disucss what the countermeasures against these attacks are.\u003c/p\u003e\r\n\r\n\u003cp\u003eIn both courses, you will practically apply the acquired skills in simple exercises based on measurements you perform on your own computer or measurements we obtained from physical devices, that we provide to you. Both courses require programming skills (C, C++, Python). We will provide you with the knowledge required beyond these, including basics on operating systems, computer architecture, and hardware design.\u003c/p\u003e\r\n\r\n\u003cp\u003eDaniel Gruss is an internationally renowned expert in side-channel research and has written many seminal works in this field and presented them at renowned international conferences, especially on transient-execution attacks that affected the entire industry and defenses that have been implemented in all operating systems.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4c7:Tb39,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis program delves into the theory and art of map making, a practice that has evolved over thousands of years and has been revolutionized by modern computer technology. It is designed to immerse students in the use of advanced computer techniques to address technical design issues, enabling them to create accurate and meaningful automated geographic mapping products. Through a series of comprehensive hands-on exercises, students will utilize Geographic Information Systems (GIS) software to produce typical GIS mapping products, thereby gaining valuable practical experience in the application of geotechnologies.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe courses within this program provide a robust combination of theoretical foundations and practical applications aimed at solving complex social and ecological problems through the use of geotechnologies. These geotechnologies encompass GIS, remote sensing (which involves examining the world through imagery collected by drones, aircraft, and satellites), and positioning systems such as GPS and Global Navigation Satellite Systems. Students will engage with a broad range of social and environmental themes through an array of readings, videos, and interactive exercises. This multifaceted approach ensures that students learn the critical fundamentals of mapping tools, including projections, symbology, classification, and analysis.\u003c/p\u003e\r\n\r\n\u003cp\u003eThroughout the duration of the program, students will not only build web mapping applications, such as interactive dashboards and multimedia story maps, but they will also have the opportunity to collect and map their own field data. This practical experience is crucial in helping students develop the skills and confidence needed to use maps as powerful analytical tools. By engaging in these activities, students will learn how to effectively communicate complex data and spatial information through visually compelling and informative maps.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe program emphasizes the importance of using geotechnologies to address real-world problems and promotes the development of solutions that contribute to a more sustainable and resilient future. By the end of the program, students will have a deep understanding of how to leverage these technologies to analyze and interpret spatial data, making them well-equipped to tackle a variety of challenges in both social and ecological contexts.\u003c/p\u003e\r\n\r\n\u003cp\u003eOverall, this program prepares students to be leaders in the field of geotechnologies, providing them with the knowledge and practical skills necessary to make significant contributions to society. Whether they are interested in environmental conservation, urban planning, disaster response, or any other field that relies on spatial data, students will find that this program equips them with the tools to create positive change through the power of mapping and geographic analysis.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4c8:T735,\u003cp\u003eBusiness analytics is the ability to collate and combine multiple streams of data to better understand business processes, customer demands, and relationships between multiple agents.\u003c/p\u003e\r\n\r\n\u003cp\u003eWe live and work in an uncertain world. Every day, business managers, economists, line managers, supervisors and front-line workers must make decisions and predictions based on limited information. This program will help you to make better informed decisions for the future to answer questions like:\r\n\u003cul\u003e\r\n\u003cli\u003eWhat is the probability that a staff member will need more than 20 days of sick leave in a year?\u003c/li\u003e\r\n\u003cli\u003eDoes the new system for preparing food for customers offer a significant improvement over the old system, or is it the same?\u003c/li\u003e\r\n\u003cli\u003eHow are sales likely to fluctuate over the next 12 months, based on trends in historical data?\u003c/li\u003e\r\n\u003c/ul\u003e\r\n\u003c/p\u003e\r\n\u003cp\u003eTo inform strategic decisions and remain competitive, businesses must leverage the insights contained in the large volumes of data produced both within the business and in the broader business environment. Providing you with skills that are highly sought after in the global workplace, this program will equip you with the analytical know-how needed to extract meaning from complex data sets and translate this meaning into actionable insights. \u003c/p\u003e\r\n\r\n\u003cp\u003eThis program covers a variety of techniques applicable to the collection, presentation, interpretation, and use of numerical data. It provides a foundation for understanding statistical procedures that will help you undertake solid statistical analysis in business and economic situations. This includes statistical inference, probability \u0026 sampling distributions, estimation, hypothesis tests, correlation \u0026 regression, experimental design, sample survey design, quality sampling, and modern business decision theory.\u003c/p\u003e4c9:T494,\u003cp\u003eLearn the core techniques of text analytics and natural language processing (NLP) while discovering the cognitive science that makes it possible in this certificate Text Analytics wi"])</script><script>self.__next_f.push([1,"th Python. On the practical side, you’ll learn how to actually do an analysis in Python: creating pipelines for text classification and text similarity using machine learning. These pipelines are automated workflows that go all the way from data collection to visualization. On the scientific side, you’ll learn what it means to understand language computationally. Artificial intelligence and humans don’t view text documents in the same way. Sometimes deep learning sees patterns that are invisible to us. But often deep learning misses the obvious. We have to understand the limits of a computational approach to language together with the ethical requirements that guide how we choose what data to use and how we protect the privacy of individuals.\u003c/p\u003e\r\n\r\n\u003cp\u003eAlong the way, you’ll explore real-world case studies using pandas, numpy, scikit-learn, tensorflow, matplotlib, seaborn, gensim, and spacy within jupyter notebooks to gain useful insights from unstructured data.\u003c/p\u003e4ca:T6e5,\u003cp\u003eGenerative AI modeling is an in-demand skill for AI model development. Employers now expect generative AI skills to be on an AI engineer’s resume. This hands-on course, which is also part of the IBM AI Applied Professional Certificate, will help you build the generative AI skills you need to stand out as an AI developer.\u003c/p\u003e\n\u003cp\u003eThroughout the course, you’ll get valuable practical experience working on guided projects that provide step-by-step instructions for building generative AI-powered applications. As part of this, you’ll work with Python and related libraries like Flask and Gradio, plus you’ll use frameworks such as Langchain. The course includes learning elements such as videos and readings to help you understand the models, frameworks, and technologies used in the projects.\u003c/p\u003e\n\u003cp\u003eYou’ll also dive into building intelligent chatbots and apps using popular large language models (LLMs) such as GPT3 and Llama 2 hosted on platforms like IBM watsonx and Hugging Face. You'll explore retrieval-augmented generation (RAG) tec"])</script><script>self.__next_f.push([1,"hnology to enhance LLMs by incorporating external information beyond their training data. You’ll be able to build voice-enabled chatbots and apps using IBM Watson ® Speech Libraries for Embed. \u003c/p\u003e\n\u003cp\u003eTo get the most out of this course, it is essential that you have a basic understanding of the Python programming language. It is also of benefit if you are familiar with HTML, CSS, and JavaScript, though this is not a requirement. This course is ideal for tech professionals who have some experience with Python and are ready to build the highly sought-after generative AI skills required to be an AI engineer or AI developer. If that’s you… enroll today and build job-ready gen AI skills in 6 weeks.\u003c/p\u003e4cb:T4f9,\u003cp\u003eIn this macroeconomics course, you will improve your skills in macroeconomic policy analysis and learn to design an economic and financial program, using real economic data. The financial programming exercise simulates what IMF (International Monetary Fund) desk economists routinely do in their country surveillance and program work.\u003c/p\u003e\n\u003cp\u003eIn the first part of the course (modules 1–7), you will analyze the economic outlook of a country case and then help to build a baseline scenario — namely, a set of projections for the main macroeconomic sectors (real, external, government and monetary) that reflects the analyst’s best guess of what will happen to the economy in the coming year, assuming no policy change.\u003c/p\u003e\n\u003cp\u003eIn the second part (modules 8–10), you will learn and discuss how macroeconomic policies can be used to address poor performance and reduce macroeconomic imbalances. We will illustrate the workings of monetary, fiscal and exchange rate policies by using a simple Keynesian model of an open economy. In the final module you will design an IMF–supported lending program scenario for our country case.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFinancial Programming and Policies, Part 2 is offered by the IMF with financial support from the Government of Japan.\u003c/em\u003e\u003c/p\u003e4cc:T9a7,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThe Mediterranean region is one of the most biodiverse in the world, home to a complex and intricate patchwork of cultures, climates, and cuisines. 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It summarizes global-to-local challenges related to achievement of the Sustainable Development Goals (SDG); outlines the history and culture of agriculture and its main characteristics with a focus on the \"Mediterranean diet\"; explains agricultural data with a focus on rural development models and value creation; explores EU policy frameworks and international agreements related to food and agriculture in the Mediterranean; and highlights emerging opportunities linked to innovation and sustainability in the sector.\u003c/p\u003e\n\u003cp\u003eThis course is for:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eStudents at the undergraduate or graduate level interested in the main challenges facing the Mediterranean region\u003c/li\u003e\n\u003cli\u003eCurrent and future practitioners in the agriculture, food, and beverage sectors who wish to gain useful insights about current and future trends and business opportunities\u003c/li\u003e\n\u003cli\u003ePolicymakers and regional stakeholders who want to deepen their knowledge of agricultural policy, investment, and decisionmaking in the region and globally\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWant to learn more about sustainable food and nutrition around the world? 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effectively:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eJobs to be Done (JTBD) framework: This approach helps identify the core needs and motivations of customers, focusing on what they're trying to accomplish rather than just their demographic characteristics.\u003c/li\u003e\n\u003cli\u003eCustomer segmentation: Techniques to categorize customers based on their needs, behaviors, and characteristics.\u003c/li\u003e\n\u003cli\u003eCustomer experience mapping: Creating detailed journey maps to understand how customers interact with products across a portfolio.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePRODUCT MARKET FIT\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA significant portion of the course is dedicated to achieving and measuring product-market fit:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eValue Proposition Canvas: A tool to align product offerings with customer needs and desires.\u003c/li\u003e\n\u003cli\u003eMinimum 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revenue:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFreemium models: Offering basic services for free while charging for premium features.\u003c/li\u003e\n\u003cli\u003eSubscription-based models: Shifting from one-time purchases to recurring revenue streams.\u003c/li\u003e\n\u003cli\u003eDirect-to-consumer sales: Eliminating intermediaries in the sales process.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePORTFOLIO MANAGEMENT\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe course provides insights into managing a collection of products effectively:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eBalanced portfolio framework: Balancing quick wins, core improvements, and big bets.\u003c/li\u003e\n\u003cli\u003eFeature gap analysis: Identifying unmet customer needs across product lines.\u003c/li\u003e\n\u003cli\u003eCross-product synergies: Leveraging strengths across different products in a portfolio.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMETRICS AND KPIS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThroughout the course, there's a strong emphasis on measuring success and driving improvements:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eObjectives and Key Results (OKRs): A framework for setting and tracking goals.\u003c/li\u003e\n\u003cli\u003eKey Performance Indicators (KPIs): Metrics to measure product and portfolio performance.\u003c/li\u003e\n\u003cli\u003eCustomer satisfaction metrics: Including CSAT, NPS, and customer effort score.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eINNOVATION CULTURE AND PROCESSES\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe course discusses how to foster a culture of innovation within organizations:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCollaborative culture: Encouraging cross-functional teamwork and idea sharing.\u003c/li\u003e\n\u003cli\u003eEmpowerment: Giving teams the freedom and resources to innovate.\u003c/li\u003e\n\u003cli\u003eRisk tolerance: Balancing the need for innovation with responsible risk management.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBUSINESS MODEL CANVAS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStudents learn to use the Business Model Canvas as a tool for visualizing and developing business models for new and existing products:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eNine key components: Including value propositions, customer segments, revenue streams, and cost structure.\u003c/li\u003e\n\u003cli\u003eFeasibility, desirability, and viability: The three key areas to evaluate business model strength.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eREVENUE MODELS\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe course explores various revenue models that 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It provides students with the tools to transform business models and drive growth through effective product management strategies.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4d0:T6cf,\u003cp\u003eThis course consists of two types of materials. The first type of materials concerns with how to \u003cstrong\u003emeasure\u003c/strong\u003e the performance of the economy as a whole. The second type of materials concerns how to explain \u003cstrong\u003ewhy\u003c/strong\u003e a country underperforms in one or more of these aspects, and \u003cstrong\u003ewhat\u003c/strong\u003e can be done about it. The behaviour of an economy as a whole is often broadly predictable, but sometimes it can suddenly change. You will learn how to understand macroeconomic data and know how it can be interpreted for analysis.\u003c/p\u003e\n\u003cp\u003eThis MOOC is part of the \u003cem\u003eProfessional Certificate in Macroeconomics\u003c/em\u003e. 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In the course you will be working towards creating a project plan for your research, giving you a head-start in your research project.\u003c/p\u003e\n\u003cp\u003eThe interuniversity, interdisciplinary Leiden-Delft-Erasmus Center for Education and Learning is a leader in multidisciplinary technological research and innovation projects. Learning from leading experts in the field you will learn to apply the best practices in your own context.\u003c/p\u003e4d6:T4e2,\u003cp\u003eOrganizations understand that data creates enormous value when transformed into Business Intelligence (BI). Data Warehousing Engineers, Data Analysts, and BI professionals play a critical role in enabling organizations to derive value out of data and therefore are in high demand.\u003c/p\u003e\r\n \r\n\u003cp\u003eThis Professional Certificate is designed to provide you the skill set and hands-on experience for working with data warehouses and BI tools.\u003c/p\u003e \r\n \r\n\u003cp\u003eYou’ll gain proficiency with Linux Commands and Bash shell scripting, create data pipelines to perform ETL (extract, transform, load), design and populate data warehouses and analyze data using SQL queries and business intelli"])</script><script>self.__next_f.push([1,"gence tools.\u003c/p\u003e \r\n \r\n\u003cp\u003eEach course within this Professional Certificate provides hands-on experience with practice labs and real-world projects to add to your portfolio. Skills you will gain include SQL, Python, ETL Pipelines, Bash Shell Scripting, Linux/UNIX shell commands and scripting, Cron, Crontab, Apache Airflow, Apache Kafka, IBM DB2, MySQL, PostgreSQL, and Cognos Analytics.\u003c/p\u003e\r\n \r\n\u003cp\u003eTo get started, all you need is basic computer and data literacy, familiarity with either Linux, Unix, Windows, or MacOS, and the desire to learn and practice new skills.\u003c/p\u003e4d7:Td8f,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-GB\"\u003eContext\u003c/span\u003e \u003c/strong\u003e\u003cspan lang=\"EN-GB\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eWhile health is one of several factors influencing diets, it is a powerful one, especially in certain life situations such as illness and the birth of a child. \u003cspan lang=\"EN-GB\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eDoctors enjoy special authority when speaking on matters of health and could play a key role in informing and persuading people to change their eating habits – during consultations but also through other channels. The global obesity epidemic, the rise of personalized medicine and increased understanding of the microbiome are also likely to converge into a situation where doctors can prescribe food as medicine, giving them influence over the decision, implementation, and confirmation stages as well. \u003cspan lang=\"EN-GB\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eCurrently, a key obstacle to medical professionals playing a stronger role in food systems transformation is their relative lack of training in nutrition, let alone sustainability. On the other hand, linking personal health to the health of the planet will for some people be an increased motivator to rethink diets. If medical doctors had better knowledge of the link between food, health, nutrition, and sustainability, they could play an active role in transforming our food system. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-GB\"\u003eSolution\u003c/span\u003e \u003c/strong\u003e\u003cspan lang=\"EN-GB\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eTo overcome this gap in knowledge and to reinforce behaviour change towards healthier and sustainable diets, EIT Food has developed a \u003cstrong\u003eshort online course\u003c/strong\u003e on nutrition targeting \u003cstrong\u003emedical students\u003c/strong\u003e. In addition, professionals in the medical sector may find this course useful for providing an up-to-date analysis of topical nutrition debates.\u003cspan lang=\"EN-GB\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe course title is “ \u003cstrong\u003eNutrition for Health and Sustainability\u003c/strong\u003e ”. It is composed of 12 hours of learning broken down in 3 modules.\u003cspan lang=\"EN-GB\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eOn this course, students will look at what makes up a healthy diet and see what types of foods play a crucial part in preventing diseases. Learners will have the opportunity to build an understanding of the relationship between food and disease and get accustomed to nutrition counselling techniques. They’ll also reflect on the possible biological, social, and psychological causes of unhealthy eating patterns, and interpret the importance of evidence-based nutrition both for human and planet health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-GB\"\u003eAmbition\u003c/span\u003e \u003c/strong\u003e\u003cspan lang=\"EN-GB\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eOur mission is to empower the future and current generation of medical doctor. We’ll equip them with fundamentals nutrition knowledge and nutrition counselling skills so that they could become the agents of change our society need.\u003cspan lang=\"EN-GB\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe goal to establish nutrition as a key part of healthcare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-GB\"\u003eHigh level contributors\u003c/span\u003e \u003c/strong\u003e\u003cspan lang=\"EN-GB\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis course has been designed with and for medical students (undergrad or higher). It has been produced by educators from the University of Reading, the University of Torino, the University of Hohenheim, the University of Groningen, IMDEA Food Institute and the Spanish National Research Council (CSIC), in collaboration with experts from the Harvard Medical School and the International Federation of Medical Students Associations (IFMSA).\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4d8:T73e,\u003cp\u003eProcessing information is the hallmark of all modern organizations, which are increasingly digital: absorbing, processing and generating information is a key element of their business.\u003cbr /\u003e\nBeing able to interact flexibly and efficiently with the underlying data and software systems is an indispensable skill. Knowledge of the Unix shell and its command-line tools boosts the effectiveness and productivity of software developers, IT professionals, and data analysts.\u003c/p\u003e\n\u003cp\u003eThe Unix tools were designed, written, actively used and refined by the team that defined the modern computing landscape. They allow the performance of almost any imaginable computing task quickly and efficiently by judiciously combining key powerful concepts. The power of Unix tools for exploring, prototyping and implementing big data processing workflows, and software engineering tasks remains unmatched. Unix tools, running on hardware ranging from tiny IoT platforms to supercomputers, uniquely allow an interactive, explorative programming style, which is ideal for the efficient solution of many of the engineering and business analytics problems that we face every day.\u003c/p\u003e\n\u003cp\u003eThrough the use of Unix tools:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSoftware developers can quickly explore and modify code, data, and tests.\u003c/li\u003e\n\u003cli\u003eIT professionals can scrutinize log files, network traces, performance figures, filesystems and the behavior of processes.\u003c/li\u003e\n\u003cli\u003eData analysts can extract, transform, filter, process, load, and summarize huge data sets.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe course is uniquely based on carefully-selected, interactive walk-through examples that demonstrate how each command operates in practice. The examples that we use involve problems that engineers and analysts face every day.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e_ ___ Class Central: Best Online Courses of the Year (2021 edition)\u003c/strong\u003e\u003c/p\u003e4d9:Td7c,"])</script><script>self.__next_f.push([1,"\u003cp\u003e¿Conoces realmente a tus clientes? ¿Conoces realmente qué productos de la canasta de mercado compran tus clientes o te dejas llevar por lo que aparenta a simple vista?\u003c/p\u003e \r\n\r\n\u003cp\u003eEn este curso aprenderás a construir modelos basados en técnicas de data mining o minería de datos, que te permitirán conocer información relevante de tus clientes y descubrir patrones de comportamiento para definir estrategias de marketing de acuerdo a la compra de productos.\u003c/p\u003e\r\n\r\n\u003cp\u003eEs fundamental conocer a nuestros clientes para alcanzar los objetivos de la organización. Este conocimiento debe ser profundo, no solo de sus cualidades y características, sino, también de sus patrones de comportamiento como consumidor, esta es información necesaria para alcanzar la fidelización del cliente. Un claro ejemplo es cómo funciona son los anuncios en redes sociales.\u003c/P\u003e\r\n\r\n\u003cp\u003eLa segmentación de mercados y la cesta compras, forman un cuerpo de conocimientos muy útiles en mercadotecnia, ya que su análisis contribuirá a definir estrategias y tácticas que permitan acertar en las necesidades y deseos de los clientes.\u003c/p\u003e\r\n\r\n\u003cp\u003eTienen aplicaciones tales como: Soporte para la toma de decisiones, análisis de información de ventas, distribución de mercancías en los anaqueles de las tiendas y segmentación de clientes con base en patrones de comportamiento. Y permite la creación de árboles de decisión que al final impacte en una estrategia predictiva.\u003c/p\u003e\r\n \r\n\u003cp\u003eLa segmentación de mercados realiza un agrupamiento de los clientes de acuerdo a su comportamiento como consumidor. Los segmentos de comportamiento son grupos de clientes que se comportan de manera similar en relación con el negocio. El algoritmo K-Means, te permite segmentar el mercado y crear conjuntos de datos, mediante el agrupamiento de clientes para interpretar información relevante de consumo.\u003c/p\u003e \r\n \r\n\u003cp\u003eLa técnica de análisis de la canasta de mercado, permite identificar diferentes association rules o reglas de asociación entre los datos disponibles sobre productos comprados.\u003c/p\u003e\r\n \r\n\u003cp\u003eAl contar con bases de datos transaccionales con técnicas de preprocesamiento, la segmentación y análisis de datos puede realizarse mediante técnicas de minería de datos que te permitan el descubrimiento del conocimiento del cliente. Y este proceso de descubrimiento es de gran beneficio para detectar más allá de un excel con gran cantidad de datos.\u003c/p\u003e\r\n\r\n\u003cp\u003eEn este curso aprenderás los fundamentos teóricos del Big Data y del análisis de datos, así como de la técnica de minería de datos relacionada con la segmentación de mercados y el análisis de la cesta de la compra.\u003c/p\u003e\r\n\r\n\u003cp\u003eAdemás, mediante inteligencia artificial con el software especializado RapidMiner, aplicarás los conceptos en la creación de un modelo de minería de datos con aprendizaje automático o machine learning, que te permitirá realizar un completo análisis de los segmentos de clientes y de la canasta de Mercado; y aplicar los resultados obtenidos en tu estrategia. Utiliza todas las herramientas de minería de datos a tu favor y aplícalo en grandes bases de datos.\u003c/p\u003e\r\n\r\n\u003cp\u003eEste programa de Certificación Profesional tiene una particularidad, al tratarse de cursos que pueden realizarse de forma independiente, se repite algún contenido en los cursos, principalmente en lo relacionado a los fundamentos teóricos y al uso del software, dada su importancia.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4da:Td50,"])</script><script>self.__next_f.push([1,"\u003cp\u003eAnimal breeding, and especially breeding of farm animals and aquaculture species, has developed into a professional industry with modern technologies, large-scale data collection, and analyses. This has resulted in very efficient and effective breeding programs. However, in many developing countries, and for many lesser known livestock and fish species there is a need for tailor-made breeding programs. In this MOOC, you will learn about the implementation and evaluation of both large industrial scale and the tailor-made breeding programs, in terms of genetic progress and genetic diversity.\u003c/p\u003e\n\u003cp\u003eTogether with other learners, you will dive into the reasons behind crossbreeding in relation to dissemination of genetic improvement. It is essential to know how key biological factors affect the structure of a breeding program and to understand the different structures a breeding program can have.\u003c/p\u003e\n\u003cp\u003eJoin this course and learn everything about how to properly evaluate breeding programs. In several knowledge clips and assignments, you will learn how to assess the effects of legislation, competition, GxE, new technology on breeding programs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEliminating diseases\u003c/strong\u003e\u003cbr /\u003e\nTwo special sections are devoted to genetic diversity and, more specifically, to (i) monogenic recessive disorders, and (ii) genetic diversity at the population level. Monogenic recessive disorders play an important role in breeding programs for companion animals such as dogs and horses. Modern DNA sequencing technology now makes it possible not only to detect these mutations but also to design breeding strategies aimed at eliminating these diseases. Genetic diversity at population level is important to maintain flexibility in populations and to keep animal populations healthy. In this course, you will learn more about how to monitor and conserve genetic diversity in breeding programmes.\u003c/p\u003e\n\u003cp\u003eAfter finishing this course you can make informed decisions when setting up a breeding program for a specific animal in a specific production system and recognize and identify key elements of the course in real-life examples of breeding programs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrerequisites\u003c/strong\u003e\u003cbr /\u003e\nPlease know that knowledge of statistics at a 2nd or 3rd year university level is needed to follow this course successfully. This course partially builds on knowledge gained in the MOOC Genetic Models for Animal Breeding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFor Whom\u003c/strong\u003e\u003cbr /\u003e\nAlthough this course is open to everyone, it is particular useful for breeders of:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCows\u003c/li\u003e\n\u003cli\u003ePoultry/Chicken\u003c/li\u003e\n\u003cli\u003eHorses\u003c/li\u003e\n\u003cli\u003ePigs/Swine\u003c/li\u003e\n\u003cli\u003eDogs\u003c/li\u003e\n\u003cli\u003eSheep\u003c/li\u003e\n\u003cli\u003eGoat\u003c/li\u003e\n\u003cli\u003eFish\u003c/li\u003e\n\u003cli\u003eShrimp\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eProfessional Certificate Program\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis course is part of the\u003ca href=\"https://www.edx.org/professional-certificate/wageningenx-animal-breeding-and-genetics?index=product\u0026queryID=0aba9f9feb927113b49a8e266d916a03\u0026position=1\"\u003e Professional Certificate Programme \"Animal Breeding and Genetics\"\u003c/a\u003e. Join the other course in the programme, \u003ca href=\"https://www.edx.org/course/genetic-models-for-animal-breeding-2\"\u003eGenetic Models for Animal Breeding\u003c/a\u003e, and advance your career as a breeder.\u003c/p\u003e\n\u003cp\u003eThe course is developed with financial support and input from the \u003ca href=\"http://www.koeponstichting.nl/index.php/about-us\" title=\"Koepon Foundation\"\u003eKoepon Foundation\u003c/a\u003e.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4db:T477,\u003cp\u003eAlgorithmics and programming are fundamental skills for engineering students, data scientists and analysts, computer hobbyists or developers.\u003c/p\u003e\n\u003cp\u003eLearning how to program algorithms can be tedious if you aren’t given an opportunity to immediately practice what you learn. In this course, you won't just focus on theory or study a simple catalog of methods, procedures, and concepts. Instead, you’ll be given a challenge wherein you'll be asked to beat an algorithm we’ve written for you by coming up with your own clever solution.\u003c/p\u003e\n\u003cp\u003eTo be specific, you’ll have to work out a route faster than your opponent through a maze while picking up objects.\u003c/p\u003e\n\u003cp\u003eEach week, you will learn new material to improve your artificial intelligence in order to beat your opponent. This structure means that as a learner, you’ll confront each abstract notion with a real-world problem.\u003c/p\u003e\n\u003cp\u003eWe’ll go over data-structures, basic and advanced algorithms for graph theory, complexity/accuracy trade-offs, and even combinatorial game theory.\u003c/p\u003e\n\u003cp\u003eThis course has received financial support from the Patrick and Lina Drahi Foundation.\u003c/p\u003e4dc:T55e,\u003cp\u003eToday’s businesses are investing significantly in capabilities to harness the massive amounts of data that fuel Business Intelligence (BI). Working knowledge of Data Warehouses and BI Analytics tools are a crucial skill for Data Engineers, Data Warehousing Specialists and BI Analysts, making who are amongst, the most valued resources for organizations.\u003c/p\u003e\n\u003cp\u003eThis course prepares you with the skills and hands-on experience to design, implement and maintain enterprise data warehouse systems and business intelligence tools. You’ll gain extensive knowledge on various data repositories including data marts, data lakes and data reservoirs, explore data warehousing system architectures, deepen on data cubes and data organization using related tables. And analyze data using business intelligence like Cognos Analytics, including its reporting and dashboard features, and inter"])</script><script>self.__next_f.push([1,"active visualization capabilities. \u003c/p\u003e\n\u003cp\u003eThis course provides hands-on experience with practice labs and a real-world inspired project that can be added to your portfolio and will demonstrate your proficiency in working with data warehouses. \u003cspan lang=\"EN\"\u003eSkills you will gain include building data warehouses, Star/Snowflake schemas, CUBEs, ROLLUPs, Materialized Views/MQTs, reports and dashboards.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis course assumes prior SQL and relational database experience.\u003c/p\u003e4dd:Tf4d,"])</script><script>self.__next_f.push([1,"\u003cp\u003eLa capacidad de analizar datos complejos y extraer conclusiones de ellos es algo que las empresas, cada vez más orientadas a la mejora del negocio a través del análisis de la ingente cantidad de datos que recogen, demandan de forma creciente a sus ejecutivos. Esto es conocido como inteligencia de negocios (Business Intelligence). \u003c/p\u003e\r\n\r\n\u003cp\u003eCon este programa de Certificación Profesional enfocado en la inteligencia de negocios aprenderás a utilizar Microsoft Excel desde 0, llegando a ser un experto en su uso para el análisis y visualización de datos, lo que te ayudará a avanzar en cualquier trabajo que requiera tomar decisiones informadas. \u003c/p\u003e\r\n\r\n\u003cp\u003eEmpezarás con las operaciones básicas de Microsoft Excel, las diferentes funciones y fórmulas que puedes usar y el uso de gráficos y formatos para presentar resultados. \u003c/p\u003e\r\n\r\n\u003cp\u003eLuego conocerás lo que son las tablas de datos, como importar y vincular datos desde distintas fuentes y cómo usar las tablas y gráficos dinámicos para extraer información en este programa informático de Microsoft. También conocerás las herramientas de análisis de hipótesis para la toma de decisiones basadas en datos. \u003c/p\u003e\r\n\r\n\u003cp\u003eEn el tercer curso de Excel te presentaremos técnicas avanzadas de importación de datos y estrategias diversas para consolidar y preparar los datos de forma que puedas extraer las conclusiones que necesitas (basadas en nuestra experiencia en el uso de Excel y demostradas con casos reales). Te presentaremos también las nuevas herramientas de modelo de datos e importación avanzada incorporados desde Excel 2010 que permiten trabajar de forma mucho más cómoda con grandes volúmenes de datos (Big Data) repartidos en varias tablas y automatizar la importación para no tener que repetir los pasos cada vez que actualices los datos. \u003c/p\u003e\r\n\r\n\u003cp\u003eEste es un certificado profesional que te permite empezar de 0 y conseguir un nivel avanzado de Excel para el tratamiento de datos, proporcionándote todo lo que necesitas para convertirte en un experto en el dominio de Excel enfocado en los negocios. Si quieres empezar de 0 y conseguir un certificado profesional de nivel intermedio, tienes la opción de este \u003ca href=\"https://www.edx.org/professional-certificate/upvalenciax-excel-para-los-negocios-nivel-intermedio\" target=\"_blank\"\u003e otro certificado\u003c/a\u003e, en el que harás los dos primeros cursos de este certificado y luego un MOOC sobre un caso práctico \u003ca href=\"https://www.edx.org/course/excel-creacion-de-un-panel-grafico-de-control-empresarial\" target=\"_blank\"\u003e (Excel: Creación de un panel gráfico de control empresarial)\u003c/a\u003e que te permitirá aplicar los conocimientos adquiridos.\u003c/p\u003e\r\n\r\n\u003cp\u003eComo los dos primeros cursos son los mismos, puedes empezar con ellos y, luego, decidir si quieres obtener el certificado avanzado o el intermedio, dependiendo del tercer curso que escojas. Incluso puedes obtener el certificado intermedio haciendo el MOOC del caso práctico y luego, si te ves con ganas, obtener el certificado avanzado haciendo \u003ca href=\"https://www.edx.org/course/excel-avanzado-importacion-y-analisis-de-datos\" target=\"_blank\"\u003e el MOOC de Excel avanzado: importación y análisis de datos\u003c/a\u003e.\u003c/p\u003e\r\n\r\n\u003cp\u003eSi ya tienes un nivel intermedio de Excel y quieres conseguir un certificado de nivel avanzado, tienes la opción de empezar con el mooc del caso práctico \u003ca href=\"https://www.edx.org/course/excel-creacion-de-un-panel-grafico-de-control-empresarial\" target=\"_blank\"\u003e (Excel: Creación de un panel gráfico de control empresarial)\u003c/a\u003e y hacer luego el mooc de \u003ca href=\"https://www.edx.org/course/excel-avanzado-importacion-y-analisis-de-datos\" target=\"_blank\"\u003e Excel avanzado\u003c/a\u003e para conseguir el certificado profesional en \u003ca href=\"https://www.edx.org/professional-certificate/upvalenciax-excel-para-los-negocios-nivel-avanzado\" target=\"_blank\"\u003e Excel para los negocios nivel avanzado\u003c/a\u003e.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4de:Ta2a,"])</script><script>self.__next_f.push([1,"\u003cp\u003eLa capacidad de analizar datos complejos y extraer conclusiones de ellos es algo que las empresas, cada vez más orientadas a la mejora del negocio a través del análisis de la ingente cantidad de datos que recogen, demandan de forma creciente a sus ejecutivos. Esto es conocido como inteligencia de negocios (Business Intelligence).\u003c/p\u003e\r\n\r\n\u003cp\u003eEste programa de Certificación Profesional, enfocado en la inteligencia de negocios, está pensado para aquellos que tienen un nivel intermedio de Excel y quieren conseguir un certificado de nivel avanzado. En él empezarás con un caso práctico en el que afianzarás tus conocimientos y luego harás un MOOC de Excel avanzado para el tratamiento y visualización de datos.\u003c/p\u003e\r\n\r\n\u003cp\u003eEn el primer curso de Excel resolverás un caso práctico creando un panel gráfico de seguimiento de negocio a partir de datos incluidos en una hoja de cálculo, lo que te permitirá repasar las funciones y herramientas básicas para el resumen de datos, como las tablas dinámicas y los gráficos avanzados. En el segundo curso te presentaremos técnicas avanzadas de importación de datos y estrategias diversas para consolidar y preparar los datos de forma que puedas extraer las conclusiones que necesitas (basadas en nuestra experiencia en el uso de Excel y demostradas con casos reales). Te presentaremos también las nuevas herramientas de modelo de datos e importación avanzada incorporados desde Excel 2010 que permiten trabajar de forma mucho más cómoda con grandes volúmenes de datos (Big Data) repartidos en varias tablas y automatizar la importación para no tener que repetir los pasos cada vez que actualices los datos.\u003c/p\u003e\r\n\r\n\u003cp\u003eSi eres principiante en Excel, tienes la posibilidad de empezar de 0 y conseguir un nivel avanzado de Excel para el tratamiento de datos con este \u003ca href=\"https://www.edx.org/es/professional-certificate/upvalenciax-excel-para-los-negocios\" target=\"_blank\"\u003e otro certificado\u003c/a\u003e que incluye dos cursos de nivel principiante e intermedio, o, si quieres empezar de 0 y conseguir un certificado profesional de nivel intermedio, tienes la opción de este \u003ca href=\"https://www.edx.org/professional-certificate/upvalenciax-excel-para-los-negocios-nivel-intermedio\" target=\"_blank\"\u003e otro certificado\u003c/a\u003e, en el que harás esos dos primeros cursos y luego el mooc sobre el caso práctico \u003ca href=\"https://www.edx.org/course/excel-creacion-de-un-panel-grafico-de-control-empresarial\" target=\"_blank\"\u003e (Excel: Creación de un panel gráfico de control empresarial)\u003c/a\u003e, que te permitirá aplicar los conocimientos adquiridos.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4df:T461,\u003cp\u003eEn este curso de análisis e interpretación de datos te presentaremos técnicas avanzadas de importación de datos y estrategias diversas para consolidarlos y prepararlos una vez importados de forma que puedas extraer las conclusiones que necesitas (basadas en nuestra experiencia en el uso de Microsoft Excel y demostradas con casos reales). \u003c/p\u003e\n\u003cp\u003eTe presentaremos también las nuevas herramientas y técnicas de modelo de datos e importación avanzada incorporados desde Excel 2010 que permiten trabajar de forma mucho más cómoda con grandes volúmenes de datos repartidos en varias tablas y automatizar la importación para no tener que repetir los pasos cada vez que actualices los datos. \u003c/p\u003e\n\u003cp\u003eVeremos también las distintas opciones disponibles a la hora de configurar tablas y gráficos dinámicos en Excel para que sean lo más versátiles posibles y nos permitan analizar distintos escenarios de forma sencilla. \u003c/p\u003e\n\u003cp\u003eEstas herramientas y técnicas de análisis de datos te permitirán implementar inteligencia de negocios (Business Intelligence) en los diferentes proyectos de una organización.\u003c/p\u003e4e0:T4d4,\u003cp\u003eEste curso estará compuesto por cuatro semanas, en cada una el alumno tendrá la posibilidad de analizar diferentes perspectivas sobre la razón de ser de los negocios y cómo innovan en su manera de satisfacer las expectativas de sus clientes.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eConocer las nuevas tendencias de comunicación digital como Influencer Marketing, para analizar cómo se está moviendo la publicidad de los estudios profesionales al dormitorio de un influencer. \u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eComprender el papel principal de la huella digital de una persona pero también de una marca en el ecosistema digital. \u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eConocer los fundamentos de SEO y por qué la búsqueda es el componente clave de cualquier estrategia digital. \u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAnalizar el impacto de estrategias SEM mediante el uso de diferentes técnicas y campañas. \u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePensar en el punto de vista UI / UX y por qué es"])</script><script>self.__next_f.push([1," tan desafiante cumplir con las necesidades y requisitos de un cliente. \u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eNavegar por las Redes Sociales, y analizar el impacto de cada una de ellas. \u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eProfundizar en el mundo de los datos mediante análisis basados en datos y escaladas de big data de las redes sociales.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e4e1:Ta26,"])</script><script>self.__next_f.push([1,"\u003cp\u003ePara el proceso de minería de datos es fundamental conocer a priori qué productos compran nuestros clientes y hacer un análisis de la cesta de la compra con los datos de entrada disponibles. Este conocimiento debe ser profundo, no solo es necesario conocer los productos que compran, sino también los patrones de comportamiento, es decir, las relaciones que existen entre la gran cantidad de los productos que se compran.\u003c/p\u003e\n\u003cp\u003eLa técnica de análisis de la canasta de mercado, requiere como primer paso conjuntos de datos de las compras de los clientes para poder realizar el preprocesamiento. Es decir, requiere los datos transaccionales generados cada vez que los clientes realizan una compra para posteriormente crear subconjuntos.\u003c/p\u003e\n\u003cp\u003eAl contar con grandes volúmenes de datos de todas las transacciones, el análisis de la cesta de la compra, puede realizarse mediante técnicas de minería de datos aplicada en las bases de datos. \u003c/p\u003e\n\u003cp\u003eLa técnica de análisis de la canasta de mercado, permite identificar diferentes association rules o reglas de asociación entre los datos disponibles sobre productos comprados en determinada cantidad de información. Y este proceso de descubrimiento es de gran beneficio para detectar más allá de un excel con gran cantidad de datos, información útil cómo si los clientes realizan regularmente el pago con tarjetas de crédito o consumen más determinado producto.\u003c/p\u003e\n\u003cp\u003eLas reglas de asociación forman un cuerpo de conocimientos muy útiles en mercadotecnia, ya que contribuirán a definir estrategias y tácticas que permitan acertar en las necesidades y deseos de los clientes. Tienen aplicaciones tales como: Soporte para la toma de decisiones, análisis de información de ventas, distribución de mercancías en los anaqueles de las tiendas y segmentación de clientes con base en patrones de comportamiento. Y permite la creación de árboles de decisión que al final impacte en una estrategia predictiva.\u003c/p\u003e\n\u003cp\u003eEn este curso aprenderás los fundamentos teóricos de análisis de datos y la técnica de minería de datos relacionada con el análisis de la cesta de la compra.\u003c/p\u003e\n\u003cp\u003eAdemás, mediante inteligencia artificial con el software especializado RapidMiner, aplicarás los conceptos en la creación de un modelo de minería de datos con aprendizaje automático o machine learning, que te permitirá realizar un completo análisis de la canasta de mercado y aplicar los resultados obtenidos en tu estrategia. Utiliza todas las herramientas de minería de datos a tu favor y aplícalo en grandes bases de datos.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4e2:T556,\u003cp\u003e¿Luchas con los datos en tu trabajo? ¿Pierdes tiempo valioso trabajando en muchas hojas de cálculo en Excel para obtener un resumen de tu negocio? ¿Tienes dificultades para obtener un tablero detallado a partir de montones de datos en tu escritorio? ¿Quieres entender cómo analizar Big Data?\u003c/p\u003e\n\u003cp\u003eSi estás buscando mejorar tu eficiencia en la oficina y aumentar tu rendimiento mediante una forma más rápida e inteligente de darle sentido a los datos, utilizando diferentes técnicas de análisis de datos, entonces este curso avanzado de análisis de datos es para ti.\u003c/p\u003e\n\u003cp\u003eSi ya sabes manejar las hojas de cálculo, este curso te ayudará a profundizar tus conocimientos aún más. Aprenderás técnicas avanzadas para un análisis de datos robusto en el ambiente de los negocios. Este curso cubre las principales tareas requeridas hoy por los analistas de datos y Big Data, incluyendo importar, resumir, interpretar, analizar y visualizar datos. Este curso de análisis de datos y estadísticas busca ofrecerte las herramientas que te permitan ser un analista de datos independiente y exitoso. La mayoría de las técnicas se enseñarán en Microsoft Excel 2013 y 2016 con extensiones y herramientas gratuitas disponibles en línea. Te invitamos a utilizar tus propios datos o, a falta de ellos, trabajar con los que provee el equipo del curso.\u003c/p\u003e4e3:Tdd9,"])</script><script>self.__next_f.push([1,"\u003cp\u003eHoy parece que se esta hablando de \"Big Data\" por todas partes. Pero ¿qué tan importante o relevante es esto y qué oportunidades ofrece para las organizaciones y nuestros países? ¡Inscríbete en el MOOC \"Big Data sin misterios\" y descubre las respuestas a estas preguntas!\u003c/p\u003e\n\u003cp\u003eLa revolución de los datos implica contar con un gran volumen de datos, pero también contar con nuevos tipos de datos. Gran parte de la población mundial, así como las máquinas, se encuentran conectadas a internet mediante dispositivos: celulares, computadores, sensores... Sin embargo, ¿estamos aprovechando el potencial de todos estos datos para tomar mejores decisiones? ¿Estamos generando valor a partir de ellos? Big Data supone un cambio de paradigma que se refiere tanto a los datos masivos como a las técnicas y tecnologías para su recopilación y análisis; con el fin de detectar patrones, aprender de la experiencia y predecir situaciones futuras para tomar decisiones estratégicas.\u003c/p\u003e\n\u003cp\u003e\"Big Data sin misterios\" te mostrara las ventajas de utilizar Big Data, la analítica avanzada y la inteligencia artificial, tanto en el sector público, como en el sector privado. El curso te proporcionará, mediante videos y lecturas, fundamentos teóricos, metodologías y ejemplos de casos reales de uso. También tendrás la oportunidad de rendir actividades evaluadas para que puedas poner a prueba lo aprendido.\u003c/p\u003e\n\u003cp\u003ePor último, la modalidad de este curso es \"a tu propio ritmo ( \u003cem\u003eself-paced\u003c/em\u003e )\". Esto significa que puedes registrarte en cualquier momento, aunque lleve abierto un tiempo. Si escoges pagar por el certificado verificado, tendrás la flexibilidad de tomar el curso hasta la fecha de cierre del curso.\u003c/p\u003e\n\u003cp\u003eLos estudiantes que obtienen el certificado en este curso también recibirán una insignia digital. Las insignias digitales permiten compartir más fácilmente y de forma más confiable las habilidades o el conocimiento adquirido. Para más informaciones sobre las insignias digitales puedes visitar el enlace \u003cem\u003e\u003cstrong\u003e\u003ca href=\"https://cursos.iadb.org/es/indes/credenciales-digitales\" rel=\"noopener\" target=\"_blank\"\u003einsignias digitales\u003c/a\u003e\u003c/strong\u003e\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eSi optas por la \u003cstrong\u003emodalidad asistente\u003c/strong\u003e tendrás acceso ilimitado a los contenidos del curso, pero no podrás realizar las actividades evaluadas ni obtener el certificado.\u003c/p\u003e\n\u003cp\u003eSi optas por la \u003cstrong\u003emodalidad certificado verificado\u003c/strong\u003e puedes acceder al curso y sus evaluaciones hasta la fecha de cierre, tras hacer un pago de USD 25\u003cspan lang=\"ES-ES\"\u003e. \u003c/span\u003e\u003cspan lang=\"ES-ES\"\u003eDe esta forma, si apruebas, además del certificado verificado, obtendrás una \u003cem\u003e\u003cstrong\u003e\u003ca href=\"https://cursos.iadb.org/es/indes/credenciales-digitales\" rel=\"noopener\" target=\"_blank\"\u003e\u003cspan lang=\"ES-ES\"\u003einsignia digital\u003c/span\u003e\u003c/a\u003e\u003c/strong\u003e\u003c/em\u003e que permite transformar la forma en que compartes tus logros académicos y profesionales, como, por ejemplo, en redes sociales.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"ES-AR\"\u003e¿Conoces la ayuda financiera de edX para optar al certificado verificado e insignia digital?\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEdX ofrece asistencia financiera a aquellos alumnos que tengan dificultades para realizar el pago. Inscríbete en el curso y rellena esta \u003cem\u003e\u003cstrong\u003e\u003ca href=\"https://courses.edx.org/financial-assistance/\" rel=\"noopener\" target=\"_blank\"\u003esolicitud de asistencia financiera\u003c/a\u003e\u003c/strong\u003e\u003c/em\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eConsulta más información en el apartado de \"Preguntas frecuentes\" que encontrarás más abajo.\u003c/li\u003e\n\u003c/ul\u003e"])</script><script>self.__next_f.push([1,"4e4:T7fc,\u003cp\u003eLos datos digitales son esenciales en la sociedad contemporánea. Cada vez son más numerosos, pero resulta difícil interpretarlos debido a su complejidad. Términos como bases de datos, big data y el business intelligence (inteligencia de negocios o inteligencia empresarial) son cada vez más comunes y se han convertido en parte fundamental de los usuarios de negocios para la toma de decisiones organizacionales. El Programa sobre Visualización de datos se ocupará de que aprendas a analizarlos y de la posibilidad de representarlos gráficamente para una comunicación eficaz, , ofreciendo información de manera sencilla y un análisis visual para la mejor comprensión de los datos.\u003c/p\u003e\r\n\r\n\u003cp\u003eLas actividades de este programa se fundarán en el empleo del software Tableau, el instrumento de referencia para las visualizaciones de datos, entre los más difusos en el mundo por su facilidad de uso. Los tres cursos se organizan en niveles progresivos:\u003c/p\u003e\r\n \r\n\u003cp\u003eEl primer curso se orienta a cualquier persona que trabaje con diferentes tipos de datos/fuentes de datos, independientemente de la precedente formación técnica. Aprenderás a utilizar el producto explorando, con una metodología práctica, los conceptos clave. Explorarás distintas técnicas para la creación de visualizaciones hasta juntarlos en un dashboard interactivo.\u003c/p\u003e\r\n \r\n\u003cp\u003eEl segundo curso se orienta a personas que hayan madurado una buena experiencia en el uso de Tableau y quieran profundizar las propias competencias, finalizadas a un uso más avanzado del instrumento. Aprenderás a personalizar los diseños de los dashboards para que la navegación en los datos resulte más interactiva.\u003c/p\u003e\r\n \r\n\u003cp\u003eEl tercer curso te permite aprender a elegir el gráfico correcto (representación gráfica) para el contexto en el que se emplearán. Se muestran las principales soluciones gráficas a disposición, así como las modalidades para poder compartirlas online y ponerlas a disposición incluso de quien no conoce Tableau"])</script><script>self.__next_f.push([1,".\u003c/p\u003e4e5:T9b2,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eWelcome to the Natural Disaster and Climate Change Risk Assessment in Infrastructure Projects course.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eClimate change is real, and it affects many aspects of our lives. Temperatures are becoming progressively more extreme and natural disasters are increasingly frequent. We cannot prevent floods, earthquakes, and tornadoes...\u003c/p\u003e\n\u003cp\u003eBut what can we do so that the damage caused has the least impact? How can we prevent our city’s infrastructure from suffering any damage and therefore put fewer people at risk? What kind of pre-project analyses do we need to perform so that we are prepared to address future climate change events?\u003c/p\u003e\n\u003cp\u003eStrengthen your technical and decision-making skills by incorporating climate change resilience and natural disaster risk analysis into the design of infrastructure projects.\u003c/p\u003e\n\u003cp\u003eThis course was designed and organized by the Inter-American Institute for Economic and Social Development (INDES) of the Inter-American Development Bank within the framework of its Operations Learning Program (OLP). Its objective is to strengthen project teams’ capacity in Latin America and the Caribbean to preemptively manage events that may affect a project so as to improve its chance of success.\u003c/p\u003e\n\u003cp\u003eIf you choose the \u003cstrong\u003eAudit Track\u003c/strong\u003e , you will have unlimited access to the course content, but you won't be able to complete the assessed activities or receive the certificate.\u003c/p\u003e\n\u003cp\u003eIf you opt for the \u003cstrong\u003eVerified Track\u003c/strong\u003e , you can access the course in an unlimited way and complete the qualified evaluations until the closing date, after making a payment of USD 25 \u003cem\u003e.\u003c/em\u003e If you pass, in addition to the verified certificate, you will obtain a \u003cem\u003e\u003cstrong\u003e\u003ca href=\"https://cursos.iadb.org/en/indes/credenciales-digitales\" rel=\"noopener\" target=\"_blank\"\u003edigital badge\u003c/a\u003e\u003c/strong\u003e\u003c/em\u003e that allows you to change the way you share your academic and professional achievements, as for example, on social media.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDid you know there is a financial aid to opt for the verified certificate?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEdX financial help:\u003c/strong\u003e edX provides financial assistance for learners who wish to obtain verified certificates but may face challenges in covering the associated fees. Subscribe to the course and \u003cstrong\u003e\u003cem\u003e\u003ca href=\"https://courses.edx.org/financial-assistance/apply/\" rel=\"noopener\" target=\"_blank\"\u003eapply for financial assistance\u003c/a\u003e\u003c/em\u003e.\u003c/strong\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4e6:T607,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eThe general objective of the course is to strengthen the technical capacity of \u003c/span\u003eprofessionals involved in the project cycle for decision-making in relation to the incorporation of disaster risk assessment and resilience to climate change in the design of infrastructure projects.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eBy the end of this course, you will be able to:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eIdentify the main elements of natural disaster risk.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eRecognize the importance of incorporating natural disaster risk assessment (including climate change effects) into the infrastructure project cycle.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eCarry out qualitative disaster-risk analyses to guide decision making in infrastructure projects.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eInterpret quantitative disaster-risk analyses to guide decision making in infrastructure projects.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eIdentify when applying qualitative analyses is enough and when quantitative analyses should also be carried out to perform risk assessment in infrastructure projects.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eIdentify technical and economic feasibility of infrastructure projects through a qualitative or quantitative analysis.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eMake risk-informed recommendations for the design, construction, and operation of \u003c/span\u003einfrastructures and develop disaster-risk-governance strategies.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e4e7:T710,\u003cp\u003eEste \u003cstrong\u003ecurso en línea\u003c/strong\u003e brinda una introducción al \u003cstrong\u003eanálisis de datos para business intelligence\u003c/strong\u003e. Aprenderás de herramientas y técnicas de estadística descriptiva e inferencial. Serás capaz de analizar data y gráficos para transformarla en información de valor que te permita obtener \u003cstrong\u003ecriterios para la toma de decisiones.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCon este curso podrás \u003cstrong\u003eutilizar datos para cumplir objetivos concretos como\u003c/strong\u003e :\u003c/p\u003e\n\u003c"])</script><script>self.__next_f.push([1,"ul\u003e\n\u003cli\u003eDescubrir quién es el cliente que representa mayor valor para la empresa\u003c/li\u003e\n\u003cli\u003eIdentificar cómo controlar los gastos\u003c/li\u003e\n\u003cli\u003eIdentificar cómo hacer más rápida la cadena de producción\u003c/li\u003e\n\u003cli\u003eSaber que esperar sobre un producto que acaba de ser lanzado al mercado\u003c/li\u003e\n\u003cli\u003eConocer cómo afecta determinado evento en las ventas\u003c/li\u003e\n\u003cli\u003eSaber cuál es el producto menos rentable para eliminarlo del portafolio\u003c/li\u003e\n\u003cli\u003eIdentificar las mejores epocas para hacer esfuerzos de posicionamiento de determinado producto\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTendrás dominio de los conceptos básicos y aprenderás a utilizar \u003cstrong\u003eestimadores\u003c/strong\u003e , histogramas, \u003cstrong\u003eescalas de medición\u003c/strong\u003e , varianza, desviación estandar, probabilidad normal, probabilidad informal, valor esperado de una variable aleatoria, \u003cstrong\u003eintervalos de confianza\u003c/strong\u003e , distribución de Poisson y más.\u003c/p\u003e\n\u003cp\u003eEste es el primer curso del \u003cstrong\u003ePrograma de Certificación Profesional\u003c/strong\u003e \u003cstrong\u003ede Inteligencia de Negocios\u003c/strong\u003e. El segundo curso es sobre \u003cstrong\u003eHerramientas de Business Intelligence\u003c/strong\u003e. Te recomendamos completar ambos para que adquieras conocimiento teórico y experiencia práctica. 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Sin embargo, los foros ya no serán supervisados por el equipo de IBM. Las preguntas técnicas relacionadas con tu experiencia en el curso deben dirigirse al equipo de soporte de edX a través de la información de contacto proporcionada en el curso. 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consulting firms specialized in investment and finance in EMDEs.\u003c/li\u003e\r\n\u003cli\u003e\u003cstrong\u003eAccess the latest information on investment and finance trends, innovations and results.\u003c/strong\u003e The course provides access to cutting-edge investment and finance strategy resources, diagnostic tools, analysis and statistics on investment opportunities and trends, and progress with development finance goals.\u003c/li\u003e\r\n\u003cli\u003e\u003cstrong\u003eCollaborate with thousands of investment, finance, development and policy experts around the world.\u003c/strong\u003e The course provides a global platform to collaborate and problem-solve with thousands of investment, finance, policy and development professionals around the world.\u003c/li\u003e\r\n\u003cli\u003e\u003cstrong\u003eEarn a World Bank Group – edX certificate.\u003c/strong\u003e Upon successful completion of the course, receive a certificate of completion to add to your LinkedIn profile and resume.\u003c/li\u003e\r\n\u003c/ul\u003e\r\n\u003cp\u003e\u003cstrong\u003eThis course is designed for:\u003c/strong\u003e\u003cbr /\u003e\r\n\u003cstrong\u003eInvestors\u003c/strong\u003e seeking to scale up their investments or invest for the first time in EMDEs, or seeking to invest in funds, bonds and sustainable investment vehicles in EMDEs will learn how to: \u003c/p\u003e\r\n\u003cul\u003e\r\n\u003cli\u003eDevelop roadmaps for investment in EMDEs.\u003c/li\u003e\r\n\u003cli\u003eUse investment diagnostic tools, de-risking and other investment support products of multilateral development banks.\u003c/li\u003e\r\n\u003cli\u003eInvest in sustainable investment bonds, funds and other vehicles.\u003c/li\u003e\r\n\u003cli\u003eAdopt principles and practice of impact investment.\u003c/li\u003e\r\n\u003c/ul\u003e\r\n\u003cp\u003e\u003cstrong\u003eGovernment officials in EMDEs\u003c/strong\u003e seeking to strengthen their national financing strategies and increase their access to 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underway to mobilize billions in development finance to attract and catalyze trillions more for resource of all kinds.\u003c/li\u003e\r\n\u003cli\u003eThe powerful and innovative new financial instruments and approaches that are being tested, deployed and scaled up globally to achieve the SDGs.\u003c/li\u003e\r\n\u003cli\u003eResults to-date with the financing for development agenda and actions needed to accelerate progress.\u003c/li\u003e\r\n\u003c/ul\u003e\r\n\u003cp\u003e\u003cstrong\u003eFinancial sector actors\u003c/strong\u003e seeking to learn about their role in catalyzing additional finance into EMDEs will learn about: \u003c/p\u003e\r\n\u003cul\u003e\r\n\u003cli\u003eActions needed to strengthen the stability, efficiency, and inclusiveness of financial systems and services.\u003c/li\u003e\r\n\u003c/ul\u003e\r\n\u003cp\u003e\u003cstrong\u003eOther professionals and students\u003c/strong\u003e are welcome to participate in the course.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4ee:Tc17,"])</script><script>self.__next_f.push([1,"\u003cp\u003eProject teams usually spend a large portion of their time solving problems instead of preventing them. Project risk management seeks to preemptively manage positive and negative events that may affect a project so as to improve its chance of success.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat will you be able to do by the end of the course?\u003c/strong\u003e\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eIdentify risks in scenarios of uncertainty\u003c/li\u003e\n\u003cli\u003eDetermine who can help you\u003c/li\u003e\n\u003cli\u003eUnderstand how to manage risks in settings with limited resources\u003c/li\u003e\n\u003cli\u003eDevelop risk response strategies\u003c/li\u003e\n\u003cli\u003eMonitor and update risks throughout the life of the project\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eWho is this course for?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe course is aimed at people from Latin America and the Caribbean.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eProject managers or supervisors\u003c/li\u003e\n\u003cli\u003eProject team members\u003c/li\u003e\n\u003cli\u003eFunders and financiers\u003c/li\u003e\n\u003cli\u003ePublic officials from national, subnational, and municipal entities\u003c/li\u003e\n\u003cli\u003eProfessionals from different fields who help develop and execute projects\u003c/li\u003e\n\u003cli\u003eProfessionals Interested in risk management in development projects\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eBy taking this course you will have the opportunity to share your knowledge and experience with other participants. There will be a case study through which you will be able to apply best risk-management practices to a project and you will also carry out practical exercises that will help you understand key concepts.\u003c/p\u003e\n\u003cp\u003eCourse content is based on the Inter-American Development Bank’s risk management methodology, which is aligned with A Guide to the Project Management Body of Knowledge, Sixth Edition, 2017, of the Project Management Institute (PMI)®.\u003c/p\u003e\n\u003cp\u003eThis course is \"self-paced\" so you can enroll at any time, even if the course has been open for a while. You can take it at the time that is most suitable for you inside the enrolment period of the course.\u003c/p\u003e\n\u003cp\u003eIf you choose the \u003cstrong\u003eAudit Track\u003c/strong\u003e , you can complete the course for free, but you won't have access to graded activities, and you won't be able to obtain a certificate upon course completion.\u003c/p\u003e\n\u003cp\u003eIf you opt for the \u003cstrong\u003eVerified track\u003c/strong\u003e , you can access the course in an unlimited way and complete the qualified evaluations until the closing date, after making a payment of $25 \u003cem\u003e. If you pass, in addition to the verified certificate, you will obtain a\u003c/em\u003e\u003ca href=\"https://cursos.iadb.org/en/indes/digital-badges\"\u003e \u003cstrong\u003edigital badge\u003c/strong\u003e\u003c/a\u003e that allows you to change the way you share your academic and professional achievements, as for example, on social media.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*Did you know there is a financial aid to opt for the verified certificate?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEdX financial help:\u003c/strong\u003e edX offers financial assistance for learners who want to earn Verified Certificates but who may not be able to pay the Verified Certificate fee. Subscribe to the course and \u003cstrong\u003e\u003ca href=\"https://courses.edx.org/financial-assistance/apply/\"\u003eapply for financial assistance\u003c/a\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSee more information in the Frequently Asked Questions section below.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4ef:T5ed,\u003cp\u003eThis course by Imperial College London is designed to help you develop the skills you need to succeed in your A-level maths exams.\u003c/p\u003e\n\u003cp\u003eYou will investigate key topic areas to gain a deeper understanding of the skills and techniques that you can apply throughout your A-level study. These skills include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFluency – selecting and applying correct methods to answer with speed and efficiency\u003c/li\u003e\n\u003cli\u003eConfidence – critically assessing mathematical methods and investigating ways to apply them\u003c/li\u003e\n\u003cli\u003eProblem solving – analysing the ‘unfamiliar’ and identifying which skills and techniques you require to answer questions\u003c/li\u003e\n\u003cli\u003eConstructing mathematical argument – using mathematical tools such as diagrams, graphs, logical deduction, mathematical symbols, mathematical language, construct mathematical argument and present precisely to others\u003c/li\u003e\n\u003cli\u003eDeep reasoning – analysing and critiquing mathematical techniques, arguments, formulae and proofs to comprehend how they can be applied\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOver seven modules, covering general motion in a straight line and two dimensions, projectile motion, a model for friction, moments, equilibrium of rigid bodies, vectors, differentiation methods, integration methods and differential equations, your initial skillset will be extended to give a clear understanding of how background knowledge underpins the A -level course.\u003c/p\u003e\n\u003cp\u003eYou’ll also be encouraged to consider how what you know fits into the wider mathematical world.\u003c/p\u003e4f0:T69c,\u003cp\u003eBy the end of this course, you'll be able to: \u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eUse calculus in kinematics for motion in a straight line\u003c/li\u003e\n\u003cli\u003eUse differentiation and integration of a vector with respect to time for motion in two dimensions\u003c/li\u003e\n\u003cli\u003eSolve projectile motion problems using both calculus/vector methods and constant acceleration formulae\u003c/li\u003e\n\u003cli\u003eUse a standard model for friction\u003c/li\u003e\n\u003cli\u003eCalculate moments understanding what they mean and how they might be used\u003c/li\u003e\n\u003cli\u003eSolve problems involving parallel and n"])</script><script>self.__next_f.push([1,"onparallel coplanar forces\u003c/li\u003e\n\u003cli\u003eApply an understanding of moments to statics problems involving rigid bodies\u003c/li\u003e\n\u003cli\u003eUse the Normal distribution as a model for continuous data\u003c/li\u003e\n\u003cli\u003eConduct a hypothesis test of the mean using a Normal distribution\u003c/li\u003e\n\u003cli\u003eUse a Normal distribution as an approximation of a Binomial distribution\u003c/li\u003e\n\u003cli\u003eAdd vectors diagrammatically\u003c/li\u003e\n\u003cli\u003ePerform the algebraic operations of vector addition and multiplication by scalars\u003c/li\u003e\n\u003cli\u003eApply vector calculations to problems in pure mathematics\u003c/li\u003e\n\u003cli\u003eUse methods for differentiating a function of a function, differentiating a product and differentiating a quotient\u003c/li\u003e\n\u003cli\u003eDifferentiate trigonometric and inverse trigonometric functions\u003c/li\u003e\n\u003cli\u003eUse implicit and parametric differentiation\u003c/li\u003e\n\u003cli\u003eIdentify integrals that can be dealt with “by sight”\u003c/li\u003e\n\u003cli\u003eUse a substitution method to integrate a function\u003c/li\u003e\n\u003cli\u003eUse partial fractions to integrate rational functions\u003c/li\u003e\n\u003cli\u003eUse the method of integration by parts\u003c/li\u003e\n\u003cli\u003eUse the method of separating the variable to solve differential equations\u003c/li\u003e\n\u003cli\u003efind the family of solutions for a differential equation\u003c/li\u003e\n\u003c/ul\u003e4f1:T53c,\u003cp\u003eAre you an urban planner, designer, policy maker or involved or interested in the creation of good living environments?\u003c/p\u003e\n\u003cp\u003eThis course will broaden your scope and diversify your take on the field of urban planning and design. We will focus on a unique Dutch approach and analyze how it can help those involved with urban planning and design to improve the physical environment in relation to the public good it serves, including safety, wellbeing, sustainability and even beauty.\u003c/p\u003e\n\u003cp\u003eYou will learn some of the basic traits of Dutch Urbanism, including its:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003econtextual approach;\u003c/li\u003e\n\u003cli\u003ebalance between research and design;\u003c/li\u003e\n\u003cli\u003esimultaneous working on multiple scale levels.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou will practice with basic techniques in spatial analysis and design pertaining to these points. You will also carry out these activities in"])</script><script>self.__next_f.push([1," your own domestic environment.\u003c/p\u003e\n\u003cp\u003eThis course is taught by the Faculty of Architecture and the Built Environment at TU-Delft, ranked no. 4 in Architecture/Built Environment on the QS World University Rankings (2016).\u003c/p\u003e\n\u003cp\u003eAll the material in this course is presented at entry level. But since the course has an integral perspective, combining planning and design aspects, it can still be relevant for trained professionals who feel they lack experience in either field.\u003c/p\u003e4f2:T794,\u003cp\u003eWildfires are a natural and essential part of our ecosystem, recycling soil nutrients and renewing healthy forests. In Alaska, around one million acres (4000 km2) burn every year, and record years have seen as many as six million acres burned. Most of these fires are allowed to spread naturally.\u003c/p\u003e\n\u003cp\u003eHowever, when wildfires occur near population centers, they can be a serious threat to the safety and well-being of people. Recent disastrous fire seasons in Alaska, California, and Australia have shown how communities near forests, in what is known as the Wildland Urban Interface (WUI), are increasingly at risk due to hotter and drier summer weather caused by climate change.\u003c/p\u003e\n\u003cp\u003eIn the fight to protect these communities, remote sensing technology has become essential. Wildfire analysts use satellite imagery and powerful computer programs to predict fire risk, detect fires early, and monitor their spread. After a wildfire is extinguished, remote sensing is used to analyze the impact of a fire and to guide sustainable restoration efforts.\u003c/p\u003e\n\u003cp\u003eParticipants in this course will learn about remote sensing of wildfires from instructors at the University of Alaska Fairbanks, located in one of the world’s most active wildfire zones. Students will learn about wildfire behavior, and get hands-on experience with tools and resources used by professionals to create geospatial maps that support firefighters on the ground.\u003c/p\u003e\n\u003cp\u003eUpon completion, students will be able to:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eAccess web resources that provide near real-time update"])</script><script>self.__next_f.push([1,"s on active wildfires\u003c/li\u003e\n\u003cli\u003eNavigate databases of remote sensing imagery and data\u003c/li\u003e\n\u003cli\u003eAnalyze geospatial data to detect fire hot spots, map burn areas, and assess severity\u003c/li\u003e\n\u003cli\u003eProcess image and GIS data in ArcGIS Pro\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eVerified track learners will receive a one-year ArcGIS Pro license in addition to unlimited course access and a verified certificate.\u003c/p\u003e4f3:Tabc,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis course by Imperial College London is designed to help you develop the skills you need to succeed in your A-level further maths exams.\u003c/p\u003e\n\u003cp\u003eYou will investigate key topic areas to gain a deeper understanding of the skills and techniques that you can apply throughout your A-level study. These skills include:\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e Fluency – selecting and applying correct methods to answer with speed and efficiency\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e Confidence – critically assessing mathematical methods and investigating ways to apply them\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e Problem solving – analysing the ‘unfamiliar’ and identifying which skills and techniques you require to answer questions\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e Constructing mathematical argument – using mathematical tools such as diagrams, graphs, logical deduction, mathematical symbols, mathematical language, construct mathematical argument and present precisely to others\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e Deep reasoning – analysing and critiquing mathematical techniques, arguments, formulae and proofs to comprehend how they can be applied\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eOver eight modules, you will be introduced to \u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e Simple harmonic motion and damped oscillations.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e Impulse and momentum.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e The work done by a constant and a variable force, kinetic and potential energy (both gravitational and elastic) conservation of energy, the work-energy principle, conservative and dissipative forces, power.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e Oblique impact for elastic and inelastic collision in two dimensions. \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e The Poisson distribution, its properties, approximation to a binomial distribution and hypothesis testing.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e The distribution of sample means and the central limit theorem. \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e Chi-squared tests, contingency tables, fitting a theoretical distribution and goodness of fit.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e Type I and type II errors in statistical tests.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eYour initial skillset will be extended to give a clear understanding of how background knowledge underpins the A -level further mathematics course. You’ll also be encouraged to consider how what you know fits into the wider mathematical world.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4f4:Tb5d,"])</script><script>self.__next_f.push([1,"\u003cp\u003eHow to derive and solve a second order differential equation that models simple harmonic motion.\u003c/p\u003e\n\u003cp\u003eHow to derive a second order differential equation for damped oscillations.\u003c/p\u003e\n\u003cp\u003eThe meaning of underdamping, critical damping and overdamping.\u003c/p\u003e\n\u003cp\u003eHow to solve coupled differential equations.\u003c/p\u003e\n\u003cp\u003eHow to calculate the impulse of one object on another in a collision.\u003c/p\u003e\n\u003cp\u003eHow to use the principle of conservation of momentum to model collisions in one dimension.\u003c/p\u003e\n\u003cp\u003eHow to use Newton’s experimental law to model inelastic collisions in one dimension.\u003c/p\u003e\n\u003cp\u003eHow to calculate the work done by a force and the work done against a resistive force.\u003c/p\u003e\n\u003cp\u003eHow to calculate gravitational potential energy and kinetic energy.\u003c/p\u003e\n\u003cp\u003eHow to calculate elastic potential energy.\u003c/p\u003e\n\u003cp\u003eHow to solve problems in which energy is conserved.\u003c/p\u003e\n\u003cp\u003eHow to solve problems in which some energy is lost through work against a dissipative force.\u003c/p\u003e\n\u003cp\u003eHow to calculate power and solve problems involving power. \u003c/p\u003e\n\u003cp\u003eHow to model elastic collision between bodies in two dimensions.\u003c/p\u003e\n\u003cp\u003eHow to model inelastic collision between two bodies in two dimensions.\u003c/p\u003e\n\u003cp\u003eHow to calculate the energy lost in a collision.\u003c/p\u003e\n\u003cp\u003eHow to calculate probability for a Poisson distribution.\u003c/p\u003e\n\u003cp\u003eHow to use the properties of a Poisson distribution.\u003c/p\u003e\n\u003cp\u003eHow to use a Poisson distribution to model a binomial distribution.\u003c/p\u003e\n\u003cp\u003eHow to use a hypothesis test to test for the mean of a Poisson distribution.\u003c/p\u003e\n\u003cp\u003eHow to estimate a population mean from sample data. \u003c/p\u003e\n\u003cp\u003eHow to estimating population variance using the sample variance. How to calculate and interpret the standard error of the mean.\u003c/p\u003e\n\u003cp\u003eHow and when to apply the Central Limit Theorem to the distribution of sample means. \u003c/p\u003e\n\u003cp\u003eHow to use the Central Limit Theorem in probability calculations, using a continuity correction where appropriate. \u003c/p\u003e\n\u003cp\u003eHow to apply the Central Limit Theorem to the sum of n identically distributed independent random variables.\u003c/p\u003e\n\u003cp\u003eHow to conduct a chi-squared test with the appropriate number of degrees of freedom to test for independence in a contingency table and interpret the results of such a test.\u003c/p\u003e\n\u003cp\u003eHow to fit a theoretical distribution, as prescribed by a given hypothesis involving a given ratio, proportion or discrete uniform distribution, to given data.\u003c/p\u003e\n\u003cp\u003eHow to use a chi-squared test with the appropriate number of degrees of freedom to carry out a goodness of fit test.\u003c/p\u003e\n\u003cp\u003eHow to calculate the probability of making a Type I error from tests based on a Poisson or Binomial distribution. \u003c/p\u003e\n\u003cp\u003eHow to calculate probability of making Type I error from tests based on a normal distribution. \u003c/p\u003e\n\u003cp\u003eHow to calculate P(Type II error) and power for a hypothesis test for tests based on a normal, Binomial or a Poisson distribution (or any other A level distribution).\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4f5:T558,\u003cp\u003eEste curso forma parte de una serie de 5 cursos de introducción al uso de sistemas de información en las empresas que te introducirá en el apasionante mundo de las TIC. \u003c/p\u003e\n\u003cp\u003eLa serie de cursos está pensada para que un profesional de un campo ajeno a las tecnologías de la información (financiero, administrativo o gerencial) adquiera los conocimientos básicos en Tecnologías de la Información necesarios para poder relacionarse de forma más provechosa con los especialistas en informática y telecomunicaciones de su empresa u otras corporaciones con las que tenga relación, siendo capaz de especificar requerimientos, evaluar cargas de trabajo y supervisar resultados de forma mucho más efectiva. \u003c/p\u003e\n\u003cp\u003eEn este curso de sistemas de información y ordenadores aprenderás los conceptos básicos de la programación de software. Trataremos la lógica básica que hay detrás de cualquier programa de ordenador, qué tipos y estructuras de datos y ficheros se utilizan, cómo funciona la programación orientada a objetos, la gestión de bases de datos y el lenguaje SQL y diversos conceptos y directrices en el desarrollo web, como el HTML, XML, CSS o javascript, entre otros. \u003c/p\u003e\n\u003cp\u003eAl completar la serie de 5 cursos, estarás preparado para relacionarte de forma eficaz con los especialistas del sector de las Tecnologías de la Información.\u003c/p\u003e4f6:T87c,"])</script><script>self.__next_f.push([1,"\u003cp\u003eEs fundamental conocer a nuestros clientes para alcanzar nuestro objetivo general. Este conocimiento debe ser profundo, no solo de sus cualidades y características, sino, también de sus patrones de comportamiento como consumidor, esta es información necesaria para alcanzar la fidelización del cliente. Un claro ejemplo es cómo funciona son los anuncios en redes sociales.\u003c/p\u003e\n\u003cp\u003eLa segmentación realiza un agrupamiento de los clientes de acuerdo a su comportamiento como consumidor. Los segmentos de comportamiento son grupos de clientes que se comportan de manera similar en relación con el negocio. Estos grupos de clientes con hábitos de compra similares son comúnmente llamados segmentos de clientes. \u003c/p\u003e\n\u003cp\u003eLa segmentación de clientes requiere toda la cantidad de información posible de ellos. Es decir, datos transaccionales generados al adquirir bienes o servicios, potencial de demanda, evolución y tendencias de mercado, entre otros.\u003c/p\u003e\n\u003cp\u003eAl contar con bases de datos, la segmentación y análisis de datos puede realizarse mediante técnicas de minería de datos que te permitan el descubrimiento del conocimiento del cliente. La segmentación de mercados mediante el algoritmo K-Means, te permite segmentar el mercado y crear conjuntos de datos, mediante el agrupamiento de clientes para interpretar información relevante de consumo. \u003c/p\u003e\n\u003cp\u003eA través del uso básico de la inteligencia artificial, en este curso aprenderás los fundamentos teóricos del Big Data y la técnica de minería de datos o data mining, relacionada con la segmentación de mercados. Serás capaz de realizar el pre procesamiento de datos, la selección de datos y el procesamiento de datos para obtener palabras clave que te permitan convertir la decisión de compra del cliente y encontrar datos relevantes para implementar medidas predictivas y generar árboles de decisión.\u003c/p\u003e\n\u003cp\u003eAdemás, mediante el software especializado RapidMiner, aplicarás los conceptos en la creación de un modelo de minería de datos, que te permitirá ser miembro de la actual inteligencia de negocios y tener una ventaja competitiva por medio del proceso de minería de datos.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4f7:T50d,\u003cp\u003eAn estimated 120 zettabytes of data are created each year—that’s 21 zeroes—including new data captured, copied, and consumed. With that number growing annually, the requirements for database infrastructure, architecture, and storage are evolving just as rapidly.\u003c/p\u003e\r\n \r\n\u003cp\u003eAccording to the U.S. Bureau of Labor Statistics, computer science for databases, including database administration, analysts, and architects, corresponds with these numbers with anticipated growth of 8% over the next 10 years, faster than the average for all occupations. To prepare yourself for a career in the industry, you must not only understand the basics of computer science, but also how to create relationships with the data being created or ingested.\u003c/p\u003e\r\n \r\n \r\n\u003cp\u003eUsing HarvardX’s most popular courses, CS50: Introduction to Computer Science as the foundation, learners explore how to think algorithmically and how to solve problems efficiently, using real-world data sets.You will build on those skills by developing the core competencies needed for database development and structures. By focusing on the primary database language of SQL, you will learn how to create data relationships, normalize data to decrease the potential for errors or redundancy, and automate and optimize searches.\u003c/p\u003e4f8:Ta6d,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThe US Bureau of Labor Statistics projects a 23% increase in cybersecurity jobs to 2033. The demand for cybersecurity analysts is growing ever stronger!\r\nWhat do cybersecurity analysts do? Cybersecurity analysts protect an organization's digital assets by identifying, analyzing, and mitigating security threats, ensuring data and systems remain secure against cyberattacks. \r\n \r\n\u003cp\u003eThis IBM Cybersecurity Professional Certificate has been designed by industry experts to provide you with the job-ready cybersecurity and digital forensic skills you need to jumpstart your cybersecurity career. No prior cybersecurity experience is required. \r\n\r\n\u003cp\u003eTo begin, you’ll build your foundational knowledge of cybersecurity essentials, tools, and technologies, operating systems, networking fundamentals, and the basics of cyberattacks. You’ll explore database fundamentals and vulnerabilities and learn about the steps a cybersecurity analyst would take to prevent, manage, and mitigate cybersecurity attacks. Plus, you’ll learn about cybersecurity architecture, compliance frameworks, standards, and regulations. \r\n\r\n\u003cp\u003eYou’ll also explore gen AI for cybersecurity and build valuable skills and hands-on experience in penetration testing, incident response, forensics, and threat intelligence. You’ll work on real-world projects developing cybersecurity plans and compliance frameworks, giving you great practical work to talk about in interviews. Plus, you’ll use real-world case studies that demonstrate that you can: \r\n\u003cul\u003e\r\n\u003cli\u003eApply incident response methodologies and forensics practices.\u003c/li\u003e \r\n\u003cli\u003eUse industry-specific security tools.\u003c/li\u003e \r\n\u003cli\u003eApply cybersecurity industry standards and best practices that mitigate risks, enhance security, and ensure compliance through audit processes.\u003c/li\u003e \r\n\u003cli\u003eUse gen AI tools to boost cybersecurity effectiveness and productivity.\u003c/li\u003e \r\n\u003cli\u003eInvestigate a real-world security breach by identifying the attack, vulnerabilities, costs, and prevention.\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e \r\n\r\n\u003cp\u003eTake advantage of the insights provided by seasoned industry experts to guide your job search, resume creation, and Interviewing to help you land your first cybersecurity role. This program will also prepare you to take and earn a 30% discount on the CompTIA Security+ and CySA+ certification exams. After successfully completing this program, you’ll have an industry-recognized IBM Professional Certificate that you can add to your resume and portfolio.\u003c/p\u003e\r\n\r\n\u003cp\u003eIf you’re looking to kickstart your cybersecurity career as a sought-after analyst, ENROLL TODAY, and look forward to being job-ready in less than 6 months!\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4f9:T8a0,"])</script><script>self.__next_f.push([1,"\u003cp\u003e¿Necesitas incorporar la inteligencia de negocio a tu empresa de forma que te permita analizar cantidades ingentes de datos (big data o macrodatos) para tomar las mejores decisiones?\r\nPower BI Desktop, la herramienta gratuita de análisis de datos de Microsoft te ayudará a conseguirlo con el menor esfuerzo posible. Gartner ha otorgado a Microsoft el reconocimiento de líder en el segmento de plataformas de análisis y business intelligence por decimocuarto año consecutivo en el informe Gartner Magic Quadrant de 2021.\u003c/p\u003e\r\n\r\n\u003cp\u003eMicrosoft Power BI Desktop te ofrece Power Query, una herramienta para importar, limpiar y acondicionar los datos a través de procesos automatizados, el modelo de datos, que te permitirá establecer relaciones entre los datos importados y añadir medidas complejas que sinteticen las conclusiones más importantes y una gran cantidad de visualizaciones que te permitirán presentar estos datos en tiempo real de forma interactiva y conectada y, no solo esto, si necesitas trabajar en equipo te ofrece una potente herramienta colaborativa en la nube (de pago) que, además, se integra con las herramientas de Microsoft Office y Teams.\u003c/p\u003e\r\n\r\n\u003cp\u003eEn este programa de Certificación Profesional conocerás cuáles son los componentes de una de las mejores herramientas de inteligencia empresarial, Power BI: Power BI Desktop para generar los informes, el servicio de Power BI en la nube para compartirlos y la aplicación móvil para hacer el seguimiento de tus datos en cualquier parte.\u003c/p\u003e\r\n\r\n\u003cp\u003eTe contaremos en profundidad cómo usar Power Query para automatizar la carga y adaptación de datos de cualquier fuente (ficheros de texto, servidores en la red, pdfs), cómo relacionar los datos importados y crear potentes medidas de forma rápida que sinteticen la información que necesitas y cómo visualizar los datos para que proporcionen la información de un solo vistazo. Todo ello con casos prácticos en los que podrás probar tus conocimientos recién adquiridos.\r\nTambién trataremos el servicio de Power BI en la nube y sus capacidades para el trabajo colaborativo, para que puedas aprovechar al máximo el potencial de la inteligencia de datos obtenida.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4fa:Tb35,"])</script><script>self.__next_f.push([1,"\u003cp\u003eEn el entorno laboral actual, es vital para los profesionales interpretar la gran cantidad de información cuantitativa a la que se tiene acceso, cada vez más tenemos que interpretar grandes volúmenes de datos (big data), ya sea analizando informes financieros o presentando datos de manera efectiva. Este programa de Certificación Profesional de vanguardia, ofrecido por EGADE Business School, escuela número uno de México y América Latina en Finanzas y Administración, te brindará la oportunidad de desarrollar estas habilidades de forma exitosa que son altamente requeridas por las empresas en el mercado laboral actual.\u003c/p\u003e\r\n\r\n\u003cp\u003eCon la ayuda de expertos, utilizarás ejercicios interactivos para aprender los fundamentos de matemáticas, estadística, ciencia de datos y finanzas, todos aplicados a los negocios.\u003c/p\u003e\r\n\r\n\u003cp\u003eLos temas cubiertos en este programa de Certificación Profesional te permitirán obtener una capacitación teórica y práctica a nivel de maestría o de posgrado para comprender mejor las funciones de la gerencia de negocios o business management; adicionalmente te preparará para continuar de forma exitosa tus estudios al decidir cursar la Maestría en Finanza o el MBA en EGADE.\u003c/p\u003e\r\n\r\n\u003cp\u003eEste programa te entregará los conceptos clave que necesitas para la toma de decisiones directivas exitosas en tu empresa, tanto en el corto plazo como en el largo plazo. Por ejemplo para tomar decisiones financieras, decisiones monetarias o decisiones de inversión es necesario conocer la tasa de interés, los costos de oportunidad, dominar los modelos financieros, conocer el presupuesto de capital y el capital de trabajo, el valor del dinero, analizar el riesgo total, conocer sobre la banca de inversión, este programa te entregará las habilidades cuantitativas que necesitas profesionalmente para ser exitoso en la toma de decisiones empresariales.\u003c/p\u003e \r\n\r\n\u003cp\u003eLas habilidades cuantitativas te ayudarán a tomar mejores decisiones empresariales, por ejemplo te proporcionarán información para obtener la mayor rentabilidad posible basándose en las opciones de inversión y considerando las decisiones de financiación. Las finanzas corporativas pueden ayudar a la empresa en la creación de valor, a analizar sus recursos financieros/fondos propios, a conocer los activos reales con los que cuenta la empresa, o seleccionar los mejores proyectos de inversión basándose en el valor actual del dinero, a entregar ingresos basados en la política de dividendos y a conocer los análisis utilizados en los procesos.\u003c/p\u003e\r\n\r\n\u003cp\u003eEl programa también cubrirá la ciencia de datos, la cual ayuda en la toma exitosa de decisiones operativas y decisiones directivas basadas en el análisis de los datos, por ejemplo, tomando en cuenta la información que entregan los datos se puede determinar el uso eficiente de los recursos.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4fb:Tc7a,"])</script><script>self.__next_f.push([1,"\u003cp\u003eEnvironmental stewardship and sustainability are becoming guiding principles for all forward-thinking organizations. The University of Maryland Center for Environmental Science (UMCES) has been at the forefront of environmental stewardship for over 90 years and is committed to developing the next generation of scientists, business leaders, policy-makers, natural resource managers, and educators to meet the unprecedented environmental and allied social challenges of the 21st century. This Professional Certificate program will guide anyone with an interest in environmental management to a) obtain literacy in environmental issues, b) use data of different spatial scales to drive decision-making, and c) effectively communicate environmental policy across a broad spectrum of constituencies.\u003c/p\u003e\r\n\r\n\u003cp\u003eWe will explore the evolution of environmental management among pioneering thought-leaders in conservation ecology and will utilize invaluable supplementary reading to understand how their essential texts remain relevant to this day. Further, we will explore how early conservation and environmental movements were shaped by the reality of politics, policy, and science. Finally, we will explore how today’s environmental managers inform effective policy when using a participatory, transdisciplinary approach to socio-environmental management and justice.\u003c/p\u003e\r\n\r\n\u003cp\u003eEnvironmental decision-making requires an approach that best matches your skill sets and organizational needs with crucial environmental issues. We will explore case-studies of the management of large, complex systems, investigate how to scale and transfer the lessons-learned from these case studies, and explore strategies for integrating coupled human and natural systems. From these global examples, including Chesapeake Bay, the world’s best studied estuary, you will learn how to develop solutions to your organization’s unique socio-environmental challenges.\u003c/p\u003e\r\n\r\n\u003cp\u003eBest-practice environmental management goes hand-in-hand with current methods of communicating changes to complex ecosystems in a manner that is accessible to all stakeholders. Today’s complex environmental challenges ranging from climate change and global warming to land use change require clear and compelling science communication. For example, diffuse sources of pollution like atmospheric emissions and subsequent deposition or groundwater contamination are difficult subjects to envision. You will learn from experienced and talented science communicators how to generate compelling graphics and visualizations combined with an effective narrative structure that will serve as springboards for action within any organization.\u003c/p\u003e \r\n\r\n\u003cp\u003eSocio-environmental report cards are acknowledged as THE effective tool for communicating status and trends of socio-environmental systems and engaging stakeholders. These tools are being used on a global scale to effect positive change at the local level. Report cards releases have become media events that have sweeping community reach from schools to legislatures. This course will walk you through the process of how these incredibly useful tools are created and disseminated.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4fc:T11ed,"])</script><script>self.__next_f.push([1,"\u003cp\u003eVivimos en un mundo direccionado por los datos, donde las organizaciones públicas y privadas de todo tipo de origen, tamaño y naturaleza, cuentan con grandes cantidades de datos o big data que, procesados con herramientas y servicios, tienen el potencial de ayudar al crecimiento económico y social y crear nuevas oportunidades de mercado. El análisis de estos datos es conocido como inteligencia de negocios o business intelligence y es fundamental para la toma de decisiones.\u003c/p\u003e \r\n\r\n\u003cp\u003eEste Programa de Certificación Profesional, compuesto por dos cursos en línea, te ayudará a comprender este contexto actual y qué criterios deben tener en cuenta quienes formulan políticas públicas al momento de redactar o desarrollar leyes, regulaciones o políticas en materia de protección de datos y privacidad, así como aspectos prácticos de los programas de gobernanza de datos.\u003c/p\u003e \r\n\r\n\u003cp\u003eLos cursos online que hacen parte de este programa son: gobernanza de datos personales en la era digital y protección de datos personales en la era digital. Comienza a aprender sobre el manejo de los datos.\u003c/p\u003e\r\n\r\n\u003cp\u003eLa protección de los datos personales ha tomado gran importancia en los últimos años. Ello se debe a que las organizaciones de todo tipo se valen de herramientas tecnológicas y digitales que permiten organizar la información, incluyendo los datos personales, para darle tratamiento de manera más rápida, eficiente y efectiva. El avance tecnológico permite a las organizaciones, gobiernos, y agentes sociales, potenciar sus capacidades de operar y lograr sus metas y objetivos.\u003c/p\u003e\r\n\r\n\u003cp\u003eMientras que hace treinta años una visita al supermercado apenas involucraba intercambiar información sobre el valor de los productos para realizar el pago de los mismos, hoy por hoy existen supermercados que usan tecnología digital de la información y la comunicación para rápidamente identificar al comprador, identificar los productos que pone en su canasta, y sin necesidad de sacar dinero o tarjetas de crédito del bolsillo, hacer el pago automático simplemente saliendo de la tienda con los productos recién sacados del estante. Todo ello se logra gracias al procesamiento de información, entre ella datos personales (identificación, cuentas bancarias, hábitos de compra, etc.). Lo mismo ocurre cuando hay intercambios sociales, comerciales o de simple información en internet, a través de aplicaciones, a través de sitios web.\u003c/p\u003e\r\n\r\n\u003cp\u003eEn este escenario rico en información y especialmente en datos personales, se requieren reglas para el adecuado manejo de este tipo de información. Esas reglas son especialmente importantes cuando la información compartida digitalmente se refiere a datos de salud, preferencia sexual, origen racial, creencias religiosas, información genética, número telefónico, origen étnico, opiniones políticas, información bancaria, entre otros datos personales. En este ámbito, asuntos como la implementación de protocolos de manejo de información, así como de medidas de seguridad para el manejo de los datos personales, son de suma importancia.\u003c/p\u003e\r\n\r\n\u003cp\u003eTodos aquellos agentes de la comunidad que realicen tratamiento de datos personales, especialmente en estos tiempos altamente digitales, deben poner la protección de los datos personales como una prioridad de sus organizaciones, de sus colaboradores, de sus directivos, de sus proveedores. Los titulares de los datos personales tienen derechos que han de ser protegidos y las organizaciones deben establecer mecanismos, procesos y protocolos para ello.\u003c/p\u003e\r\n\r\n\u003cp\u003eSi bien no todos los países tienen las mismas reglas, existen principios orientadores, aproximaciones similares, y otras diametralmente diferentes. Es importante que se conozcan los diferentes enfoques frente a la privacidad y los datos personales, ya que el mundo digital involucra muchas veces la interacción con agentes de otros países y sujetos a otras jurisdicciones.\u003c/p\u003e\r\n\r\n\u003cp\u003eEste Programa de Certificación Profesional, compuesto por dos cursos en línea, te ayudará a comprender este contexto actual y qué criterios deben tener en cuenta quienes formulan políticas públicas al momento de redactar o desarrollar leyes, regulaciones o políticas en materia de protección de datos y privacidad, así como aspectos prácticos de los programas de gobernanza de datos.\r\nLos cursos online que hacen parte de este programa son: gobernanza de datos personales en la era digital y protección de datos personales en la era digital. Comienza a aprender sobre el manejo de los datos.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4fd:T675,\u003cp\u003eLa ciencia de datos y las habilidades de aprendizaje automático siguen teniendo una gran demanda en todas las industrias, y la necesidad de profesionales de datos está en auge. Al completar este programa de Certificación Profesional, estarás equipado con los conceptos básicos para comenzar tu carrera en ciencia de datos y aprendizaje automático (machine learning).\u003c/p\u003e\r\n\r\n\u003cp\u003eEs un mito que para convertirse en un científico de datos se necesita un doctorado. Esta Certificación Profesional es adecuado para cualquier persona que tenga algunas habilidades informáticas y una pasión por el autoaprendizaje. No se necesitan conocimientos previos de informática o programación. Cualquier persona con algunas habilidades informáticas y una pasión por el autoaprendizaje puede tener éxito, comenzamos con fundamentos y vamos desarrollamos problemas y temas más complejos.\u003c/p\u003e\r\n\r\n\u003cp\u003eCuando estés listo, puedes desarrollar temas más complejos en nuestro programa de Certificación Profesional en Ciencia de Datos, compuesto por de 9 cursos, que cubre una amplia gama de temas de ciencia de datos que incluyen: herramientas y bibliotecas de código abierto, metodologías, Python, bases de datos, SQL, visualización de datos , análisis de datos, aprendizaje automático y un proyecto final.\u003c/p\u003e\r\n\r\n\u003cp\u003eCon la gran necesidad de profesionales con habilidades relacionadas a la ciencia de datos y análisis de datos en el mercado actual, este programa iniciará tu camino en la ciencia de datos y te entregará los fundamentos de la ciencia de datos para que tengas la confianza de dar el paso y comenzar tu carrera en ciencia de datos.\u003c/p\u003e4fe:T521,\u003cp\u003eThe US Bureau of Labor Statistics forecasts a 32% growth in information security analyst jobs until 2032. These analysts are in demand as part of the team that keeps networks secure.\u003c/p\u003e\n\u003cp\u003eThis course provides practical hands-on computer networking and network security experience that employers want. Through innovative hands-on labs, you’ll learn how to secure a small "])</script><script>self.__next_f.push([1,"home office network (SOHO), install and configure DHCP, and filter DNS. You’ll also get real-world practice installing and using an open-source Extended Detection and Response (XDR) system.\u003c/p\u003e\n\u003cp\u003eAdditionally, you’ll build valuable supporting knowledge of ports, protocols, and IP addresses, including IPv6 and network routing. You’ll learn about layer 2 and 3 addressing, routers, and routing tables.\u003c/p\u003e\n\u003cp\u003ePlus, you’ll develop knowledge of cybersecurity analyst tools for data protection, endpoint protection, and Security information and event management (SIEM), which you can apply to an organization’s compliance and threat intelligence needs, which is crucial in today’s cybersecurity landscape.\u003c/p\u003e\n\u003cp\u003eYou’ll complete a final project where you will demonstrate your ability to perform network and security planning tasks.\u003c/p\u003e\n\u003cp\u003eNetworking and network security skills pay. Invest in yourself and enroll today!\u003c/p\u003e4ff:T576,\u003cp\u003eThis course by Imperial College London is designed to help you develop the skills you need to succeed in your A-level mathematics exams.You’ll also be encouraged to consider how what you know fits into the wider mathematical world. \u003c/p\u003e\n\u003cp\u003eOver seven modules, covering an introduction to calculus, Newton’s laws and statistical hypothesis testing your initial skillset will be extended to give a clear understanding of how background knowledge underpins the A -level course. \u003c/p\u003e\n\u003cp\u003eYou will investigate key topic areas to gain a deeper understanding of the skills and techniques that you can apply throughout your A-level study. These skills include: \u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFluency – selecting and applying correct methods to answer with speed and efficiency\u003c/li\u003e\n\u003cli\u003eConfidence – critically assessing mathematical methods and investigating ways to apply them\u003c/li\u003e\n\u003cli\u003eProblem solving – analysing the ‘unfamiliar’ and identifying which skills and techniques you require to answer questions\u003c/li\u003e\n\u003cli\u003eConstructing mathematical argument – using mathematical tools such as diagrams, graphs, logic"])</script><script>self.__next_f.push([1,"al deduction, mathematical symbols, mathematical language, data handling, construct mathematical argument and present precisely to others\u003c/li\u003e\n\u003cli\u003eDeep reasoning – analysing and critiquing mathematical techniques, arguments, formulae and proofs to comprehend how they can be applied\u003c/li\u003e\n\u003c/ul\u003e500:Tc7a,"])</script><script>self.__next_f.push([1,"\u003cp dir=\"ltr\"\u003eEl mercado se ha vuelto cada vez más complejo. Los consumidores son mucho más exigentes con los productos y servicios que se les ofrecen, constantemente están comparando a las empresas competidoras y requieren cada vez mejores experiencias de compra. Esta dinámica está orillando a los negocios a actualizar sus estrategias en general. Es por ello que hoy se pueden ver innovadores modelos de negocio, diversas estrategias digitales para satisfacer las necesidades de distintos grupos de compradores, y modificaciones importantes en las estructuras organizacionales.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\n\nEste curso se divide en cuatro temas en los que se guiará a los participantes en la identificación y definición de los modelos de negocio actuales, y en las maneras de rediseñar los modelos de negocio tradicionales. Se mencionarán diversas maneras de adaptar las organizaciones a la digitalización, proponiendo cambios en la cultura organizacional que favorezcan esa evolución en los negocios, y se hablará de algunos facilitadores tecnológicos que impulsan la transformación digital. Todo esto con la intención de dar a los estudiantes más herramientas para lograr exitosamente el cambio hacia la digitalización de su negocio. Haciendo énfasis en que la Transformación Digital no es un capricho del mercado. Sino una necesidad que está fortaleciéndose al paso del tiempo y que está orillando a buscar diversas soluciones que satisfagan tanto a los consumidores, como a los negocios. Al final del camino se busca una relación ganar- ganar, donde las empresas se reestructuran, invierten y se transforman en pro de adaptarse a las nuevas necesidades de sus clientes, y éstos generan lealtad en aquellos negocios que les ofrecen mejores experiencias de compra.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\n\nSi los retails (o cualquier negocio) desea mantenerse vigente en el gusto de los compradores, forzosamente deberá considerar incursionar en la digitalización para diversificar las opciones que puede ofrecerles a los consumidores y satisfacer de mejor manera sus necesidades. Debemos ser conscientes de que la Transformación Digital seguirá fortaleciendo su importancia, y que quienes no se sumen a esta tendencia, difícilmente lograrán sobresalir en el mercado.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\n\nPara ello los retailers deben conocer cómo se pueden evaluar las herramientas existentes para la digitalización, y así analizar los beneficios que ofrecen, frente a los esfuerzos que requieren. Todo esto con la finalidad de escoger aquellas herramientas que puedan ser más útiles en sus negocios. Al mismo tiempo que puedan desarrollar la Omnicanalidad de sus estrategias. Lo que les dará congruencia, facilidad de identificarse con sus clientes, y fortaleza en cuando a su imagen y esfuerzos de venta. \u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\n\nComo se ha comentado, uno de los objetivos es el de fomentar la Transformación Digital en los retails a partir del conocimiento de los requerimientos de éstas, de las estrategias que se pueden implementar, junto con los beneficios que éstas pueden ofrecer. Todo con la finalidad de que, a partir del conocimiento, los participantes se animen a digitalizar sus negocios.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"501:T451,\u003cp dir=\"ltr\"\u003eLa dinámica que se observa en el mercado hoy en día, está exigiendo a las empresas a actualizar sus estrategias (modelos de negocio, estructura organizacional, innovar en sus productos y servicios, etc.) para poder seguir siendo competitivas.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\n\nEsto requiere de cambios importantes en los que se necesitan líderes capaces de impulsar a los negocios y a sus colaboradores hacia todos esos cambios. En este curso además de identificar conceptos básicos de la transformación digital, se distinguirán las nuevas tecnologías y metodologías para aplicar de manera correcta esa transformación en los retails.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\n\nSe mencionará la importancia del cambio cultural en las organizaciones, en pro de alinearse con los nuevos requerimientos y necesidades del mercado y se verá una propuesta metodológica para lograr una transformación digital exitosa. Todo esto desarrollado en cuatro temas que llevarán de la mano al participante hacia el entendimiento de la necesidad de digitalizarse y conservar un posicionamiento adecuado en su mercado e industria.\u003c/p\u003e502:Ta60,"])</script><script>self.__next_f.push([1,"\u003cp\u003eEste curso se orienta a la formación de profesionales en Inteligencia Empresarial mediante el aprendizaje de la plataforma Tableau, instrumento líder del sector. Tableau permite ayudar a las que personas vean y entiendan los propios datos. Actualmente, las empresas y los entes de la Pública Administración cuentan con moles de datos cada vez mayores, pero a menudo no son capaces de extraer la información útil de estos. El objetivo que pretende cualquier analista es el de analizar los datos a disposición para obtener información y, a partir de ellos, generar nuevos conocimientos. En el proceso analítico, la visualización de datos supone su papel crucial. A diferencia de la opinión común, la visualización de datos no se refiere solamente a la elección de las propiedades gráficas que se deben asignar a los datos, sino que es la pieza clave del proceso analítico que permite entender cómo representar de la mejor manera los propios datos y obtener información útil en la que fundar las propias decisiones sobre estrategias orientadas en datos (data-driven). Considerando esto, Tableau es el instrumento ideal para obtener información oculta en los datos de forma fácil y veloz. La suite de Tableau incluye numerosos productos que permiten que los analistas se preparen, analicen y compartan los propios datos.\u003c/p\u003e\n\u003cp\u003eEn las lecciones de este curso se presentarán todas las funcionalidades de Tableau Desktop, la herramienta dedicada a la construcción de los análisis. El primer capítulo ofrece una panorámica de todos los elementos preliminares para conocer el instrumento. Se presentan los distintos productos de la suite de Tableau, la interfaz gráfica y los ambientes de trabajo para pasar, en un segundo momento, a la presentación de las unidades mínimas del trabajo en Tableau: dimensiones, medidas y tipos de datos. En el segundo capítulo veremos los contenidos relativos a los modos y a los tipos de conexiones a los datos. Tableau admite numerosas conexiones, tanto a archivos locales como a servidores, y permite guardar el trabajo hecho en distintos formatos. El tercer capítulo se dedica a las estrategias de selección y organización de la información. Para que el análisis sea más eficaz y para garantizar una mejor comprensión de los datos, Tableau pone a disposición numerosas opciones de filtrado, orden y agrupación de datos. El cuarto capítulo contiene todos los elementos necesarios para aprender a trabajar con conjuntos, fechas y medidas múltiples. Esta sección presenta distintos contenidos orientados al aprendizaje de modalidades para combinar los datos en el ambiente laboral.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"503:Teeb,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cspan lang=\"ES\"\u003eLa AI o inteligencia artificial, el internet de las cosas, el Big Data, los asistentes virtuales y las tecnologías digitales han cambiado las reglas del juego en el mundo de los negocios. \u003c/span\u003eEl acceso a internet mediante dispositivos móviles y portátiles transformaron la manera como las personas interactúan con el mundo. Estas interacciones han permitido la generación y recolección de datos que los usuarios que utilizan diariamente aplicaciones, redes sociales y teléfonos móviles. Esto brinda información muy valiosa tanto a organizaciones, como a empresas. En consecuencia, las organizaciones deben enfrentar nuevos retos que implican el resguardo de datos personales de sus clientes potenciales y activos mediante las mejores prácticas que diferentes reguladores mundiales emiten.\u003c/p\u003e\n\u003cp\u003ePor todo lo anterior y, ante el crecimiento de la cantidad de datos, la inteligencia artificial es vital para entender y aprovechar todo este conocimiento. No solo eso, el desarrollo de las tecnologías digitales ha crecido a tal grado que implementaciones como el reconocimiento de imágenes, el lenguaje natural o chatbots, el desarrollo de aplicaciones para el uso de asistentes personales como Siri o Alexa y el aprendizaje automático para desarrollar aplicaciones de IA pueden ser aplicados a soluciones de negocios sin la necesidad de contar con amplios recursos materiales para llevarlos a cabo.\u003c/p\u003e\n\u003cp\u003ePor otro lado, el mundo de los negocios ha comenzado a ser dominado por las grandes empresas que implementan metodologías ágiles y aplican soluciones de inteligencia artificial y machine learning en sus procesos. Esto potencializa la calidad de sus procesos, mejora el entendimiento de las necesidades de los clientes, brinda una mejor toma de decisiones basada en datos y ayuda a dirigir los esfuerzos de las organizaciones al mejoramiento de la experiencia de usuario. Empresas como IBM, Amazon, Microsoft o Google son punta de lanza en el sector empresarial debido al dominio de estas metodologías que permiten reducir el riesgo que implica apostarle a la innovación.\u003c/p\u003e\n\u003cp\u003eSegún un reporte de Forrester del 2020 titulado “\u003ca href=\"https://www.ibm.com/downloads/cas/KNQQOLGG\"\u003eReinventing Workflows\u003c/a\u003e”, las organizaciones han identificado un impacto directo en la optimización de sus procesos gracias a la inteligencia artificial en las áreas de mejoramiento de experiencia de usuario con un 66% de efectividad; optimizaron la agilidad de su organización en un 62%; incrementaron sus utilidades en un 59%; incrementaron su productividad en un 58%; y redujeron costos en un 50%.\u003c/p\u003e\n\u003cp\u003ePor lo anterior, es vital que las empresas adopten estrategias que impliquen una transformación digital gradual para que puedan abrirse a las nuevas oportunidades que brinda la inteligencia artificial si desean mantenerse vigentes en el mercado y ser competitivas.\u003c/p\u003e\n\u003cp\u003eEn este MOOC abordarás las herramientas que permiten aprovechar las ventajas de la tecnología mediante casos prácticos y amigables para aplicar en tu negocio. Te proporcionaremos todos los consejos importantes que llevarán a tu empresa al éxito utilizando la IA. Así mismo, te brindaremos los conocimientos técnicos con un enfoque amigable que te permitan entender cómo funcionan los sistemas de inteligencia artificial actuales para recolectar y realizar análisis de datos en tiempo real.\u003c/p\u003e\n\u003cp\u003eAbordaremos distintos ejemplos prácticos de aplicación de inteligencia artificial en diferentes áreas de negocio y con diferentes tecnologías y te proporcionaremos herramientas amigables para la solución de casos aplicados a los negocios.\u003c/p\u003e\n\u003cp\u003eNuestro interés no es que tu negocio se convierta en un proveedor de tecnología, sino que ocupes la tecnología para potencializar tu negocio.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"504:T611,\u003cp\u003eLa justicia abierta es un movimiento en auge que utiliza las nuevas tecnologías, el big data, las plataformas digitales y el blockchain, entre otros, para mejorar nuestros sistemas legales haciendo más transparente el funcionamiento del sistema judicial y aumentando su rendición de cuentas. Gracias a las nuevas herramientas tecnológicas, la oportunidad de realizar mejoras nunca ha sido tan grande. Este curso está diseñado para ayudar a los emprendedores públicos: personas apasionadas como tú. Ya seas un abogado, un juez, un tecnólogo o simplemente un ciudadano, este curso te ayudará a saber como puedes usar las nuevas tecnologías para aumentar la eficiencia, mejorar la equidad, combatir la corrupción y mejorar la legitimidad del poder judicial.\u003c/p\u003e\n\u003cp\u003eEste curso en línea consta de diez módulos cortos que te servirán como breves introducciones a diferentes aspectos de la justicia abierta. Estas 'mini conferencias' de aproximadamente diez minutos se combinan con entrevistas a profesionales destacados que han liderado iniciativas de justicia abierta alrededor del mundo a través de la recopilación de datos y su análisis y la creación de aplicaciones. Estas entrevistas tienen como objetivo central que conozcas a personas clave que han trabajado activamente construyendo este movimiento que nos ayuda a todos conocer mejor y hacer valer nuestros derechos.\u003c/p\u003e\n\u003cp\u003eQueremos reconocer al \u003ca href=\"https://www.te.gob.mx/\"\u003eTribunal Electoral del Poder Judicial de la Federación México\u003c/a\u003e como fundador de este MOOC.\u003c/p\u003e505:T92a,"])</script><script>self.__next_f.push([1,"\u003cp\u003eChampions of digital transformation have many data technologies to choose from:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eArtificial intelligence\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMachine learning\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCloud computing\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDigitization\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003ePredictive analytics\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInternet of Things\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDeep learning\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAs a business leader, you may be asking yourself, what’s next? R? Python? Graph Databases? The latest data analytics technology that was launched last week?\u003c/p\u003e\n\u003cp\u003eHow do you craft an effective digital strategy from these technologies? How do you build the data transformation roadmap to give your organization a competitive advantage? How will you digitally transform your organization to give the best customer experience?\u003c/p\u003e\n\u003cp\u003eIn this course, we will explore three case studies to learn the practical tools and methods used to build a digital organization. The first course gave you the theory of the dynamic capabilities, fitness landscapes, and strategic foresight framework to transform an organization. Now, we will put those theories to use in three types of organizations.\u003c/p\u003e\n\u003cp\u003eThe first organizational type is a new startup. You will learn how to build a digital organization from the ground up using the lean startup methodology. \u003c/p\u003e\n\u003cp\u003eThe second organizational type is an established organization. This type of organization has developed processes and deep-rooted culture. The challenge is to help the organization digitally transform while retaining the best aspects of the organization.\u003c/p\u003e\n\u003cp\u003eThe third organizational type is the organization that turns to digital transformation to help save it. This may be the most challenging digital transformation effort because you need to regain customer trust under a tight deadline while your competitors are eager to capture your customers.\u003c/p\u003e\n\u003cp\u003eFor all three digital transformations, we will use Disciplined Agile Delivery to plan and execute the organizational change programs. \"\u003ca href=\"https://www.pmi.org/disciplined-agile/process/introduction-to-dad\"\u003eDisciplined Agile Delivery (DAD) is a people-first, learning-oriented hybrid agile approach to IT solution delivery. It has a risk-value delivery lifecycle, is goal-driven, is enterprise aware, and is scalable\u003c/a\u003e.\" DAD is ideal for digital transformation projects.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"506:T9bb,"])</script><script>self.__next_f.push([1,"\u003cp\u003eOrganizations are pressured to become digital enterprises for good reason. Today every company is a technology company at its core. Only through integrated information technology systems designed for business analytics can companies sense and respond to changing market demands. \u003c/p\u003e \r\n\r\n\u003cp\u003eBusiness agility demands a digital enterprise. \u003c/p\u003e \r\n\r\n\u003cp\u003eDigital technologies have the potential to transform organizations into lean, agile businesses that are strongly focused on their customers. But, just introducing digital technologies into an enterprise is not enough. Without the right organizational culture, business processes, and active people training, you will not realize the full value of digital technologies. \u003c/p\u003e \r\n\r\n\u003cp\u003eIn this program, we help you see the dynamic capabilities of your organization. Using the fitness landscape model we help managers see where they are in relation to other organizations and how to reconfigure their dynamic capabilities to move higher up the competitive fitness landscape. \u003c/p\u003e\r\n\r\n\u003cp\u003eThese “dynamic capabilities” include both the social and the digital - how we work and the technologies that support it.. Terabytes of processing and machine learning won’t be the solution to your business problems on their own. As companies pursue big data and cloud computing strategies to improve their decision making with actionable insights - the technologies will change. To truly sustainably improve the customer experience and achieve the competitive advantage that the power of data promises, you need the social technologies and processes that are able to adapt along with your data strategy. \u003c/p\u003e \r\n\r\n\u003cp\u003eThis ability to reconfigure and deploy new capabilities within the organization by creating, extending, or modifying the existing resources is a “dynamic capability.” With the speed of technology disruption, business leaders must enable their company to adapt. \u003c/p\u003e\r\n\r\n\u003cp\u003eThis certificate will also teach you about how to discover data sources in your company, and leverage those structured and unstructured data to improve your whole business. From supply chain sourcing to marketing and delivery. \u003c/p\u003e\r\n\r\n\u003cp\u003eThese lessons will help you make the right choices about digital transformation to make your organization better fulfill its mission and delight customers. To get there you must first understand the vision of a digital enterprise, what it looks like, how it operates, and why it’s so powerful in delivering results.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"507:T68d,\u003cp\u003e\u003cspan lang=\"en-US\"\u003eWe are in the middle of burgeoning 4th industrial revolution (4IR) characterized by unprecedented scale, scope and complexity and its velocity and depth are still not fully understood. Emergence of new business models (BM) and demise of incumbents, as well as reconfiguration of production, demand, logistics and delivery implicates new paradigms over all industries. Particularly, recent turmoils in global economy due to Pandemic have resulted in vastly accelerated evolution and acceptance of 4IR.\u003c/span\u003e\u003cspan lang=\"en-US\"\u003e\u003c/span\u003e\u003cspan lang=\"en-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIoT, cloud computing, big data analytics, A.I., 3D printing, autonomous electric vehicles and blockchain technologies are changing the business environments which results in many new opportunities and threats. In this environment, it is imperative to understand and respond to impacts of these changes on BM’s for corporate survival and sustainability.\u003cspan lang=\"en-US\"\u003e\u003c/span\u003e\u003cspan lang=\"en-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIn this class, we overview technology enablers and paradigm shifts of 4IR and characterize their impacts on corporate BM. State-of-the-art developments in innovation and entrepreneurship theories pertaining to the topic are curated and introduced, with a focal treatment of common themes such as customer focus, importance of platform and ecosystem strategy, value creation via evolutionary BM development towards Product-Service System (PSS) and role of organizational commitment and consensus. Such insights shall be useful both for entrepreneurs preparing for innovative new businesses and for incumbent enterprises leading evolutionary and revolutionary innovations.\u003c/p\u003e508:T5cc,\u003cp\u003eDigital Agricultural Technologies (DATs) are innovations that enable farmers and agribusiness entrepreneurs to leapfrog to increase their productivity, efficiency, and competitiveness, facilitate access to markets, improve nutritional outcomes and enhance resilience to climate change. These technologies range from mobile apps to digital identities "])</script><script>self.__next_f.push([1,"for farmers to solar applications for agriculture to portable agriculture devices. DATs are increasingly becoming indispensable in the global food and agriculture sector, from fast and convenient information delivery to providing virtual marketplaces. Considering the fact that digital technologies can accelerate agro-food outcomes is juxtaposed with low adoption rates of the same, the World Bank’s operations are increasingly incorporating digital agriculture as a critical element in its operations. Thus, it is important to study digital agriculture technologies in further detail.\u003c/p\u003e\n\u003cp\u003eThis five-week course will provide a high-level overview of DAT concepts, potential impact, range of technologies available, used cases as well as forward-looking technologies. The course will introduce the participants to different agriculture data platforms already available and will encourage them to discover the scope and utility of the open data platforms for analytics and intelligence in agriculture. Participants will be required to engage in discussion forums with their peers and complete quizzes throughout the course.\u003c/p\u003e509:T463,\u003cul\u003e\n\u003cli\u003eDevelop a customer solution using Amazon API Gateway\u003c/li\u003e\n\u003cli\u003eDifferentiate between Amazon SQS and Amazon SNS\u003c/li\u003e\n\u003cli\u003eDescribe Amazon Lambda and when it is used\u003c/li\u003e\n\u003cli\u003eSummarize the various use cases for DynamoDB \u003c/li\u003e\n\u003cli\u003eSolve the customer's use case which needs a data analytics solution in AWS\u003c/li\u003e\n\u003cli\u003eDerive insights using clickstream data\u003c/li\u003e\n\u003cli\u003eRecall the various use cases for using Amazon Kinesis Firehose and Amazon Quicksight\u003c/li\u003e\n\u003cli\u003eRecognize when you would use Amazon Simple Storage Service (Amazon S3)\u003c/li\u003e\n\u003cli\u003eEvaluate ways to migrate container workloads to AWS using a hybrid model\u003c/li\u003e\n\u003cli\u003eDifferentiate between Amazon ECS and Amazon ECS Anywhere\u003c/li\u003e\n\u003cli\u003eLearn different ways the solution could be optimized using container-based workloads\u003c/li\u003e\n\u003cli\u003eEvaluate when to use Amazon Relational Database Services\u003c/li\u003e\n\u003cli\u003eFormulate an account management strategy that follows best "])</script><script>self.__next_f.push([1,"practices on governance and standards\u003c/li\u003e\n\u003cli\u003eDemonstrate when to use AWS Organizations\u003c/li\u003e\n\u003cli\u003eDiscuss AWS IAM Identity Center in great detail\u003c/li\u003e\n\u003cli\u003eRecall when it is important to use AWS CloudTrail\u003c/li\u003e\n\u003c/ul\u003e50a:T62d,\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eWhat is this course about?\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this course, we will look at the advanced technologies that are driving FinTech forward.\u003c/p\u003e\n\u003cp\u003eFinTech is not only a major strategic focus in the banking and finance industry around the world. The fast-moving FinTech technologies also create opportunities and pose challenges to global financial institutions, large tech giants, techfins, retailers and other global companies and the legal and compliance sectors. Besides technology innovations and R\u0026amp;D in FinTech, nurturing FinTech talents and attracting students and professionals to the FinTech arena is very important.\u003c/p\u003e\n\u003cp\u003eIn this MOOC, we will discuss six advanced technologies that drive transformation in the finance and technology industries today from a layman’s perspective. The contents will focus on how these technologies are used in FinTech applications including, artificial intelligence (AI), machine learning (ML), natural language processing (NLP) and big data analytics in daily scenarios such as insurtech, robo-advisory, intelligent transport monitoring, applications in green finance, as well as, blockchain and distributed ledger technology in banking and DeFi. The course will also discuss the technologies and practical impacts of the much talked about Central Bank Digital Currencies (CBDCs) and cyber security. And last but not least, we will learn about the critical roles of RegTech and SupTech in strengthening compliance and enhancing the efficiencies of financial institutions in the FinTech ecosystem.\u003c/p\u003e50b:T687,\u003cp\u003eNow is a great time to launch a rewarding career in Information Technology (IT) support - no experience or degree is required to get started. The US Bureau of Labor Statistics forecasts a 9% growth in jobs through 2030, averaging 7"])</script><script>self.__next_f.push([1,"0,000 openings each year, and a medium salary of $58,000 annually for an entry-level Computer Support Specialist. With over 400,000 US job openings, Computer Tech Support Specialists are in high demand.\u003c/p\u003e \r\n \r\n\u003cp\u003eIn this self-paced, certificate program for beginners, you will learn IT support and build competency in IT fundamentals topics, including hardware, operating systems, software, system administration, programming, databases, networking, cybersecurity, and cloud computing, as well as critical skills covered such as customer service and troubleshooting. Mastery of these skills is essential for IT Helpdesk Support and also provides multiple options to grow your career as they are required skills for many technology jobs, including Software Engineer, Data Analyst, and Data Scientist.\u003c/p\u003e\r\n \r\n\u003cp\u003eThis Professional Certificate program from IBM is built by experts to prepare you for an entry-level job in Technical Support. If you can dedicate a few hours per week, you can complete the program in 3 to 6 months. By the end of the program, you will be equipped with job-ready skills employers look for, whether you are just starting out your IT career or changing jobs.\u003c/p\u003e\r\n\r\n\u003cp\u003eWhen you successfully complete the program you’ll receive dual credentials, IBM Digital Badges for each course to help your profile stand out, as well as a Professional Certificate to showcase your job readiness to potential employers.\u003c/p\u003e50c:T52f,\u003cp\u003eGain the skills to start an exciting new career as a Cloud Technology Consultant with this professional certificate program from Amazon Web Services. Over 9 courses, you'll learn how to understand business goals and translate them into effective cloud computing solutions using AWS services.\u003c/p\u003e\r\n\r\n\u003cp\u003eNo prior experience or degree is required - the training is designed for beginners. Through expert instruction, hands-on projects, and 18 guided labs, you'll master both the technical skills like cloud architecture, automation, data analytics and the crucial soft skills like problem-solving, inte"])</script><script>self.__next_f.push([1,"rpersonal communication and teamwork needed to succeed as a cloud consultant.\u003c/p\u003e\r\n\r\n\u003cp\u003eBy completing the program, you'll earn an employer-recognized certificate and access to career resources like resume reviews, interview prep, and job search support. You'll even get the chance to build a capstone project demonstrating your proficiency with the AWS Well-Architected Framework to showcase to potential employers.\u003c/p\u003e\r\n\r\n\u003cp\u003eWith this professional training, you'll be equipped to provide technical direction to cloud implementation teams and create conceptual designs for secure, scalable cloud solutions aligned with business needs. Open new career opportunities in the high-growth field of cloud technology consulting.\u003c/p\u003e50d:T52e,\u003cp\u003eEmbark on a transformative journey designed for aspiring data engineers, software developers, and data professionals. Through hands-on learning experiences, you'll master data manipulation, pipeline building, and task automation using Python, Rust, Bash, SQL, and cutting-edge tools like Pandas, FastAPI, and Kubernetes.\u003c/p\u003e\r\n\r\n\u003cp\u003eDevelop your proficiency in critical areas such as Linux and Bash scripting, data manipulation with Python and SQL, web application development, containerization, and data pipeline optimization. Gain hands-on experience with industry-standard platforms like Hadoop, Spark, and Snowflake, while leveraging powerful tools like Databricks and MLflow to execute data analytics and manage machine learning workflows. Our renowned faculty and industry connections ensure you receive a world-class education, keeping pace with the latest trends and best practices.\u003c/p\u003e\r\n\r\n\u003cp\u003eBy completing this program, you'll acquire a versatile skill set that empowers you to tackle real-world data challenges, automate workflows, and drive data-driven decision-making. Whether you're seeking to advance your current career or transition into the exciting field of data engineering, this program provides you with the knowledge, tools, and hands-on experience necessary to thrive in today's data-centri"])</script><script>self.__next_f.push([1,"c world.\u003c/p\u003e50e:T7ef,\u003cp\u003eDeveloped by Blockchain at Berkeley and faculty from UC Berkeley's premier Computer Science department, the Blockchain Fundamentals Professional Certificate program is a comprehensive survey of core topics in cryptocurrency, including Bitcoin, and blockchain technology. This program will help you develop the critical skills needed to future-proof your career. \u003c/p\u003e\r\n\r\n\u003cp\u003eThe barrier of entry for the blockchain space can oftentimes seem rather high, especially since the concept of blockchain and the benefits it provides is not yet as widely understood as other innovations. In order to overcome this barrier, this program will explore the main ideas, technologies, and ecosystem surrounding blockchain technology from both technical and non-technical standpoints. This program will help you develop the intuition for thinking of blockchain systems. You will learn the key strengths and motivations of distributed ledger technology, and also be exposed to the underlying mechanisms by which they function. \u003c/p\u003e\r\n\r\n\u003cp\u003eUnderstanding blockchain architecture and the new paradigm of scalable, decentralized applications is imperative for future-proofing your career. Blockchain-related jobs are the second fastest growing in today’s labor market and opportunities are not limited to technical research or development positions; there is a need for project management, community support, law, design, and more.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program is accessible by anyone, with any background. Whether students are planning their next career move as a blockchain developer, crypto trader, data analyst, researcher, or consultant, or are just curious about this field, the Blockchain Fundamentals professional certificate is the best way to get up to speed on blockchain technology. \u003c/p\u003e\r\n\r\n\u003cp\u003eAfter taking Blockchain Fundamentals, students will have a deepened understanding of blockchain, which they can use to formulate their own informed blockchain mental models, hypotheses, and use cases -- imperative for understanding the industry."])</script><script>self.__next_f.push([1,"\u003c/p\u003e50f:T5d1,\u003cp\u003ePower BI is a robust business analytics and visualization tool from Microsoft that helps data professionals bring their data to life and tell more meaningful stores.\u003c/p\u003e\n\u003cp\u003eThis four-week course is a beginner's guide to working with data in Power BI and is perfect for professionals. You'll become confident in working with data, creating data visualizations, and preparing reports and dashboards.\u003c/p\u003e\n\u003cp\u003eTake this course if you are a:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eBeginner level data professional\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eStudent, Researcher, or Academic\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMarketing analyst, business and data analyst, or financial analyst\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCourse Requirement: (a) Windows computer (recommended) or (b) Mac computer with access to a Windows virtual machine or Boot Camp. Link to \u003ca href=\"https://learn.microsoft.com/en-us/power-bi/fundamentals/desktop-get-the-desktop#minimum-requirements\" rel=\"noopener\" target=\"_blank\"\u003eminimum requirements for installing Power BI Desktop\u003c/a\u003e. Email account so you can sign up for the Microsoft 365 Developer Program as part of this course. This can be a personal, work, or school email account.\u003c/p\u003e\n\u003cp\u003eImage Attribution: \u003ca href=\"https://www.behance.net/gallery/68466617/Data-visualization-InfographicUNICEF-reports-Vol-1\"\u003e\"Data visualization \u0026amp; Infographic/UNICEF reports Vol. 1\"\u003c/a\u003e by Shangning Wang, Olga Oleszczuk is licensed under \u003ca href=\"https://creativecommons.org/licenses/by-nc-nd/4.0/?ref=ccsearch\u0026atype=rich\"\u003e CC BY-NC-ND 4.0 \u003c/a\u003e\u003c/p\u003e510:T723,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003ePeople metrics and analytics is the application of statistical techniques into HR data elements using key measures or indicators to examine the performance of the HR function and employees, and to subsequently disclose the value of people’s intangible assets in the integrated reports as part of the 6 Capitals. \u003c/span\u003eIn many organizations, HR departments collect data on the various aspects of people management such as recruitment, selection, employee turnover, training, remuneration and performance managemen"])</script><script>self.__next_f.push([1,"t but hardly integrate statistical techniques with metrics (measures or indicators) to understand the reasons underlying the pattern of relationships between a given set of variables, and in some cases even predict the cause-effect-relationships. \u003cspan lang=\"EN-US\"\u003eConsequently, decisions regarding the attraction, sourcing, deploying, developing, and retaining of talent are based on intuition and not evidence.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis course will provide participants with insights on how to apply people analytics linked to specific metrics and based on an organizational HR measurement system.\u003cspan lang=\"EN-US\"\u003e Hence a more effective approach is to adopt a structured HR measurement system for determining a business need, identifying a human capital problem, organizing data, analyzing and reporting on the patterns of relationships. This is useful for defining specific metrics to track, analyze and information likely to enable managerial decisions. The course provides v\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003ealue-based metrics, analytics and reporting methods such as internal HR dashboards to visualise people-specific data and external human capital disclosure in the corporate annual reports taking into consideration the international reporting standards.\u003c/span\u003e\u003c/p\u003e511:T7bf,\u003cp\u003e\u003cspan lang=\"EN\"\u003eSupply chain management is instrumental and strategically important for an organization’s success. With the advent of digital technologies, cloud, and especially AI and Gen AI, there are novel ways to serve customers, enhance profitability, and save costs by adopting these disruptive technologies. At the same time, innovation in the supply chain process is recommended to harness the power of AI and Gen AI.\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis course \"AI and Gen AI for Supply Chain Transformation\" is designed to provide learners with a comprehensive understanding of the role of AI and Gen AI in transforming supply chain management. The course covers the fundamentals of supply chain management"])</script><script>self.__next_f.push([1,", associated processes, data, analytics, teams, and technologies for managing the supply chain.\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe course will cover topics such as machine learning, deep learning, natural language processing, and computer vision, and how these technologies are used in supply chain management. The course will then delve into the applications of AI and Gen AI in supply chain management, including demand forecasting, supply planning, inventory management, sourcing optimization, and logistics management. Learners will understand how AI and Gen AI can help organizations improve their supply chain efficiency, reduce costs, and enhance customer satisfaction.\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe course will also explore the benefits of using AI and Gen AI in supply chain management, such as improved accuracy, speed, and decision-making. Learners will understand how AI and Gen AI can help organizations gain a competitive advantage and drive business success. The course will also discuss the challenges associated with implementing AI and Gen AI in supply chain management, such as data quality, privacy, and security concerns. The course will feature case studies and real-world examples.\u003c/p\u003e512:T713,\u003cp\u003eIn this course we explore how innovations in digital technology will impact upon the mining and minerals business. We identify the stages along the value chain that present the greatest opportunities for value creation through the adoption of digital technology and optimisation techniques, and investigate how the nature of work and the skills required to operate successfully in the sector will change as a result of the industries digital transformation.\u003c/p\u003e\n\u003cp\u003eThrough this course you will be exposed to various developments in digital technology that are poised to be integrated into the way mines are designed, operated and monitored, as you gain an understanding of the different stages of the value chain that can be digitally optimised, allowing for additional value to be extracted from an or"])</script><script>self.__next_f.push([1,"ebody. We will discuss how automation and remote operations can alter the way that a mine is planned, and explore how industry 4.0 concepts involving sensors, models and feedback/feedforward systems can be integrated to enable precision mining. \u003c/p\u003e\n\u003cp\u003eThe role of data in a digitally transformed mining industry will be discussed, appreciating that data analytics requires a team based multidisciplinary approach, and we will look at how to manage globally dispersed specialist operational teams. You will also be introduced to the use of visualisation as a tool for communication, both within a geographically diverse team environment and to external stakeholders, as an understanding of how the required workforce skillset is likely to change as a result of digitisation of the sector. \u003c/p\u003e\n\u003cp\u003eThis interactive course incorporates videos, expert insights, case studies and discussions to deepen your current understanding of the impact of applications of digital technologies on the mining business.\u003c/p\u003e513:T5fc,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eRewards are a fundamental aspect of human motivation, and the workplace is no exception.\u003c/span\u003e While traditional compensation packages are important, successful organisations are rethinking their rewards strategies to align with the needs of today's workforce.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis course delves deep into the world of rewards and recognition, going beyond the basics of salary and benefits. You'll explore how to design a strategic programme that not only attracts and retains talent but also fuels performance and fosters a positive work environment.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eWhat you’ll learn:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan lang=\"EN-US\"\u003eStrategic rewards\u003c/span\u003e: \u003cspan lang=\"EN-US\"\u003eDesign a strategic rewards \u003c/span\u003eprogramme to motivate and retain top talent.\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"EN-US\"\u003eExploring data:\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e Analyse market data to inform your rewards strategy and stay competitive.\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"EN-US\"\u003eThe right rewards:\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e"])</script><script>self.__next_f.push([1," Make data-driven decisions about rewards and compensation packages.\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"EN-US\"\u003eHelping your team:\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e Support line managers in making fair and consistent reward decisions.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis is a five-week, intermediate, self-paced course and delivered through engaging content, making it easy to fit into your busy schedule. \u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eEnrol today and take your HR career to the next level!\u003c/p\u003e514:T4ac,\u003cp\u003eThis the final capstone course in the Instructional Design and Technology MicroMasters program where we delve into evaluation of course design and develop your final capstone project. Evaluation models allow instructional designers to engage in continuous improvement of course design to optimize the student learning environment. During this course we will explore evaluations models, learning management system’s data analytics, and Kirkpatrick’s Levels of evaluation. Finally, you will design, build and publish your capstone project, which is an online learning module.\u003c/p\u003e\n\u003cp\u003eThis is the final course in the Instructional Design and Technology MicroMasters and content builds on the first three courses in the program. It is highly recommended that learners complete the three pre-requisite courses prior to enrolling in LDT400x.\u003c/p\u003e\n\u003cp\u003eThis course is part of the Instructional Design and Technology MicroMaster’s program from UMGC. Upon completion of the program and receipt of the verified MicroMaster’s certificate, learners may then transition into the full UMGC Master’s Program in Learning Design and Technology. See the MicroMasters program page for more information.\u003c/p\u003e515:T443,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eDigital transformation requires exponential shift in leadership focus and organizational culture to design their business models. This course will help Executives, Business Strategists \u0026amp; Planners and IT Project Teams of Banks \u0026amp; Financial Institutions to understand the components of digital transformation"])</script><script>self.__next_f.push([1," and potential of integration of new technologies, use of data-analytics, the ubiquity of mobile devices, and offering all the services under one hood.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eFor the finance industry to stay relevant for their customers, they will need to re-think and re-work on their existing business models. And the new model must respond to the needs of the trend setting consumers and realize the market’s potential.\u003c/p\u003e\n\u003cp\u003eThis course will also help you to understand some key concepts, to help and guide the consumer’s journey, building omni channel model, collaboration \u0026amp; strategy, use of exponential technology to serve consumer’s aspirations and help you to choose a Strategic Starting Point to update the business processes and procedures.\u003c/p\u003e516:T579,\u003cp\u003eDeveloped by Blockchain at Berkeley and faculty from UC Berkeley's premier Computer Science department, this course provides a wide overview of many of the topics relating to and building upon the foundation of Bitcoin and blockchain technology. \u003c/p\u003e\n\u003cp\u003eThe course covers many key topics in the blockchain space. First, we take a look at distributed systems and alternative consensus mechanisms, as well as cryptoeconomic and proof-of-stake. We then move on to the fundamental applications of bitcoin and blockchain technology, including exploring enterprise blockchain implementations (JP Morgan’s Quorum, Ripple, Tendermint, and HyperLedger), the challenges and solutions around scaling blockchain adoption, and the measures that the government is taking to regulate and control blockchain technology. We wrap up the course by also taking a look at the various blockchain ventures today and conclude with a blockchain-based future thought experiment. \u003c/p\u003e\n\u003cp\u003eThis course is open to anyone with any background. Whether you are planning your next career move as a blockchain developer, crypto trader, data analyst, researcher, or consultant, or are just looking for an introduction to Blockchain.This course will help you beginto develop the critical skills needed to future-pro"])</script><script>self.__next_f.push([1,"of your career. \u003c/p\u003e\n\u003cp\u003eThis is the second course in the Blockchain Fundamentals Professional Certificate program.\u003c/p\u003e517:T589,\u003cul\u003e\n\u003cli\u003eA formal definition of distributed consensus and foundational topics such as the CAP Theorem and the Byzantine Generals Problem.\u003c/li\u003e\n\u003cli\u003eThe alternative consensus mechanisms to Bitcoin’s Proof-of-work, including Proof-of-Stake, voting-based consensus algorithms, and federated consensus.\u003c/li\u003e\n\u003cli\u003eThe meaning and properties of cryptoeconomics as it relates to its two compositional fields: cryptography and economics, as well as the goals for cryptoeconomics with respect to distributed systems fundamentals\u003c/li\u003e\n\u003cli\u003eThe various enterprise-level blockchain implementations, such as JP Morgan’s Quorum, Ripple, Tendermint, and HyperLedger, including the industry use cases for blockchain, ICOs, and the increasing regulations surrounding blockchain.\u003c/li\u003e\n\u003cli\u003eThe challenges with scaling and obstacles to widespread blockchain adoption, as well as the possible solutions within vertical scaling (e.g. blocksize increases, Segregated Witness, and the Lightning Network) and horizontal scaling (e.g. sidechains, sharding).\u003c/li\u003e\n\u003cli\u003eThe measures that governments have taken to regulate and control blockchain technology e.g. Anti-Money Laundering (AML) and Know Your Customer (KYC) regulations, anonymity goals, and government techniques for deanonymization of entities on blockchain.\u003c/li\u003e\n\u003cli\u003eAn exploratory look into blockchain ventures today, such as venture capitalism, ICOs, and crowdfunding.\u003c/li\u003e\n\u003c/ul\u003e518:T5ba,\u003cp\u003eDeveloped by Blockchain at Berkeley and faculty from UC Berkeley's premier Computer Science department, this course presents Bitcoin and cryptocurrencies as the motivation for blockchain technologies, and provides a comprehensive and in-depth overview of the fundamental concepts of the crypto space with a particular emphasis on Bitcoin. \u003c/p\u003e\n\u003cp\u003eThe course covers basic properties of bitcoin, the mechanics behind it (e.g. including cryptographic hash functions, Bitcoin Script, privacy, and"])</script><script>self.__next_f.push([1," hash commitment schemes) and its roots in the Cypherpunk movement and Libertarian ideals. You'll learn about practical applications of Bitcoin such as wallets and mining, as well as how to destroy bitcoins, including network attacks and malicious mining strategies. We will also take a brief look at Ethereum and how blockchain can be used outside of cryptocurrencies. \u003c/p\u003e\n\u003cp\u003eThis course is open to anyone with any background. Whether you are planning your next career move as a blockchain developer, crypto trader, data analyst, researcher, or consultant, or are just looking for an introduction to the Bitcoin technology. This course will help you to begin developing the critical skills needed to future-proof your career. \u003c/p\u003e\n\u003cp\u003eThis course is part of the Blockchain Fundamentals Professional Certificateprogram. If you are planning to enroll in the entire series, we suggest starting with this course and then progressing on to CS198.2x Blockchain Technology.\u003c/p\u003e519:T41f,\u003cul\u003e\n\u003cli\u003eThe basic properties and intent of centralized/decentralized currency and an in-depth understanding of Bitcoin from the ground up, including - Identity, Transactions, Record Keeping, and Consensus.\u003c/li\u003e\n\u003cli\u003eThe roots of Bitcoin in the Cypherpunk movement and Libertarian ideals, and the revolutionary significance of Bitcoin as opposed to some of its early predecessors.\u003c/li\u003e\n\u003cli\u003eThe mechanics behind Bitcoin, such as the Bitcoin network, cryptography and cryptographic hash functions, Bitcoin Script, privacy, and hash commitment schemes.\u003c/li\u003e\n\u003cli\u003eReal-world aspects of Bitcoin, such as wallets, wallet mechanics, mining, transactions, and Bitcoin governance and the various ways one can interface with the Bitcoin network.\u003c/li\u003e\n\u003cli\u003eHow to destroy Bitcoin, including various network attacks.\u003c/li\u003e\n\u003cli\u003eThe properties behind the second largest blockchain platform, Ethereum, including the Ethereum Virtual Machine and the idea of Turing completeness, the key protocol differences between Bitcoin and Ethereum, the use cases of Ethereum.\u003c/li\u003e\n\u003c/ul\u003e51a:T523,\u003cp\u003e\u003c"])</script><script>self.__next_f.push([1,"span lang=\"ES\"\u003eThis course will be composed by four weeks, in each one the student will have the possibility to analyze different perspectives about the rationale of businesses and how they innovate their way to satisfy the expectations of their customers.\u003c/span\u003e\u003cspan lang=\"ES\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan lang=\"ES\"\u003eLearning about the new digital communication trends as Influencer Marketing, to analyze how the advertising is moving from professional studios to the bedroom of an influencer.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"ES\"\u003eUnderstanding about the main role of the digital footprint of a person but also of a brand in the digital ecosystem.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"ES\"\u003eLearning about the SEO Fundamentals and why searching is the key component of any digital strategy.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"ES\"\u003eAnalyzing the impact of SEM Alignment through the usage of different techniques and campaigns.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"ES\"\u003eThinking about the UI/UX point of view, and why is so challenging to accomplish the needs and requirements from a customer.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"ES\"\u003eNavigating through Social Media, and analyze the impact of each of them.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"ES\"\u003eGoing deeper in the data world by data driven analytics and big data escalations from social media.\u003c/span\u003e\u003c/li\u003e\n\u003c/ul\u003e51b:T597,\u003cp\u003eI dati digitali sono essenziali per la società contemporanea. Sempre più numerosi ma difficili da interpretare nella loro complessità. Il Program su Data Visualization guiderà l’apprendimento dell’analisi e delle possibilità di rappresentazione grafica dei dati per una loro comunicazione efficace. Le attività saranno basate sull’utilizzo del software Tableau, strumento di riferimento per la visualizzazione dei dati, tra i più diffusi al mondo. I tre corsi sono organizzati su livelli progressive:\u003c/p\u003e\r\n\r\n\u003cp\u003eIl primo corso è pensato per chiunque lavori con i dati, indipendentemente dal background tecnico. Imparerai a utilizzare il prodotto esplorando con una metodologia “Hands-on” i concetti chiave. E"])</script><script>self.__next_f.push([1,"splorerai diverse tecniche per la creazione di visualizzazioni fino a metterle insieme in una dashboard interattiva.\u003c/p\u003e\r\n\r\n\u003cp\u003eIl secondo corso è pensato per chiunque abbia maturato su Tableau una buona esperienza d’uso e voglia approfondire la propria competenza finalizzata ad un uso più avanzato del tool. Imparerai a personalizzare i layout delle dashboard per rendere più interattiva la navigazione dei dati.\u003c/p\u003e\r\n\r\n\u003cp\u003eIl terzo corso ti consente di imparare a scegliere il giusto grafico rispetto al contesto d’uso. Vengono illustrate le principali soluzioni grafiche a disposizione nonché le modalità per poterle condividere online e renderle fruibili anche a chi non conosce Tableau.\u003c/p\u003e51c:T5b8,\u003cp\u003eSmall tech startups, large corporations, and even government organizations are all beginning to recognize that creating solutions for their clients must go beyond simply producing functional and useable products/services. To best support their populations, designing tech solutions to problems must be more than functional. They must be more than usable. Solutions must be designed to maximize the totality of the experience that a person has with the designed solution.\u003c/p\u003e\n\u003cp\u003eAchieving this goal must begin with a rich understanding of who we are designing for. The better, more complete and holistic understanding of our users as well as anyone that can be directly or indirectly affected by what we design, the more likely we can match the solution with the mental model of our users/stakeholders.\u003c/p\u003e\n\u003cp\u003eIn this course you will learn concepts, tools, and techniques, needed to gather data to better understand who you are designing for so that you can develop information system solutions that will maximize the experiences that users/stakeholders have with those solutions, enabling you to work in a myriad of real-world roles that require designing IT solutions for clients. You will learn concepts like the utility, usability and user experience, why it is important to understand who a system is designed for, and why it is "])</script><script>self.__next_f.push([1,"important to deploy a diversity of techniques to truly get as complete a picture of your users and stakeholders as possible.\u003c/p\u003e51d:T55a,\u003cp\u003eIn this Capstone you’ll demonstrate your ability to perform like a Data Engineer. Your mission is to design, implement, and manage a complete data and analytics platform consisting of relational and non-relational databases, data warehouses, data pipelines, big data processing engines, and Business Intelligence (BI) tools.\u003c/p\u003e\n\u003cp\u003eThis Capstone project will require that you apply and sharpen the skills and knowledge you developed in the various courses in the IBM Data Engineering Professional Certificate and utilize multiple tools and technologies to design databases, collect data from multiple sources, extract, transform and load data into a data warehouse, and utilize a cloud-based BI tool to create analytic reports and visualizations. You will also implement predictive analytics and machine learning models using big data tools and techniques.\u003c/p\u003e\n\u003cp\u003eThis capstone requires significant amount of hands-on lab effort throughout the course. You’ll exhibit your knowledge and proficiency working with Python, Bash scripts, SQL, NoSQL, RDBMSes, ETL, MySQL, PostgreSQL, Db2, MongoDB, Apache Airflow, Apache Spark, and Cognos Analytics.\u003c/p\u003e\n\u003cp\u003eUpon successfully completing this Capstone, you should have the confidence and portfolio to take on real-world data engineering projects and showcase your abilities to perform as an entry-level data engineer.\u003c/p\u003e51e:Tbb4,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis Human Resources (HR) Management Professional Certificate is an exciting developmental opportunity for any working professional who wants to improve their understanding of core Human Resource Management (HRM) practices and apply key competencies for optimal organizational performance.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe course series will expose people management practitioners (from any field or industry) to Strategic HR Management as a fascinating, practice-based and evolving field in the broader discipline of Industrial Psychology. \u003c/p\u003e\r\n\r\n\u003cp\u003eFor those already practicing in the HR field and/or in HR departments, this course will expand your understanding of the strategic management dimension of your role.\u003c/p\u003e\r\n\r\n\u003cp\u003eIt will draw from the \u003ca href=\"https://hybridlearning.sun.ac.za/shrm-2/\"\u003eStrategic Human Resources Postgraduate Qualification\u003c/a\u003e at Stellenbosch University, supplemented with further practitioner-based perspectives on the Strategic HR function. It will equip you with a range of practical people management skills; whether you are an early HR career professional seeking better career prospects, a middle manager seeking to adopt a more strategic process to the HR development planning of your role, or an executive looking to improve your understanding about the crucial dimensions of HR.\u003c/p\u003e\r\n\r\n\u003cp\u003eParticipants will use knowledge acquired from this certificate as a springboard on which to build concrete mechanisms for the organization’s existing People Strategy and nestle HR securely within the organization’s overall strategic goals and plans. Conversely, the organizational goals and strategies may be optimized, or even rewired, by the strategic approach that might become available to you during the course. \u003c/p\u003e\r\n\r\n\u003cp\u003eThis learning experience can help you contribute to the overall resilience of your organization, as you learn how to align your peers’, colleagues’, or management team’s – perspectives about the strategic value of HR management.\u003c/p\u003e\r\n\r\n\u003cp\u003eDecision-makers can expect an improved awareness of how data and analytics can help shape people management strategies, from the global level (e.g., 4IR, AI, The Great Resignation, gig economy, employee benefits, recessions and inflation, post pandemic HR trends) to the local level (e.g., labor relations, employee relations, unions).\u003c/p\u003e\r\n\r\n\u003cp\u003eFurther, for any up-and-coming leader or manager, the course will expose you to new ideas to foster a culture of learning and professional development in your organization, by applying the strategic principles of talent management.\u003c/p\u003e\r\n\r\n\u003cp\u003eProspective participants without opportunity to study full-time but want to stay in tune with strategic considerations of HR, will benefit from this compact, flexible learning offering.\u003c/p\u003e\r\n\r\n\u003cp\u003eShould these professional certificate courses interest you to further your formal studies, do sign up for alerts to other \u003ca href=\"https://hybridlearning.sun.ac.za/shrm-2/\"\u003e(credit-bearing) study opportunities\u003c/a\u003e.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"51f:T401,This series of courses begins with an introduction to the US healthcare delivery system, its many systemic challenges and the prior efforts to develop and deploy informatics tools to help overcome those problems. It goes on to discuss health informatics from an historical perspective, its current state and its likely future state now that electronic health record systems are widely deployed, the HL7 FHIR (Fast Healthcare Interoperability Resources) standard is being rapidly accepted as the means to access and share the data stored in those systems and analytics is increasing being used to support clinical research using that aggregated data. It then turns to the impact of FHIR in transforming healthcare with a focus on some of the important and evolving areas of informatics including health information exchange, population health, public health, mHealth and big data and analytics. Use cases and case studies are used in all of these discussions to help students connect the technologies to real world challenges.520:T551,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eIBM Db2 on Cloud is a cloud database service created by IBM, used to manage transactional, operational, and analytical data. The database engine is fast, flexible, and secure and can be used for a variety of purposes, from real-time analytics and building cloud-native apps to machine learning and AI. As a powerful, scalable tool that can reduce costs and reporting time. Database skills, including Db2 on Cloud skills, are highly valued by employers.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIdeal for data administrators and data scientists interested in learning the fundamentals of Db2, you’ll learn to use simple SQL script files to create tables and a table structure within IBM Db2 on Cloud. You will also learn to load data using a CSV and an SQL file. Completing this project can provide you with practical Db2 experience that employers value.\u003c/p\u003e\n\u003cp\u003eAs part of this hands-on project, you will be \u003cspan lang=\"EN-US\"\u003eprovided with a browser-accessible \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003edevelopment environme"])</script><script>self.__next_f.push([1,"nt that already has \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003emany technologies and libraries preinstalled, saving you the time and hassle of \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003esetting everything up. Also, note that this \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003eplatform works best with current versions of \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003eChrome, Edge, Firefox, Internet Explorer, or Safari.\u003c/span\u003e\u003c/p\u003e521:T585,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eIBM Db2 on Cloud is a cloud database service created by IBM, used to manage transactional, operational, and analytical data. The database engine is fast, flexible, and secure and can be used for a variety of purposes, from real-time analytics and building cloud-native apps to machine learning and AI. As a powerful, scalable tool that can reduce costs and reporting time. Database skills, including Db2 on Cloud skills, are highly valued by employers.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIdeal for data administrators and data scientists interested in learning the fundamentals of Db2, you’ll learn to use simple SQL script files to create tables and a table structure within IBM Db2 on Cloud. You will also learn to load data using a CSV and an SQL file. Completing this project can provide you with practical Db2 experience that employers value.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eAs part of this hands-on project, you will be \u003cspan lang=\"EN-US\"\u003eprovided with a browser-accessible \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003edevelopment environment that already has \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003emany technologies and libraries preinstalled, saving you the time and hassle of \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003esetting everything up. Also, note that this \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003eplatform works best with current versions of \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003eChrome, Edge, Firefox, Internet Explorer, or Safari.\u003c/span\u003e\u003c/p\u003e522:Ta0c,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cstrong\u003eThe Hiring Process:\u003c/strong\u003e You'll learn how you can help your organization succeed by determining staffing needs, recruiting qualified candidates, conducting effective interviews, and selecting the best candidate for the role. You'll be guided through the hiring process from beginning to end—from developing a hiring plan through the onboarding process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterviewing:\u003c/strong\u003e You'll learn how to construct a fair and effective interview process that will lead you to a quality hire. You'll also get information on different types of interviews, practice in designing effective interview questions, and tips for how to respond to unexpected situations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOnboarding:\u003c/strong\u003e You'll learn what makes an onboarding plan successful, and you'll learn how orientation, onboarding, and training come together to promote long-term success for new employees.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTraining and Development:\u003c/strong\u003e You'll learn how HR professionals identify training needs and how different types of training can be used to support employees. You'll also see how career development helps individuals reach their full potential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpreting Data:\u003c/strong\u003e You'll learn what happens after analyzing the data, including methods for extracting insights from different types of data. You will also learn tips on how to use data to develop a strategic plan and present your findings to relevant stakeholders. Through practice, you will develop your data interpretation skills and become a more effective HR professional.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffective Meetings:\u003c/strong\u003e You will learn how to create an agenda, take minutes, and use different types of materials. You will also learn how to address some of the common challenges associated with meetings. Finally, you'll get some tips for hosting virtual meetings and consider some key elements of policies related to meetings in the workplace.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExit Interviews:\u003c/strong\u003e You will explore the purpose and goals of an effective exit interview process, the merits of various interview formats and styles, what to ask (and what not to), and how to advocate for the proper use of exit interview feedback within your organization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHR Federal Law:\u003c/strong\u003e You'll learn about some important federal employment laws and examine how those laws are implemented in the workplace. You'll also learn about key resources for learning about relevant federal laws, and you'll practice employing your research skills to examine federal laws relevant to your organization.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"523:Tbb0,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eThe age of machine learning has arrived! Arm technology is powering a new generation of connected devices with sophisticated sensors that can collect a vast range of environmental, spatial and audio/visual data. Typically this data is processed in the cloud using advanced machine learning tools that are enabling new applications reshaping\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e the way we work, travel, live and play. \u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eTo improve efficiency and performance, developers are now looking to analyse this data directly on the source device – usually a microcontroller (we call this ‘the Edge\u003cspan lang=\"EN-US\"\u003e’). But with this approach comes the \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003echallenge of implementing machine learning on devices that have constrained computing resources.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis is where our course can help!\u003c/p\u003e\n\u003cp\u003eBy enrolling in \u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eMachine Learning at th\u003c/span\u003e\u003c/strong\u003e \u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003ee Edge on Arm: A Practical Introduction\u003c/span\u003e\u003c/strong\u003e\u003cspan lang=\"EN-US\"\u003e you’ll learn how to train machine learning models and implement them on industry relevant Arm-based microcontrollers.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eWe’ll start your learning journey by taking you through the basics of \u003cstrong\u003eartificial intelligence\u003c/strong\u003e , \u003cstrong\u003emachine learning\u003c/strong\u003e and \u003cstrong\u003emachine learning at the edge\u003c/strong\u003e , and illustrate why businesses now need this technology to be available on connected devices. We’ll then introduce you to the concept of datasets and how to train algorithms using tools like Anaconda and Python. We'll then go on to explore advanced topics in machine learning such as artificial neural networks and \u003cstrong\u003ecomputer vision\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eAlong the way, our practical lab exercises will show you how you can address real-world design problems in deploying machine learning applications, such as speech and pattern recognition, as well as image processing, using actual sensor data obtained from the microcontroller. We'll also introduce you to the open source TensorFlow Python library, which is useful in the training and inference of deep neural networks.\u003c/p\u003e\n\u003cp\u003eIn the final module you’ll be able to apply what you’ve learned by implementing machine learning algorithms on a dataset of your choice.\u003c/p\u003e\n\u003cp\u003eThe ST DISCO-L475E board used in this course can be purchased directly from our technology partner STMicroelectronics: \u003cspan lang=\"EN-US\"\u003e\u003ca href=\"https://www.st.com/content/st_com/en/campaigns/educationalplatforms/iot-arm-edx-edu.html\"\u003ehttps://www.st.com/content/st_com/en/campaigns/educationalplatforms/iot-arm-edx-edu.html\u003c/a\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThrough our vast ecosystem, Arm already powers a wide range of devices and applications that rely on \u003cstrong\u003emachine\u003c/strong\u003e \u003cstrong\u003elearning at the edge\u003c/strong\u003e\u003cspan lang=\"EN-US\"\u003e. Be a part of this vibrant community of developers and start your machine learning \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003ejourney by enrolling in our course today! \u003c/span\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"524:Ta3b,"])</script><script>self.__next_f.push([1,"\u003cp dir=\"ltr\"\u003eClimate change, biodiversity loss and pan-syndemics are some of today’s most pressing complex challenges. Much of our economies and societies are exhaustive, vulnerable, and unfair. We need to actively restore and regenerate ecosystems and their services while transforming our economies to become more circular and just. We require new knowledge systems and cultures leading to transformative action as the human impact on earth needs to be fundamentally redesigned.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\n\nScientific knowledge and reasoning are the fundamental tools to guide policy and management decisions, especially in times of crises. But the limitations of reductionist science are evident due to the lack of widespread action in addressing today's highly complex challenges, which are self-emergent, unpredictable, span across nested scales, depend on societal behavioral transitions, and lack data.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\n\nDesign disciplines offer creative ways of prototyping solutions in an iterative way. Design responds to a current problem by proposing future pathways, through a feedback exchange from praxis. Designerly praxis can benefit from science, for example by directing interventions and leveraging relationships based on quantitative data. Neither the analytical and descriptive tools of science, nor the iterative process of design alone are adequate for addressing complex challenges. Combining both cultures and methods of reasoning as a fluid, intervention-based and synergistic process is beneficial for fostering the regenerative, transformative action that is urgently required.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\n\nTherefore, this MOOC series entitled “Designing Resilient Regenerative Systems” (DRRS) offers four consecutive MOOCs that aim to address these urgent and complex challenges. Participants are invited on a learning journey that includes emphasis on extending our worldviews, concepts like regeneration and resilience, befriending complexity and uncertainty, methods and hybrid practices of science and design, meta-design and the seeing of patterns and root causes, connecting more with our inner self, and becoming bio-regional weavers within communities of transformational learning and praxis.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\n\nThis first MOOC places global crises in context with local and regional examples for planetary health. Participants build consciousness by questioning the dominant reductionist worldviews that drive our global societies and learn ways of rethinking our relationship with nature as a holistic approach of “interbeing,” which places humans as part of the broader web of life.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"525:T980,"])</script><script>self.__next_f.push([1,"\u003cp dir=\"ltr\"\u003eThis MOOC series is about creating positive impact in complex systems. It is about navigating complexity and uncertainty with new tools and practices, such as “organic emergence”: Complex systems are inherently dynamic and unpredictable: their properties are emergent. An organic way to engage with emergence is to trust in having the right tools and techniques to adaptively cope with sudden surprises or challenges, and to reveal hidden opportunities. In this first MOOC “Regenerative Systems: Sustainability to Regeneration” you train your consciousness about dominant and alternative worldviews. You learn the roots of sustainability and pathways to regeneration. You acquire tools to reframe complexity and befriend uncertainty. You learn to reconnect with nature and to design as nature. You gain awareness through practices of physical and mental activation via self-compassion techniques and flow experiences in nature.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\n\nExciting real-world illustrations take you to Hemsedal Norway, Annecy France, Ostana Italy, and Mallorca Spain. This offers a comparative understanding of communities and regions undergoing sustainability transitions across different contexts, cultures, climates and geographies.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\n\nThe prominent content you will learn in this first MOOC are an update on the current state of sustainability science, different angles to understand regeneration, methods such as systemic design and systems-oriented design, resilience assessment, circularity mapping, visual dialogue, cross-scale design, “view from above” perspectives, biomimicry, transdisciplinary research, real-world elaboration, metadesign, and more.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\n\nThe MOOCs’ didactics are designed to combine time and place independent virtual learning through pre-recorded conversations, both accessible as movies and audio files, readings, and practical engagement in nature. Virtual content stimulates physical and social interaction in the bio-region of the participants. An accompanying visual mapping process called Gigamapping acts as a designerly way to co-create your own learning journey and connect across the MOOC series to your final transformative design project. Your personal QUEST guides you through your learning journey. Our DRRS virtual network allows you to exchange with other MOOC participants and to find learning partners in your region where you live.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"526:T72d,\u003cp\u003eOrganizations need skilled, forward-thinking Big Data practitioners who can apply their business and technical skills to unstructured data such as tweets, posts, pictures, audio files, videos, sensor data, and satellite imagery, and more, to identify behaviors and preferences of prospects, clients, competitors, and others. ****\u003c/p\u003e\n\u003cp\u003eThis course introduces you to Big Data concepts and practices. You will understand the characteristics, features, benefits, limitations of Big Data and explore some of the Big Data processing tools. You'll explore how Hadoop, Hive, and Spark can help organizations overcome Big Data challenges and reap the rewards of its acquisition. \u003c/p\u003e\n\u003cp\u003eHadoop, an open-source framework, enables distributed processing of large data sets across clusters of computers using simple programming models. Each computer, or node, offers local computation and storage, allowing datasets to be processed faster and more efficiently. Hive, a data warehouse software, provides an SQL-like interface to efficiently query and manipulate large data sets in various databases and file systems that integrate with Hadoop.\u003c/p\u003e\n\u003cp\u003eOpen-source Apache Spark is a processing engine built around speed, ease of use, and analytics that provides users with newer ways to store and use big data.\u003c/p\u003e\n\u003cp\u003eYou will discover how to leverage Spark to deliver reliable insights. The course provides an overview of the platform, going into the different components that make up Apache Spark. In this course, you will also learn how Resilient Distributed Datasets, known as RDDs, enable parallel processing across the nodes of a Spark cluster.\u003c/p\u003e\n\u003cp\u003eYou'll gain practical skills when you learn how to analyze data in Spark using PySpark and Spark SQL and how to create a streaming analytics application using Spark Streaming, and more.\u003c/p\u003e527:T50e,\u003cp\u003eThis course from the Stanford NGSS Assessment Project (SNAP) is designed to guide participants in exploring the role performance assessment can play in helping their students meet the goals "])</script><script>self.__next_f.push([1,"of the Next Generation Science Standards. Participants will learn SNAP's strategies for analyzing what an assessment is evaluating, analyzing multidimensional student data, and making instructional decisions based on evidence of students' progress and additional needs. Participants will have opportunities throughout the course to practice using these strategies with sample short performance assessments (20-minute tasks) and student data.\u003c/p\u003e\n\u003cp\u003eThis course follows a hybrid format designed to support groups of colleagues learning together. Instruction is delivered through four video sessions and the assignments for those sessions are done in person (face-to-face) with groups of colleagues. One member of the group should be designated a facilitator. Because this format is so unusual, we have created introductory videos to help you organize your groups and prepare for the face-to-face sessions. Note: SNAP is not present in these meetings -- we provide facilitation guides and all of the materials you will need for the assignments, but these meetings are run by you and your colleagues.\u003c/p\u003e528:T50f,\u003cp\u003eThis course from the Stanford NGSS Assessment Project (SNAP) is designed to guide participants in exploring the role performance assessment can play in helping their students meet the goals of the Next Generation Science Standards. Participants will learn SNAP's strategies for analyzing what an assessment is evaluating, analyzing multidimensional student data, and making instructional decisions based on evidence of students' progress and additional needs. Participants will have opportunities throughout the course to practice using these strategies with sample short performance assessments (20 minute tasks) and student data. \u003c/p\u003e\n\u003cp\u003eThis course follows a hybrid format designed to support groups of colleagues learning together. Instruction is delivered through four video sessions and the assignments for those sessions are done in-person (face-to-face) with groups of colleagues. One member of the group should be designated a"])</script><script>self.__next_f.push([1," facilitator. Because this format is so unusual, we have created introductory videos to help you organize your groups and prepare for the face-to-face sessions. Note: SNAP is not present in these meetings -- we provide facilitation guides and all of the materials you will need for the assignments, but these meetings are run by you and your colleagues.\u003c/p\u003e529:T792,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eUnlock the power of data and transform your HR practice with our dynamic course, Principles of Analytics. \u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eDive into the pivotal role that data plays in today's workplace and discover innovative ways to harness its potential for strategic HR decisions.\u003c/p\u003e\n\u003cp\u003eDesigned for intermediate-level HR professionals eager to elevate their skills, this self-paced, four-week course offers a comprehensive exploration of data-driven HR practices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eWhat\u003c/span\u003e you’ll learn:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eThe\u003c/span\u003e role of data in HR:\u003c/strong\u003e\u003cspan lang=\"EN-US\"\u003e Understand the critical importance of data in the modern workplace and how it drives decision-making and strategic planning.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eHarnessing\u003c/span\u003e data for HR:\u003c/strong\u003e\u003cspan lang=\"EN-US\"\u003e Learn the various methods and tools HR professionals can use to collect, \u003c/span\u003eanalyse and interpret data to enhance organisational outcomes.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eData's\u003c/span\u003e influence on organisations:\u003c/strong\u003e\u003cspan lang=\"EN-US\"\u003e Explore how data integrates into \u003c/span\u003edifferent parts of the organisation, from improving employee experience to optimising customer service and stakeholder engagement.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eEvidence-\u003c/span\u003e based practice:\u003c/strong\u003e\u003cspan lang=\"EN-US\"\u003e Discover how to apply evidence-based practices to inform \u003c/span\u003eorganisational measures and outcomes, ensuring decisions are grounded in reliable data.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eCreating\u003c/span\u003e value:\u003c/strong\u003e\u003cspan lang=\"EN-US\"\u003e Gain insights into how data-driven strategies can creat"])</script><script>self.__next_f.push([1,"e value for employees, customers, and other stakeholders, enhancing overall \u003c/span\u003eorganisational performance.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eJoin us for this expertly designed course by HR professionals and take a decisive step towards becoming a data-savvy HR leader.\u003c/p\u003e52a:T4bf,\u003cp\u003eDive deep into the transformative world of Data Visualization with our detailed course, perfect for professionals and enthusiasts eager to leverage data in decision-making processes. 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Para el desarrollo de estos laboratorios se utilizará software libre, por ejemplo el monitor de tráfico Wireshark que permite revisar la estructura de los paquetes en una red. Por otro lado también se utilizará software de simulación de redes de uso gratuito, por ejemplo el simulador Packet Tracer del proveedor de telecomunicaciones CISCO.\u003cspan lang=\"ES\"\u003e\u003c/span\u003e\u003cspan lang=\"ES\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eAprende a través de una metodología de enseñanza dinámica ofrecida desde la Escuela de Ingeniería, Ciencia y Tecnología de la Universidad del Rosario, la cual explica los conceptos teóricos de una manera precisa y posteriormente los pone en práctica a través de los laboratorios, sin necesidad de implementar infraestructura compleja o costosa. El estudiante también puede optar por presentar test al final de cada módulo los cuales le permitirán validar y afianzar el conocimiento adquirido.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"52e:T79c,\u003cp\u003eThis online course in behavioral neuroscience focuses on research related to mental health conditions such as anxiety and depression and was developed by University of Alaska Fairbanks faculty member Dr. Abel Bult-Ito. Enrollment in this course gives you access to content that will help you:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eObtain competency in research methods using behavioral tests to observe animal behavior and measure anxiety and depression in laboratory mice.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCollect behavioral data from mouse videos from compulsive-like, non-compulsive-like, and randomly bred mouse strains; a mouse model of OCD.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEstablish a foundation in using behavioral tests in laboratory mice to be able to confidently learn how to use new tests.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDevelop an ability to analyze behavioral data and be proficient in the visualization of these data.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDevelop an ability to interpret and discuss results in the context of human behavior, and especially psychiatric disorders, and the mouse model of OCD.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eObtain a competency in describing key characteristics of anxiety and depression in humans and in animal models.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDevelop an ability to compare and contrast compulsive-like, anxiety-like, and depression-like behaviors in mice to equivalent conditions in humans.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDevelop a capability to formulate original research hypotheses.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eObtain a competency in describing and discussing how basic research, as performed in this certificate program, contributes to the animal model of OCD and how it may have the potential to contribute to improving healthcare, the human condition and the biological basis of human behavior. This certificate program also will enhance your personal development and your professional development.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eLearners who join this course should be free of objections to using mice in research.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e52f:T5c0,\u003cp\u003eThis course is part of the Certified Lifestyle Medicine Executive MicroMasters "])</script><script>self.__next_f.push([1,"program which consists of 9 courses and a capstone exam. After completing the program, you can also apply to Doane University to complete your MBA online for approximately $10,500 (learn more about the program \u003ca href=\"https://www.doane.edu/lp/certified-lifestyle-medicine-executive.html?r_source=Web%20-%20CLME\" title=\"Doane University MBA\"\u003ehere\u003c/a\u003e). \u003c/p\u003e\n\u003cp\u003eHealth informatics technology (HIT) is the field of study that focuses on acquiring, storing, and retrieving healthcare data. In order to address the challenges of safety, quality, effectiveness, and efficiency in healthcare systems for population health, HIT is essential. Electronic healthcare records and data are dynamic and at a population level, HIT enables the use of aggregate data to refine and enhance our understanding of what interventions are most clinically and cost effective for subsets of patients within a population. \u003c/p\u003e\n\u003cp\u003eWhile most health informatics texts take a hospital-centric approach, this course focuses on how to operationalize informatics solutions to address important public health challenges impacting individuals, families, communities, and the environment in which they live. \u003c/p\u003e\n\u003cp\u003eThis course is part of the Certified Lifestyle Medicine Executive MicroMasters program. For an introduction to lifestyle medicine, see the Lifestyle Medicine Competencies Professional Certificate program.\u003c/p\u003e530:T4fd,\u003cp\u003eHealth Informatics Technology (HIT) deals with the acquisition, storage and retrieval of data and is central in addressing the challenges of safety, quality, effectiveness and efficiency in a healthcare system. Proper management of dynamic data, such as electronic healthcare records, can enable a new approach for clinical workflow, evidence-based decision making and team-based care to improve overall public health. Gain an understanding of how healthcare informatics is deployed in diverse healthcare systems around the world.\u003c/p\u003e\n\u003cp\u003eIn addition, leaders and administrators in the healthcare industry must have a basic understanding of health"])</script><script>self.__next_f.push([1,"care finance, risk, legal and regulatory issues in order to navigate and change the system. Managing risk is one of the primary responsibilities of a leader. This requires a basic understanding of the financial health and regulatory constraints one operates within to anticipate and address changing dynamics.\u003c/p\u003e\n\u003cp\u003eThis course will focus on a combination of technology management and and finance to help you articulate innovative approaches to managing costs and improving access, quality and safety. In the end, you will learn how to assess the fiscal status of a healthcare organization through sophisticated technology.\u003c/p\u003e531:T56b,\u003cp\u003eViviamo nel \"big data age\", qualunque attività umana, qualsiasi azione, produce dati.\u003cbr /\u003e\nQuesto corso affronta le diverse fasi dell’indagine statistica, dal campionamento statistico all’utilizzo degli strumenti inferenziali per ottenere stime, puntuali o intervallari, e per verificare la plausibilità di ipotesi su una o più caratteristiche della popolazione oggetto di studio.\u003cbr /\u003e\nIn altre parole, lo scopo formativo del corso è di condurre lo studente verso una matura capacità di sintesi e di astrazione nella formalizzazione e nell’analisi dei problemi economico-gestionali attraverso l’impiego degli strumenti di statistica inferenziale ritenuti più adeguati agli obiettivi di analisi e alle caratteristiche dei dati raccolti.\u003c/p\u003e\n\u003cp\u003eToday, we live in a big data age where all human activities and actions produce \u003cbr /\u003e\ndata. In this course, we will trace the steps of statistical surveys: from statistical \u003cbr /\u003e\nsampling, to the inferential tools used for interval and point estimates and for verifying the plausibility of hypotheses on one or more characteristics of the population studied. \u003cbr /\u003e\nIn other words, the scope of this course is to provide the students with a sensible synthetic ability as well as with a mature competence of abstraction when formalizing and analyzing the economic and managerial problems with the most appropriate tools.\u003c/p\u003e532:Ta0b,"])</script><script>self.__next_f.push([1,"\u003cp\u003eQuesto corso si pone l’obiettivo di formare professionisti della Business Intelligence attraverso l’apprendimento della piattaforma Tableau, strumento leader di questo settore.\u003c/p\u003e\n\u003cp\u003eLa mission di Tableau è quella di aiutare le persone a vedere e capire i propri dati. Oggi le aziende, così come gli enti della Pubblica Amministrazione, si ritrovano ad avere moli di dati sempre più grandi, ma spesso non sono in grado di estrarre informazioni utili da questi dati. L’obiettivo che ogni analista si pone è quello di analizzare i dati a disposizione per estrarne informazioni e generare, a partire da questi, nuova conoscenza.\u003c/p\u003e\n\u003cp\u003eNel processo analitico, un ruolo cruciale lo riveste la data visualization. A differenza dell’opinione comune, la visualizzazione dei dati non riguarda solo la scelta delle proprietà grafiche da assegnare ai dati, ma è il tassello del processo analitico che permette di capire come rappresentare al meglio i propri dati per ricavarne informazioni utili per basare le proprie decisioni su strategie data-driven.\u003c/p\u003e\n\u003cp\u003eDate queste premesse, Tableau è lo strumento giusto per far emergere la conoscenza nascosta dai dati in modo facile e veloce. La suite Tableau comprende numerosi prodotti che permettono agli analisti di preparare, analizzare e condividere i propri dati.\u003c/p\u003e\n\u003cp\u003eNelle lezioni di questo corso verranno presentate tutte le funzionalità di Tableau Desktop, il tool dedicato alla costruzione delle analisi.\u003c/p\u003e\n\u003cp\u003eIl primo capitolo presenta tutti gli elementi preliminari alla conoscenza dello strumento. Vengono introdotti i diversi prodotti della suite Tableau, l’interfaccia grafica e gli ambienti di lavoro, per passare, infine, alla presentazione delle unità minime del lavoro su Tableau: dimensioni, misure e tipi di dato.\u003c/p\u003e\n\u003cp\u003eNel secondo capitolo troviamo i contenuti relativi ai modi e ai tipi di connessione ai dati. Tableau supporta numerose connessioni, sia a file in locale sia a server, e consente di salvare il lavoro fatto in formati diversi.\u003c/p\u003e\n\u003cp\u003eIl terzo capitolo è dedicato alle strategie di selezione e organizzazione dell’informazione. Per rendere l’analisi più efficace e garantire una migliore comprensione dei dati, Tableau mette a disposizione numerose opzioni per filtrare, ordinare e raggruppare i dati.\u003c/p\u003e\n\u003cp\u003eIl quarto capitolo contiene tutti gli elementi necessari per imparare a lavorare con set, date e misure multiple. Questa sezione presenta contenuti diversi orientati all’apprendimento di modalità di combinazione dei dati nell’ambiente di lavoro.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"533:T771,\u003cp\u003eI contenuti di questo corso sono pensati per permettere agli utilizzatori di Tableau di migliorare le proprie capacità sull’uso del tool, a un livello intermedio. Nei moduli precedenti, abbiamo introdotto i concetti fondamentali per lavorare con Tableau e analizzare i propri dati. Una volta maturate queste conoscenze, siamo pronti per proseguire nel percorso di apprendimento di Tableau, per portare la qualità e la complessità delle nostre analisi a un livello superiore. Questo corso intermedio introduce quattro nuovi moduli, suddivisi come descritto di seguito.\u003c/p\u003e\n\u003cp\u003eIl modulo 1 è dedicato al tema della combinazione dei dati. Spesso i dati provengono da fonti diverse; dobbiamo quindi essere in grado di stabilire tra questi delle relazioni che ci permettano di includere in un’analisi tutte le informazioni di cui abbiamo bisogno. Vedremo quindi come costruire queste relazioni su Tableau, ricorrendo a Join, Union e Data Blending.\u003c/p\u003e\n\u003cp\u003eNel modulo 2 lavoreremo con i campi calcolati e i parametri. I primi sono nuovi campi (dimensioni e misure) che, a partire dalla logica utilizzata, aggiungono nuovi dati al data base di partenza. I secondi, invece, sono valori dinamici che possono essere impiegati in tantissimi modi per personalizzare i propri calcoli e la loro visualizzazione all’interno dello spazio di lavoro.\u003c/p\u003e\n\u003cp\u003eIl modulo 3 contiene tutte le conoscenze utili per svolgere operazioni al livello della vista. Vedremo in questo modulo come Tableau esegue le operazioni di calcolo secondo un ordine preciso, che influisce sui risultati dei calcoli che facciamo.\u003c/p\u003e\n\u003cp\u003eInfine, nel modulo 4 andremo a lavorare con i dati geografici. Nel processo analitico, sono diverse le domande che possiamo porci. Padroneggiando le conoscenze sulla manipolazione dei dati geografici possiamo rispondere al “dove”, un aspetto fondamentale da considerare nel processo analitico.\u003c/p\u003e534:T8a3,"])</script><script>self.__next_f.push([1,"\u003cp\u003eAs part of the Principles of Manufacturing MicroMasters program, this course focuses on decision making for system design, as it arises in manufacturing systems and supply chains.\u003c/p\u003e\n\u003cp\u003eYou will learn about frameworks and models for structuring key system design issues and trade-offs that arise in today’s supply chains and manufacturing systems.\u003c/p\u003e\n\u003cp\u003eThe course will also cover various models, methods and software tools for decision support for:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eLogistics network design\u003c/li\u003e\n\u003cli\u003eCapacity planning and flexibility\u003c/li\u003e\n\u003cli\u003eMake-buy\u003c/li\u003e\n\u003cli\u003eSupply chain contracting\u003c/li\u003e\n\u003cli\u003eSupply chain risk mitigation\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou will learn through industry applications and cases to illustrate concepts and challenges.This course should be taken in sequence following Supply Chains and Manufacturing Systems: Planning.\u003c/p\u003e\n\u003cp\u003eDevelop the engineering and management skills needed for competence and competitiveness in today’s manufacturing industry with the Principles of Manufacturing MicroMasters Credential, designed and delivered by MIT’s #1-ranked Mechanical Engineering department in the world. Learners who pass the 8 courses in the program will earn the MicroMasters Credential and qualify to apply to gain credit towards MIT’s Master of Engineering in Advanced Manufacturing \u0026amp; Design program.\u003c/p\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003cp\u003ePlease note: edX Inc. has recently entered into an \u003ca href=\"https://news.mit.edu/2021/mit-harvard-transfer-edx-2u-0629\"\u003eagreement to transfer the edX platform to 2U, Inc\u003c/a\u003e., which will continue to run the platform thereafter. The sale will not affect your course enrollment, course fees or change your course experience for this offering. It is possible that the closing of the sale and the transfer of the edX platform may be effectuated sometime in the Fall while this course is running. Please be aware that there could be changes to the edX platform Privacy Policy or Terms of Service after the closing of the sale. However, 2U has committed to preserving robust privacy of individual data for all learners who use the platform. For more information see the \u003ca href=\"https://support.edx.org/hc/en-us/articles/4403415754007-edX-and-2U\"\u003eedX Help Center\u003c/a\u003e.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"535:T433,\u003cp\u003eThe blockchain market has changed how organisations share and transact data, and individuals perceive data ownership and transparency towards suppliers. New corporate ecosystems and collaborations push innovators to experiment with new business models. At the same time, regulators define boundaries and frameworks for applying this new technology.\u003c/p\u003e\n\u003cp\u003eHowever, the industry lacks professionals with well-founded knowledge and experience to apply blockchain technology in the right context. The Chartered Blockchain Analyst - CBA®️ addresses this gap with a holistic certification programme developed with leading academic and industry players.\u003c/p\u003e\n\u003cp\u003eThe CBA®️ has been designed for for professionals with backgrounds in enterprise, healthcare, public services, utilities and supply chain, in all functions and at all levels of responsibility. The CBA® Level 1 Preparatory Course is also accredited by \u003ca href=\"https://cpduk.co.uk/courses/dec-institute-cba13x-preparatory-course-for-cbaa-1\" rel=\"noopener\" target=\"_blank\"\u003eThe CPD Certification Services\u003c/a\u003e.\u003c/p\u003e536:T82a,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThe CBA®️ Level 1 MOOC is a preparatory course which comprises three macro-modules, aligned with all other preparatory materials offered by DEC Institute (exam syllabus, study booklet, practice questions and tests). These 3 macro-modules consist of the following modules:\u003c/p\u003e\n\u003cp\u003eTECHNOLOGY:\u003c/p\u003e\n\u003cp\u003eIn this section, we explore 18 topics, including Blockchain History, Blockchain Principles, Databases, Ledgers \u0026amp; DLTs, Cryptography, Consensus Mechanisms, Blockchain Architectures, Blockchain Technology Stack, Smart Contracts, Cryptocurrencies, Tokens, Blockchain Protocols, Wallets, Privacy, Scalability, Interoperability, Decentralized Applications, Decentralized Autonomous Organization, and Oracles.\u003c/p\u003e\n\u003cp\u003eBUSINESS \u0026amp; ECONOMICS:\u003c/p\u003e\n\u003cp\u003eIn this section, we explore 14 topics, including Blockchain and the Internet, Innovation, Disruption \u0026amp; Adoption Principles, Blockchain Ecosystems, Blockchain Economics, Tokenisation in Business, Decentralized Applications in Business, Blockchain Governance in Business and DAOs, Blockchain and Sustainability, Application Areas in Traditional Financial Services, Decentralised Finance (DeFi), Application Areas in Non-Financial Service Sectors, Application Areas of Non-Fungible-Tokens (NFTs), Supply Chain and Self Sovereign Identity (SSI).\u003c/p\u003e\n\u003cp\u003eLEGAL \u0026amp; REGULATORY:\u003c/p\u003e\n\u003cp\u003eIn this section, we explore 7 topics, including Legal and Regulatory Treatment of Cryptoassets, Legal \u0026amp; Regulatory Standing of Blockchain Technology, Legal Status of Smart Contracts, Challenges of Regulating DLT, Competition Law and Antitrust, Blockchain and Regtech and GDPR \u0026amp; Other Data Regulations.\u003c/p\u003e\n\u003cp\u003eAfter completing this MOOC, candidates can register for the CBA®️ Level 1 examination at PearsonVUE through the website of \u003ca href=\"https://www.decinstitute.org/\" rel=\"noopener\" target=\"_blank\"\u003eDEC Institute\u003c/a\u003e, where they can download and study additional preparatory material (mandatory reading: Study Booklet CBA®️ Level 1), which will be required to successfully pass the examination and obtain the CBA®️ I designation.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"537:T449,\u003cp\u003eHow do airlines decide when to increase ticket prices? Should a hotel charge less per night for a long stay than a short one? Why do some software companies bundle very different products together? How should a fashion retailer decide when do start discounting clothes? Why do so many discounted rates end in \".99\"? How should a company balance the risk of holding too much inventory on hand and the risk of turning away customers? Does it ever make sense for retailers to lie to suppliers about how much they will need to order? Should retailers with multiple locations hold most of their inventory in a central warehouse or at the individual locations?\u003c/p\u003e\n\u003cp\u003eThese are only a small sample of the operational and pricing challenges all businesses regularly face. These challenges are often addressed individually and in isolation but, in reality, all of these decisions interact with each other. This class looks at the demand and supply management challenges faced by companies in various industries and provides an introduction to the tools that can be used to address these challenges.\u003c/p\u003e538:T9ed,"])</script><script>self.__next_f.push([1,"\u003cp\u003eBlockchain technology is changing how businesses operate. With trust built into blockchain solutions, it is important to understand how this new technology is different and how it works in comparison with technologies of the past.\u003c/p\u003e\n\u003cp\u003eFirst, we cover the main concepts of what blockchain is and some of its major characteristics. We take you through the important players in this diverse community. We discuss how it began, first introduced for the administration of the Bitcoin cryptocurrency, and how it is now applied to all aspects of business including government, banking, supply chains, and other industries.\u003c/p\u003e\n\u003cp\u003eNext, we analyze the mechanics of blockchains and how they work. We will cover the concept of transparent ledgers, public and permissioned, and focus on using cryptography to achieve consensus, immutability, and transparency. This is all part of blockchain's ability to provide \"trusted data from untrusted sources\", disrupting traditional accounting methodologies and international trade.\u003c/p\u003e\n\u003cp\u003eWe will cover some of the functions of blockchain. You will discover the power of the smart contract, the building blocks for transactions on the blockchain. You will gain an understanding of the different blockchain structures and how start up decisions influence how your blockchain deals with security, identity, consensus, and governance.\u003c/p\u003e\n\u003cp\u003eWe then dive into the various methods of blockchain governance. You will understand how different governance models dictate how your blockchain operates, and gain insight into consortiums and how this transformative technology is creating new avenues of communication.\u003c/p\u003e\n\u003cp\u003eNext, we examine the problems blockchain solves that have been difficult to overcome in the past with more centralized architectures, discovering how blockchain tackles the double spend issue, creates autonomy and transparency while facilitating innovative ways for multiple parties to interact. You will then discover the new and creative ways blockchain is changing the future. New insights and models dealing with Identity will be explored. You will look at trends in Decentralized Finance, NFT (non-fungible tokens), CBDC (central bank digital currencies) and how the push for interoperability between blockchains is becoming increasingly important.\u003c/p\u003e\n\u003cp\u003eFinally, we take a deep dive into the various use cases of blockchain, complete with analyzing real examples of how different industries are executing the technology and opening up new avenues for improving their businesses.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"539:T4d8,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eThis course will give you the necessary background for developing cybersecurity skills and understanding threat intelligence in cybersecurity.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThroughout the course, you will gain a comprehensive understanding of network defensive tactics and explore practical examples of how to protect networks from potential threats. You will delve into the concepts and tools of data loss prevention and endpoint protection, to equip yourself with strategies to safeguard critical data. Additionally, you will have the opportunity to explore a data loss prevention tool and learn how to effectively classify data within your database environment and enhance data security measures.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis course also covers essential security vulnerability scanning technologies and tools, enabling you to identify potential weaknesses in systems and applications. You will recognize various application security threats and common vulnerabilities. \u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eEnroll in this course today in order to establish the necessary foundation for developing cybersecurity expertise as part of the IBM Cybersecurity Analyst Professional Certificate program.\u003c/p\u003e53a:T8e0,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis course introduces the elements involved in the design of psychological research. To produce reliable, valid results that can be used to understand psychological processes, researchers must take great care in how they define and measure variables, how they sample groups of individuals from the populations of people or animals that they are interested in, how they control extraneous variables in the research setting, and myriad other factors. Psychologists can choose from an array of research designs, including observational, correlational, experimental, or quasi-experimental methods. And depending on the design, they must consider the many factors that affect the reliability of those observations and the validity of any conclusions that might be drawn from them. This course considers all these and many other aspects of psychological research. A large segment of this course is then devoted to issues related to how psychologists ensure that the research methods they employ are ethical. Everyone would agree that ethical considerations are one of the most important things researchers must think about when they plan their studies, but it’s also one of the most invisible in reports on psychological research. Any article that reports the results of a psychological study provides voluminous details about the methodology, about issues like sampling and validity, for instance, but little if any mention of the relevant ethical issues, even though there are ethical issues in play at every step of the process. Behind all the fascinating research in the psychological literature are rules, regulations, and expectations about how the people or animals we study will be treated, and about how the data we collect will be analyzed and reported. And behind all those standards are more general principles that have been established from grim experience that includes some of the worst things humans have ever done in the name of science, and other missteps that hindsight tells us we don’t want to repeat. In this course, we investigate that history and abstract from it those principles that now guide ethical decisions about how to do research. To what goals do those principles aspire? And what breaches are they meant to keep us from repeating?\u003c/p\u003e"])</script><script>self.__next_f.push([1,"53b:T72a,\u003cul\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eDescribe the steps of the scientific method.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eExplain how to judge the quality of a source for a literature review.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eCompare and contrast the kinds of research questions scientists ask.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eSpecify how variables are defined.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eExplain what it means for an observation to be reliable.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eCompare and contrast the major research designs.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eCompare and contrast forms of validity as they apply to the major research designs.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eIllustrate the history of ethical concerns about scientific research using specific examples.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eDescribe purposes served by codes of research ethics.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eExplain the five general Ethical Principles of the APA Ethics Code.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eDescribe the issues addressed by the APA Ethical Standards that apply to researchers.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eExplain how IRBs and IACUCs operate. \u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eOutline the major ethical considerations in planning a research study using nonhuman animals.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eExplain the importance of the three Rs in animal research.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eExplain the protections ethics codes offer for human participants in research. \u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eDescribe the implications of WEIRD participants being overrepresented in psychological research.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eIdentify the consequences of the different types of fraud in research.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eExplain how replication and preregistration address the problems of p-hacking and HARKing.\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003eApply your knowledge of ethical violations and propose solutions for real-life research.\u003c/p\u003e\u003c/li\u003e\n\u003c/ul\u003e53c:T43c,\u003cp\u003eThis course provides details on how card payments are processed, including differences between older ‘magnetic stripe’ transactions and newer EMV (or ‘chip’) based transactions. Using t"])</script><script>self.__next_f.push([1,"his as a base, the course outlines specifically how payment solutions are being implemented around the world on smartphones and mobile devices, the differences between mobile acceptance and mobile issuance, and how other types of payment methods and payment processes may be implemented on these devices. This includes discussions of tokenization and token service providers, their relationship to financial institutions and merchants, as well as card transactions using card readers, contactless payments, and the rise of mobile apps, mobile wallets, and the ledger systems that are often used to manage these processes. Topics of mobile security, security standards, cryptography, protection of sensitive data, potential data breaches, malware, and key management are touched upon at a high level, but primary focus is provided to the mobile payments eco-system and implementations.\u003c/p\u003e53d:T4de,\u003cp\u003eIn this course you will learn how to think systematically and critically about the concepts and challenges informing implementation of any mobile payments strategy. You will come to understand the core issues that underlie the changing landscape of payments and how to secure digital transactions by learning:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ethe principle components of open and closed loop payment systems\u003c/li\u003e\n\u003cli\u003ethe difference between MagStripe and EMV payments and threat models associated with each\u003c/li\u003e\n\u003cli\u003ethe differences and similarities between contact and contactless payments using EMV\u003c/li\u003e\n\u003cli\u003ewhat the core standards for payments are and how they are changing\u003c/li\u003e\n\u003cli\u003ethe different types of cardholder authentication\u003c/li\u003e\n\u003cli\u003ethe differences between mobile issuance and mobile acquiring and the role that financial institutions play in each\u003c/li\u003e\n\u003cli\u003ethe roles that tokenization and token service providers play in mobile payments\u003c/li\u003e\n\u003cli\u003eabout the migration away from dedicated payment terminals and what that means for the future\u003c/li\u003e\n\u003cli\u003eabout securing enrollment and implementation of mobile applications \u003c/li\u003e\n\u003cli\u003ewhat the key drivers for change a"])</script><script>self.__next_f.push([1,"re in the area of payments and how these are shaping the emerging patterns for fraud\u003c/li\u003e\n\u003c/ul\u003e53e:T835,"])</script><script>self.__next_f.push([1,"\u003cp\u003eOver the past decade emerging technologies, paired with massive changes in regulations, have driven an unprecedented transformation of finance around the world. This process is happening more rapidly in China and Asia than anywhere else. This course is designed to explore FinTech fundamentals and help make sense of this wave of change as it happens.\u003c/p\u003e\n\u003cp\u003eNew players such as start-ups and technology firms are challenging traditional players in finance, bringing democratization, inclusion and disruption. Companies engaged in social media, e-commerce, and telecommunications, as well as, companies and start-ups with large customer data pools, creative energies, and technical capacities, have brought competition to the existing financial infrastructure and are remaking the industry.\u003c/p\u003e\n\u003cp\u003eThese transformations have not only created challenges but also unprecedented opportunities, building synergies with new business and regulatory models, particularly in emerging markets and developing countries. To meet these changes, 21st-century professionals and students must be equipped with up-to-date knowledge of the industry and its incredible evolution. This course - designed by HKU with the support of SuperCharger and the Centre for Finance, Technology and Education - is designed to enable learners with the necessary tools to understand the complex interaction of finance, technology and regulation.\u003c/p\u003e\n\u003cp\u003eIn this course, through a series of video lectures, case studies, and assessments you will explore the major areas of FinTech including, beginning with What is FinTech before turning to Money, Payment and Emerging Technologies, Digital Finance and Alternative Finance, FinTech Regulation and RegTech, Data and Security, and the Future of Data Driven Finance, as well as, the core technologies driving FinTech including Blockchain, AI and Big Data. These will set the stage for understanding the FinTech landscape and ecosystem and grappling with the potential direction of future change.\u003c/p\u003e\n\u003cp\u003e\u003cimg alt=\"\" src=\"https://www.edx.org/sites/default/files/gafm_seal_logo.jpg\" /\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"53f:T43b,\u003cp\u003eUpon completion of the course, participants should be able to:\u003c/p\u003e\n\u003cp\u003e• Explain the basic concepts, definitions, and accounting principles in the integrated GFS framework.\u003c/p\u003e\n\u003cp\u003e• Classify basic government flows and stock positions according to GFSM 2014.\u003c/p\u003e\n\u003cp\u003e• Apply the general principles to classify an entity in the public sector and in relevant subsectors, such as the general government and public corporations.\u003c/p\u003e\n\u003cp\u003e• Record the fiscal flows and stocks associated with the activities of public sector entities, following the GFSM 2014 guidelines and classifications.\u003c/p\u003e\n\u003cp\u003e• Explain how the main GFS aggregates and analytical balances are calculated, and what they show about the government’s impact on the economy.\u003c/p\u003e\n\u003cp\u003e• Develop a migration plan to adopt the GFSM 2014 methodology, and compile and disseminate GFS following international guidelines.\u003c/p\u003e\n\u003cp\u003e• Recognize the value of comprehensive, consistent, and internationally comparable GFS, and the use of the key GFS indicators in the design, monitoring, and evaluation of fiscal policy.\u003c/p\u003e540:Tc4f,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis course will teach you how to digitalize the 'conventional' grid and which digital technologies you can use for this, including but not limited to, AI, machine learning, blockchain and computer simulations.\u003c/p\u003e\n\u003cp\u003eEven though the power grid has been the main driver of the rapid advancement of technology in the past decades, the engineering of the grid is outdated by today's standards. Equipment is still hardwired and analogue, there is limited data acquisition, and many control actions are performed manually. Subsequently, it becomes harder to ensure similar system quality and efficient operations when more renewables are added to the grid. The next big leap, therefore, is to revamp and digitalize the energy system. \u003cbr /\u003e\nWe invited five prominent industry leaders to share industry perspectives, case studies and applications of digitalization in the fields of Grid Operations, Electric Power Systems, Power Distribution, Electrical Systems, Control Systems and Cybersecurity, but also in Consultancy and Software Development. Our guest speakers are: Philip Gladek (CEO of Spectral), Bas Kruimer (Business Director Digital Grid Operations at DNV), Martin Wevers (Grid Planner at TenneT), Evelyn Heylen (Head of research at Centrica) and Antoine Marot (Lead AI Scientist at RTE). \u003cbr /\u003e\nYou will also get hands-on experience through solving an optimal scheduling problem, noting the differences between different numerical simulation methods and applying machine learning to predict system overloads.\u003c/p\u003e\n\u003cp\u003eThis course is aimed at professionals in the energy industry who want to broaden their perspective and discover alternative approaches to energy integration in an intelligent way such as:\u003cbr /\u003e\n- grid operators, electrical systems managers, control systems managers, power engineers\u003cbr /\u003e\n- cybersecurity consultants, software developers, artificial intelligence managers/ scientists, energy consultants\u003cbr /\u003e\n- project managers, planners, policy makers etc.\u003cbr /\u003e\nAny other enthusiasts with the desire to learn more about current practices of the power grid, novel digital technologies and trends to deploy them can also enrol in this course.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\"At DNV, our vision is to be a trusted voice to tackle global transformations. The energy transition is vital to decelerate climate change. Integrating energy systems in an intelligent way is a critical skill for the engineers, project managers, planners, policymakers, and scientists of the future.\u003c/em\u003e\u003cbr /\u003e\n\u003cem\u003eThe course “Digitalization of intelligent and integrated energy systems” comes at the right time to tackle the challenges and complexity of today’s energy systems. It provides you with a powerful framework to digitalize energy systems by using AI, machine learning, simulations, digital twins and sheds light on cyber security matters. It helps you to integrate electric cars, heat, gas and electricity into the energy system. I would highly recommend this innovative course to everyone who is interested in the digital transformation of the energy system.\"\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBas Kruimer, Business Director Digital Grid Operations, Energy Systems, DNV\u003c/p\u003e"])</script><script>self.__next_f.push([1,"541:T5e8,\u003cp\u003eMathematics is the most essential tool in any STEM professional’s toolbox. In this course, we will provide you with an introduction to linear algebra, multivariable calculus, and differential equations, through exploring the main definitions, theorems and practical examples required. \u003c/p\u003e\n\u003cp\u003eCan we use linear algebra to do data compression? What’s the meaning of an eigenvalue and an eigenvector in a mechanical system? How do vector fields help to describe wind flow? How can you make optimal parameter choices in industrial processes?\u003c/p\u003e\n\u003cp\u003eWe aim to answer all these questions and more, so that you can use these mathematical techniques when tackling problems in your own field of study.\u003c/p\u003e\n\u003cp\u003eWe will use examples, graphic representations, applets, and exercises to exemplify the various theorems and definitions.\u003c/p\u003e\n\u003cp\u003eYou will acquire the skills to cope with matrix-formulated problems typically arising from applications in science and technology. Not only will you be able to use practical algorithms, solve systems of equations and differential equations, compute the singular value and eigenvalue decomposition, and solve optimisation problems, you will also acquire a set of properties that will assist in simplifying and understanding mathematical problems.\u003c/p\u003e\n\u003cp\u003eThe course will give you the tools to transform optimisation problems and differential equations into matrix language. Most importantly, you will learn that matrix computations are ubiquitous in science and engineering.\u003c/p\u003e542:T48d,\u003cul\u003e\n\u003cli\u003eWhat vector spaces are and how their elements can be represented by coordinate vectors with respect to a basis\u003c/li\u003e\n\u003cli\u003eLinear transformations between vector spaces and how to represent them in matrix notation\u003c/li\u003e\n\u003cli\u003eTo compute inner products, norms, and orthogonal projections\u003c/li\u003e\n\u003cli\u003eTo define and calculate eigenvalues and eigenvectors and their algebraic and geometric multiplicities\u003c/li\u003e\n\u003cli\u003eTo calculate the singular value decomposition\u003c/li\u003e\n\u003cli\u003eTo understand the concepts of a real function of multi"])</script><script>self.__next_f.push([1,"ple variables, partial and directional derivatives and the multivariate chain rule\u003c/li\u003e\n\u003cli\u003eTo determine critical points and identify extrema of multivariate functions\u003c/li\u003e\n\u003cli\u003eTo understand the concepts of (conservative) vector fields and be able to calculate and simplify their line integrals\u003c/li\u003e\n\u003cli\u003eTo understand what gradient, divergence, and curl operators are and how to calculate them\u003c/li\u003e\n\u003cli\u003eTo classify and solve (systems of) first-order differential equations\u003c/li\u003e\n\u003cli\u003eTo understand and apply linear algebra techniques to solve linear systems of differential equations with constant coefficients and analyse their stability\u003c/li\u003e\n\u003c/ul\u003e543:T802,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eNowadays, many organizations and individuals produce applications for their businesses. Unfortunately, these apps may be plagued by bugs, slow speeds, or poor performance. How can you know that your app is performing at an acceptable standard? Monitoring and observability are key to ensuring continuous uptime and delivery for your applications.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eApplication monitoring is the process by which developers can identity, measure, and evaluate how well an application is working. Monitoring allows developers to proactively watch and fix issues in application performance. Observability, on the other hand, refers to how well an application can be monitored by the data gained from monitoring. Monitoring and observability work together to provide insights into your system and keep it in working order.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis course provides a comprehensive overview of monitoring and observability. You will learn the fundamentals of monitoring, observability, and evaluation, as well as popular tools for application monitoring, such as Prometheus and Grafana. The course will also cover data visualization tools used in monitoring, like Kibana and Splunk.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe course will also cover the Three Pillars of Observability, and you will learn how to work with the OpenTelemetry framework and how to create logs with Mezmo.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThroughout this course, you will participate in multiple interactive labs to gain experience with monitoring and observability skills, as well as the popular tools mentioned above. This will provide you with hands-on experience with the tools and skills used every day by professionals.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIn the final project, you’ll apply the knowledge you’ve gained to a real-world application scenario. You will be able to demonstrate your knowledge of monitoring and observability, and you will gain the confidence to perform these tasks in a practical setting.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"544:Td03,"])</script><script>self.__next_f.push([1,"\u003cp\u003eÉ comum ver equipes de projetos tendo que investir grande parte de seu tempo para resolver problemas em vez de preveni-los. A gestão de riscos de projetos procura gerenciar antecipadamente os eventos positivos e negativos que possam afetar o projeto, a fim de aumentar sua probabilidade de êxito.\u003c/p\u003e\n\u003cp\u003eO que você estará apto a fazer ao final deste curso?\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003e\n\u003cp\u003eIdentificar riscos em cenários de incerteza.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDefinir quem pode apoiá-lo.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEntender como gerenciar riscos em situações em que os recursos são limitados.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEstabelecer estratégias para responder aos riscos.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMonitorar e atualizar os riscos ao longo do projeto.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eA quem se dirige este curso? A profissionais da América Latina e do Caribe, particularmente a:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eDiretores ou supervisores de projetos.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMembros de equipes de projetos.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFinanciadores.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eFuncionários públicos de entidades nacionais, subnacionais e municipais.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eProfissionais de diversas áreas, que contribuem para a formulação e execução de projetos.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eProfissionais interessados na gestão de riscos em projetos de desenvolvimento.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAo fazer este curso, você terá a oportunidade de compartilhar seu conhecimento e sua experiência com outros participantes. Além disso, terá acesso a um estudo de caso, ao qual poderá aplicar as boas práticas de gestão de riscos em um projeto e fará exercícios práticos, que o ajudarão a compreender conceitos-chave.\u003c/p\u003e\n\u003cp\u003eO conteúdo apresentado no curso está baseado na metodologia de gestão de riscos do Banco Interamericano de Desenvolvimento, a qual está alinhada com o Guia do Conhecimento em Gerenciamento de Projetos (Guia PMBOK®), sexta edição, 2017, do Project Management Institute (PMI)®.\u003c/p\u003e\n\u003cp\u003eEsse curso é na modalidade “ritmo próprio” (self-paced), você poderá se inscrever a qualquer momento, mesmo que o curso esteja aberto há algum tempo. Pode fazê-lo no tempo que dura o curso, no momento que for mais adequado para você.\u003c/p\u003e\n\u003cp\u003eSe você optar pelo modo \u003cstrong\u003eAssistente\u003c/strong\u003e , terá acesso ilimitado ao conteúdo do curso, mas não poderá realizar as atividades avaliadas nem obter o certificado.\u003c/p\u003e\n\u003cp\u003eSe você optar pelo modo \u003cstrong\u003eCertificado verificado\u003c/strong\u003e , você pode acessar o curso de forma ilimitada e realizar as avaliações qualificadas até a data de encerramento, após efetuar o pagamento de \u003cspan lang=\"pt\"\u003e25 \u003c/span\u003eUS$. Se você passar, além do certificado verificado, você obterá uma \u003cstrong\u003e\u003ca href=\"https://iadb.badgr.io/public/badges/F1e92dnLQnSll8HgvyKOAA\" rel=\"noopener\" target=\"_blank\"\u003ecredencial digital\u003c/a\u003e\u003c/strong\u003e * que permite mudar a forma de compartilhar suas conquistas acadêmicas e profissionais, como por exemplo, nas redes sociais.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*Você sabia que existe um apoio financeiro para obter o certificado verificado?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA edX oferece auxílio financeiro para aqueles alunos que têm dificuldades para efetuar o pagamento. Inscreva-se no curso e preencha este formulário de \u003ca href=\"https://courses.edx.org/financial-assistance/\" rel=\"noopener\" target=\"_blank\"\u003e\u003cstrong\u003eassistência financeira\u003c/strong\u003e\u003c/a\u003e.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"545:T415,\u003cp\u003e\u003cstrong\u003eCompetências que você dominará:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Identificar as principais etapas para elaborar um plano de gestão de riscos em projetos de desenvolvimento, segundo a metodologia do BID, a qual está baseada nos padrões internacionais do PMI, para que se alcancem satisfatoriamente os resultados e impactos esperados.\u003c/p\u003e\n\u003cp\u003e2. Reconhecer o processo de identificar os riscos que podem afetar o projeto e documentar suas características, a fim de facilitar sua análise e gestão posteriores.\u003c/p\u003e\n\u003cp\u003e3. Reconhecer os principais elementos do processo de análise de riscos, avaliando e combinando a probabilidade de sua ocorrência e seu impacto, a fim de priorizá-los.\u003c/p\u003e\n\u003cp\u003e4. Reconhecer as estratégias de gestão de riscos que permitam gerenciar as oportunidades e ameaças que possam impactar os objetivos do projeto.\u003c/p\u003e\n\u003cp\u003e5. Reconhecer os principais elementos do processo de monitoramento de riscos, otimizando de modo contínuo as respostas, a fim de melhorar a eficiência na gestão do ciclo de vida do projeto.\u003c/p\u003e546:T605,\u003cp\u003eAs large language models (LLMs) revolutionize the AI landscape, it is crucial to understand and address the unique security challenges they present. This comprehensive course is designed to equip you with the knowledge and skills needed to identify, mitigate, and prevent vulnerabilities in your LLM applications. Through a series of in-depth lessons, you will:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eExplore common security threats, such as model theft, prompt injection, and sensitive information disclosure\u003c/li\u003e\n\u003cli\u003eLearn techniques to prevent attackers from exploiting vulnerabilities and compromising your AI systems\u003c/li\u003e\n\u003cli\u003eDiscover best practices for secure plugin design, input validation, and sanitization\u003c/li\u003e\n\u003cli\u003eUnderstand the importance of actively monitoring dependencies for security updates and vulnerabilities\u003c/li\u003e\n\u003cli\u003eGain insights into effective strategies for protecting against unauthorized access and data breaches\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWhether you are a developer, data scientist, or AI enthu"])</script><script>self.__next_f.push([1,"siast, this course will provide you with the essential tools to ensure the integrity and safety of your LLM applications. By the end of the course, you will be well-versed in the latest security measures and be able to confidently deploy robust, secure AI solutions.\u003c/p\u003e\n\u003cp\u003eDon't let vulnerabilities undermine the potential of your LLM applications. Join us today and take the first step towards becoming an expert in LLM security. Enroll now and unlock the knowledge you need to safeguard your AI projects in an increasingly complex digital landscape.\u003c/p\u003e547:T610,\u003cp\u003eWhether on their own, connected to a local network, or plugged into the internet, computers are central to many usual tasks and routines. It's important for young people to develop their knowledge of how these devices work so that they can appreciate the impact of computers and networks on the world around them.\u003c/p\u003e\n\u003cp\u003eIn this 3-week course, you'll explore how to build young learners' knowledge in an age-appropriate manner. You'll start by thinking about how you can help your learners recognise information technology and how computers process an input to give an output.\u003c/p\u003e\n\u003cp\u003eIn the second week of the course, you'll learn about the key pieces of technology that allow us to connect computers to networks and the advantages that networks can bring. You'll also consider how you can make the concept of a computer network relevant to young learners.\u003c/p\u003e\n\u003cp\u003eBy the end of week 2, you'll be able to explain to your learners why the internet and the World Wide Web are not the same thing, as well as help them identify a range of services that run over the internet. \u003c/p\u003e\n\u003cp\u003eIn the third week, you'll investigate other computer systems that consist of many devices working together.\u003c/p\u003e\n\u003cp\u003eYoung people also need to understand how to act on the internet \u0026mdash; both for their own safety and to contribute to a pleasant online environment. You'll think about the skills and attitudes that young people need and how you can help them develop these not only while teaching about"])</script><script>self.__next_f.push([1," computer networks but also more generally across the curriculum.\u003c/p\u003e548:Ta98,"])</script><script>self.__next_f.push([1,"\u003cp dir=\"ltr\"\u003eConversational AI/voice technology is the ability to interact with devices and systems using natural language speech. It has become increasingly popular and widespread in recent years, thanks to advances in artificial intelligence (AI), natural language processing (NLP), and speech recognition. The technology can be found in various applications, such as smart speakers, smart assistants, chatbots, voice search, voice commerce, voice biometrics, voice analytics, customer service automation (IVR), and generative AI tools (chatGPT).\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\n\n****Conversation AI//voice technology has many benefits for users and businesses alike. It can provide access to the digital world for people who have difficulties with typing or seeing, such as those with disabilities, low literacy, or low vision. It can also enhance productivity, convenience, safety, and engagement by enabling hands-free and eyes-free communication and control. Moreover, it can create personalized and immersive experiences. Within an enterprise organization, voice technology can lead to productivity enhancements, e.g., automation of processes in call centers, training facilitation, expedited information sharing, etc.\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\n\n****However, voice technology also poses some pressing questions that need to be addressed: ****\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003c/p\u003e\n\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cp dir=\"ltr\"\u003eWhat are conversational AI/voice specific issues of ethical and moral concern, and how do we define them? \u003c/p\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cp dir=\"ltr\"\u003eWhat are the values to be promoted for ethical interaction with conversational/voice AI technology? \u003c/p\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cp dir=\"ltr\"\u003eWhat are the rights to be respected for successful and ethical experiences? \u003c/p\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cp dir=\"ltr\"\u003eWhat preventative measures can we take to protect people from specific harm?\u003c/p\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cp dir=\"ltr\"\u003eWhat can we do to cover gaps that are not yet fully covered by existing laws and guidelines? \u003c/p\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cp dir=\"ltr\"\u003eWhat can we learn from past harmful incidents and vulnerabilities, and how can we prevent them from happening in the future?\u003c/p\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp dir=\"ltr\"\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\n\nThis course discusses the use and value of voice technology to provide access to the digital world and bring tangible improvements to people’s lives, the ethical challenges and impact of conversational/voice AI, and the principles and frameworks to avoid potential harm in humans interacting with the technology. This course offers practical guidance on value to be promoted, the rights to be respected, and preventative measures to be taken for successful, ethical voice experiences.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"549:Tbec,"])</script><script>self.__next_f.push([1,"\u003cp\u003eHoje mais do que nunca, os cidadãos da América Latina e o Caribe exigem uma administração pública ágil e proativa que lhes permita fazer seus negócios sem sair de suas casas. Em outras palavras, eles exigem um governo digital. Da mesma forma, os governos precisam se transformar para tirar proveito dos benefícios que a digitalização traz aos países: maior eficiência e produtividade, melhor tomada de decisões e menos corrupção. Nos últimos anos, os governos do Brasil, começaram a criar estratégias digitais para melhorar seus serviços às pessoas e empresas, transformando a gestão dos seus procesos e melhorando o intercâmbio de informações dentro e entre instituições. Graças a este curso, você conhecerá algumas das experiências bem-sucedidas de transformação digital do governo no Brasil.\u003c/p\u003e\n\u003cp\u003eNeste curso vamos apresentar três módulos sobre o design de projetos de governo digital. Primeiro há um módulo básico, incluindo a identificação dos problemas que provavelmente serão melhorados através da transformação digital: uma estratégia, um diagnóstico e uma estrutura de monitoramento e avaliação; todos os quais são necessários em qualquer projeto de desenvolvimento, mas têm diferentes toques no mundo do governo digital. Em seguida, você verá alguns dos componentes técnicos mais comuns, tais como simplificação de procedimentos, modernização da gestão administrativa, criação de uma visão holística dos dados e reforço da cibersegurança. E finalmente, você discutirá questões transversais que são necessárias para viabilizar os componentes técnicos a serem implementados: uma estrutura regulatória, talento humano, comunicação e design centrado no usuário.\u003c/p\u003e\n\u003cp\u003eNão perca a oportunidade e inscreva-se para aprender com especialistas do Banco Interamericano de Desenvolvimento e benchmarks internacionais que compartilham suas experiências e lições aprendidas neste curso.\u003c/p\u003e\n\u003cp\u003eEsse curso é na modalidade “ritmo próprio” (self-paced), você poderá se inscrever a qualquer momento, mesmo que o curso esteja aberto há algum tempo. Pode fazê-lo nas semanas que dura o curso, no momento que for mais adequado para você.\u003c/p\u003e\n\u003cp\u003eSe você optar pela \u003cstrong\u003emodalidade Assistente\u003c/strong\u003e , terá acesso ilimitado ao conteúdo do curso, mas não poderá realizar as atividades avaliadas nem obter o certificado.\u003c/p\u003e\n\u003cp\u003eSe\u003cspan lang=\"PT\"\u003e optar pelo acesso ao curso na\u003c/span\u003e \u003cstrong\u003emodalidade\u003c/strong\u003e \u003cstrong\u003eCertificado verificado,\u003c/strong\u003e poderá acessar o curso de forma ilimitada e poderá realizar as avaliações qualificadas até a data de encerramento, após efetuar o pagamento de US$ 29. Se você for aprovado no curso, além do certificado, receberá uma \u003cstrong\u003e\u003ca href=\"https://cursos.iadb.org/pt-br/indes/credenciais-digitais\" rel=\"noopener\" target=\"_blank\"\u003ecredencial digital\u003c/a\u003e\u003c/strong\u003e adicional que permite transformar a maneira de compartilhar suas conquistas acadêmicas e profissionais, como por exemplo, nas redes sociais.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"54a:T936,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cem\u003ePlease note: The capstone project is only accessible for ID-verified MicroMasters Program learners who successfully obtained verified certificate in all MicroMasters Program courses.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn the first three courses of the MicroMasters Program, you will learn about all the different steps in a biobased process and the business and operations aspects you should consider before choosing a certain process. In this capstone project, you will work on integrating the technological section with the business and operations sections to develop a sustainable biobased practice. The focus is on linking the various aspects into an integral research, based on literature research and applied to a practical case.\u003c/p\u003e\n\u003cp\u003eThe final product in this capstone project is a written report. From a business perspective, you will write an advice for an audience of your choice, for example the executive board of a company, an investor or a governmental agency. You are free to choose the subjects you want to address in this advice. This means you get the opportunity to work on a case of your choice and receive feedback from experts in the field.\u003c/p\u003e\n\u003cp\u003eIn order to find the information and publications you need, you will get tips and advice on how to do proper literature research. Along the way, you will get feedback on your proposal, draft and final report. The final report should reflect the academic research capabilities on a master's level, i.e. defining a research proposal, proper literature research, methodology, data, results and conclusions and discussion.\u003c/p\u003e\n\u003cp\u003eYou can start the capstone project after completing all other courses in the \u003ca href=\"https://www.edx.org/micromasters/wageningenx-business-and-operations-for-a-circular-bio-economy\"\u003eMicroMasters Program Business and Operations for a Circular Economy\u003c/a\u003e, with a verified certificate for every course:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/circular-economy-an-interdisciplinary-approach\"\u003eCircular Economy: An Interdisciplinary Approach\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/business-strategy-and-operations-in-a-biobased-eco\"\u003eBusiness Strategy and Operations in a Biobased Economy\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/from-fossil-resources-to-biomass-a-chemistry-persp\"\u003eFrom Fossil Resources to Biomass: a Chemistry Perspective\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e"])</script><script>self.__next_f.push([1,"54b:T66c,\u003cp\u003eIl linguaggio di programmazione è uno degli strumenti che abbiamo per interpretare e risolvere i problemi di tutti i giorni. Un linguaggio che è alla base di problemi comuni, come le previsioni del tempo o l'analisi della deformazione di una struttura di un'auto in un incidente stradale. \u003c/p\u003e\n\u003cp\u003eQuesto corso fornisce un’introduzione alle metodologie e agli strumenti per la risoluzione di problemi attraverso l'uso del computer. Sarai guidato nell'individuazione di metodologie di progetto, sviluppo ed analisi degli algoritmi di base per il calcolo scientifico nonché all’uso dei principali strumenti di calcolo (hardware e software), con particolare riguardo all’influenza che questi ultimi esercitano sullo sviluppo degli algoritmi stessi. \u003c/p\u003e\n\u003cp\u003eUna volta completato questo corso, segui la seconda parte con \u003ca href=\"https://www.edx.org/course/laboratorio-di-programmazione-strumenti-e-programmi\"\u003eLaboratorio di programmazione: strumenti e programmi\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProgramming Lab\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProgramming languages offera solution for interpreting and solving everyday problems. A language that underlies basic problems, like forecasting the weather, or understanding the dynamics of a car accident from the damage to the car. \u003c/p\u003e\n\u003cp\u003eThis course introduces students to the various methods and tools for problem-solving via computer. You will learn how to select the best methodology for your project, how to develop and analyze basic algorithms for scientific calculation, and how to use the main calculation tools (hardware e software). You will also see how these tools influence the development of algorithms.\u003c/p\u003e54c:T4c6,\u003cp\u003eThere are billions of devices in homes, factories, oil wells, hospitals, cars, and thousands of other places. With the proliferation of devices, you increasingly need solutions to connect them, and collect, store, and analyze device data. AWS IoT provides broad and deep functionality, spanning the edge to the cloud, so you can build IoT solutions for virtually any use case across "])</script><script>self.__next_f.push([1,"a wide range of devices. \u003c/p\u003e\r\n\u003cp\u003eThis course will introduce you to the Internet of Things and then explore Amazon Web Services’ IoT services, and then expert instructors will dive deep into topics such as the device gateway, device management, the device registry, and shadows. They will also discuss security features and implications, core and edge computing capabilities and benefits, and the use of HTTP and MQTT as communications protocols. Lastly, they will discuss the integration of IoT solutions with analytics tools, which will allow you to analyze the IoT data being collected by your fleet of devices. \u003c/p\u003e\r\n\u003cp\u003eThis course will provide a combination of video-based lectures, demonstrations and hands-on lab exercises, run in your own AWS account, that will allow you to build, deploy and manage your own IoT solution.\u003c/p\u003e54d:T682,\u003cp\u003e\u003cem\u003ePlease Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis course introduces you to the IBM Cloud. You will learn about the many offerings and services on IBM Cloud that make it the most open and secure public cloud for developers and enterprises.\u003c/p\u003e\n\u003cp\u003eThe course begins with an introduction to the IBM Cloud platform which covers topics such as data center locations and configuring identity and access management. You will discover the various Infrastructure-as-a-Service (IaaS) options available on IBM Cloud. Next, you will learn about the deployment options on IBM Cloud; this includes topics such as Containers, Kubernetes, and OpenShift. You will also become familiar with IBM Cloud services such as Databases, Artificial Intelligence and Watson, Blockchain, Internet of Things, and many others.\u003c/p\u003e\n\u003cp\u003eIn addition to videos, you will also see demos of various IBM Cloud features and services in action, as well as perform hands-on labs to gain practical "])</script><script>self.__next_f.push([1,"experience with IBM Cloud at no charge.\u003c/p\u003e\n\u003cp\u003eThis course is of interest to anyone who wants to be a cloud practitioner and use Cloud skills as developers, architects, system engineers, network specialists, and many other roles. The material also serves the needs of those who perform the tasks of advising, building, moving, and managing cloud solutions. This course is also suitable for learners who want to prepare for IBM Cloud Foundations Certification.\u003c/p\u003e54e:T8f4,"])</script><script>self.__next_f.push([1,"\u003cp\u003eNominated for the 2020 edX Prize\u003c/p\u003e\r\n\r\n\u003cp\u003eThe explosion of embedded and connected smart devices, systems and technologies in our lives has created an opportunity to connect every ‘thing’ to the internet. The resultant data collection and connectivity has created efficiencies and solutions previously dreamt up only in science fiction stories.\u003c/p\u003e \r\n\r\n\u003cp\u003eThis is disrupting and transforming every industry around the world.\u003c/p\u003e \r\n\r\n\u003cp\u003eThis MicroMasters program will put you at the front of a digital revolution, where you can design problem-solving systems or guide cutting-edge IoT projects in your industry and area of expertise. Professionals from any field who want to leverage their existing business and/or technical knowledge across IoT-related functions in their workplace will learn exactly what IoT is, how it works, and how to harness its power to improve and streamline your business.\u003c/p\u003e \r\n \r\n\u003cp\u003eThe program will show you the scope of the IoT, and reveal underlying principles and architecture of its networks, devices, programming, data, and security. You will learn how an idea can become a viable, workable IoT product and be able to generate your own IoT ideas and design and/or support their development. \u003c/p\u003e \r\n\r\n\u003cp\u003eVerified learners will be able to participate in regular live discussions with course lecturers, and remotely access real laboratory equipment for practical sessions. In addition, verified learners will be given access to Cisco Network Academy resources.\u003c/p\u003e \r\n\r\n\u003cp\u003eCurtin is ranked in the top 200 in the world for Electrical and Electronic Engineering, has a 5-star QS rating, and is ranked ERA 5 (the highest possible rating for Excellence in Research in Australia).\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eNote:\u003c/b\u003e\u003c/p\u003e\r\n\u003cp\u003eThe IoT Capstone Project (IOT6x) is the final course in CurtinX's Internet of Things (IoT) MicroMasters® Program.\r\n\r\nLearners must have successfully completed IOT1x, IOT2x, IOT3x, IOT4x, and IOT5x to enroll and participate in this capstone course. It is also only available for verified learners.\r\n\r\nDue to these prerequisites and the intensive nature of this instructor-paced course, the IoT Capstone Project only runs once a calendar year, typically from early April until mid-July. Enrollments close soon after the course starts.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"54f:T6db,\u003cp\u003eAI is revolutionizing the way we live, work and communicate. At the heart of AI is Deep Learning. Once a domain of researchers and PhDs only, Deep Learning has now gone mainstream thanks to its practical applications and availability in terms of consumable technology and affordable hardware.\r\nThe demand for Data Scientists and Deep Learning professionals is booming, far exceeding the supply of personnel skilled in this field. The industry is clearly embracing AI, embedding it within its fabric. The demand for Deep Learning skills by employers -- and the job salaries of Deep Learning practitioners -- are only bound to increase over time, as AI becomes more pervasive in society. Deep Learning is a future-proof career.\u003c/p\u003e\r\n\r\n\u003cp\u003eWithin this series of courses, you’ll be introduced to concepts and applications in Deep Learning, including various kinds of Neural Networks for supervised and unsupervised learning. You’ll then delve deeper and apply Deep Learning by building models and algorithms using libraries like Keras, PyTorch, and Tensorflow. You’ll also master Deep Learning at scale by leveraging GPU accelerated hardware for image and video processing, as well as object recognition in Computer Vision.\u003c/p\u003e\r\n\r\n\u003cp\u003eThroughout this program you will practice your Deep Learning skills through a series of hands-on labs, assignments, and projects inspired by real world problems and data sets from the industry. You’ll also complete the program by preparing a Deep Learning capstone project that will showcase your applied skills to prospective employers.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program is intended to prepare learners and equip them with skills required to become successful AI practitioners and start a career in applied Deep Learning.\u003c/p\u003e550:T53c,\u003cp\u003eThe sports industry is one of the most exciting industries in the world to work in. From event management to sports marketing to sports analytics, this program will touch on all aspects of the business side of sports. To work on the business side of sports it requires a "])</script><script>self.__next_f.push([1,"blend between sports management and business administration education, which is the platform this certificate is built on!\u003c/p\u003e\r\n\r\n\u003cp\u003eMillions of people engage with professional sports and other sports organizations on a daily basis, and many dream of working in the industry. This professional certificate program will teach you about the inner workings of the sports industry and skills needed to work in specific areas of the sports industry. Successful learners will come away with a foundational understanding of the sport industry as a whole.\u003c/p\u003e\r\n\r\n\u003cp\u003eYou will learn what makes the sports industry unique, and how successful sport-business professionals operate. The program will introduce you to specific skills in sales and data-driven decision making, both of which are critical in the sport industry.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe business administration and sport management focus provides the necessary skills for recent college grads, high school students, sport professionals looking for professional development, or people looking to work in the sport industry.\u003c/p\u003e551:T9bc,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis professional certificate takes the innovative course series “Digital Transformation Playbook” and applies it to Supply Chain. The Smith and Clark schools leverage the foundational courses in Digital Strategy and Execution and extend those with a simulated course experience for the Supply Chain sector.\u003c/p\u003e\r\n\r\n\u003cp\u003eSupply Chain sector leaders are usually program managers, directors, or unit leads that need to know how to effectively utilize digital technology. At its core, the basic lessons are the same. In practice, the complexity of bureaucracy and the need to consider internal marketing (politics) within each organization is also essential.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program will take learners on a digital transformation journey to build required skills and prove mastery. The last course in this series adds required lessons and simulates digital transformation in supply chain based on real events and a real digital transformation. This allows business leaders to apply the skills and knowledge gained in the certificate by using micro-progressed case studies as scenarios to explore and solidify knowledge against the course objectives.\u003c/p\u003e\r\n\r\n\u003cp\u003eCourse objectives include:\r\n\u003cul\u003e\r\n\u003cli\u003eIdentify critical technologies that have the potential for disruption in your market and learn how to respond to them.\u003c/li\u003e\r\n\u003cli\u003eImprove management of internal and external relationships to better identify opportunities and risks.\u003c/li\u003e\r\n\u003cli\u003eKnow the customer journey analytics and Lean Analytics cycle that drive adoption of technologies.\u003c/li\u003e\r\n\u003cli\u003eEngage stakeholders with adoption of new technology to improve your program, product, or service offering.\u003c/li\u003e\r\n\u003cli\u003eDefine current capabilities and the dynamic future capabilities that are needed to respond to stakeholder and customer needs.\u003c/li\u003e\r\n\u003cli\u003eEstablish team and organization designs that align interests and create incentives for high performance.\u003c/li\u003e\r\n\u003cli\u003eEmpower people to solve problems and engage in transformation at various levels of the organization.\u003c/li\u003e\r\n\u003cli\u003eDrive outcomes with disciplined planning and execution that change the direction and capabilities of Agile Product Teams at scale.\u003c/li\u003e\r\n\u003cli\u003eGovern and manage the technology mix that is required to enable empowered teams with DevOps principles and practices that drive fast flow, feedback, and team learning.\u003c/li\u003e\r\n\u003cli\u003eKnow and be able to leverage relevant industry examples that serve as success stories to drive belief and vision in your plans.\u003c/li\u003e\r\n\u003c/ul\u003e\r\n\r\n\u003c/p\u003e"])</script><script>self.__next_f.push([1,"552:Ta09,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis professional certificate takes the innovative course series “Digital Transformation Playbook for X” and applies it to Government. But it doesn’t do so haphazardly. The Smith and Clark schools leverages the foundational courses in Digital Strategy and Execution and extend those with a simulated course experience for the Government sector.\u003c/p\u003e\r\n\r\n\u003cp\u003eGovernment sector leaders that are usually program managers, directors, or unit leads need to know how to sense and respond to digital technology. At its core, the basic lessons are the same. In practice, the complexity of bureaucracy and the need to consider internal marketing (politics) within each organization is also essential.\u003c/p\u003e \r\n\r\n\u003cp\u003eThis course series will take its learners on a digital transformation journey where they build required skills to prove mastery. The last course in this series adds required lessons and simulates digital transformation in government based on real events and a real digital transformation. This allows government leaders to apply the skills and knowledge gained in the certificate by using micro-progressed case studies as scenarios to explore and solidify knowledge against the course objectives.\u003c/p\u003e\r\n\r\n\u003cp\u003eCourse objectives include:\r\n\u003cul\u003e\r\n\u003cli\u003eIdentify critical technologies that have the potential for disruption across markets into your market, and how to respond to them.\u003c/li\u003e\r\n\u003cli\u003eBetter sense and manage customer relationships within your organization, business partnerships, and end users to better identify opportunities and risks.\u003c/li\u003e\r\n\u003cli\u003eKnow the customer journey analytics and Lean Analytics cycle that drive adoption of technologies and engage stakeholders with your program, product, or service offering.\u003c/li\u003e\r\n\u003cli\u003eDefine your current and future capability sets, and what dynamic capabilities are needed to improve your ability to respond to stakeholder and customer needs.\u003c/li\u003e\r\n\u003cli\u003eEstablish team and organization designs that align interests and create incentives that empower people to solve problems and engage in transformation.\u003c/li\u003e\r\n\u003cli\u003eDrive outcomes with disciplined planning and execution that change the direction and capabilities of Agile Product Teams at scale.\u003c/li\u003e\r\n\u003cli\u003eGovern and manage the technology mix that is required to enable empowered teams with DevOps principles and practices that drive fast flow, feedback, and team learning.\u003c/li\u003e\r\n\u003cli\u003eKnow and be able to leverage relevant industry examples that serve as success stories to drive belief and vision with the art of the possible into your organization.\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"553:T67a,\u003cp\u003e\u003cspan lang=\"ES\"\u003eComprender el funcionamiento de la economía de una forma sencilla te dará\u003c/span\u003e\u003cspan lang=\"ES\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003elas herramientas para entender los fenómenos sociales relacionados con la\u003cspan lang=\"ES\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003easignación de recursos.\u003cspan lang=\"ES\"\u003e\u003c/span\u003e\u003cspan lang=\"ES\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eEste curso, sin importar tu formación previa, te brindará los conceptos básicos y fundamentales de la economía, con el fin de que comprendas el funcionamiento del mercado y cómo toman decisiones los individuos. Adicionalmente, te permitirá entender el papel del Estado en el manejo de la economía de un país, así como conceptos clave de desarrollo y crecimiento económico. También te posibilitará el entendimiento de indicadores macroeconómicos.\u003cspan lang=\"ES\"\u003e\u003c/span\u003e\u003cspan lang=\"ES\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eEl curso está dividido en tres partes: en la primera parte se darán las definiciones básicas para entender el lenguaje de la ciencia económica; en la segunda parte se hará especial énfasis en la microeconomía, entrando en el detalle de la teoría del consumidor y del productor; y en la última parte se enseñará lo referente a la macroeconomía, explicando la utilidad de los principales indicadores económicos como PIB, IPC, desempleo, inflación, tipo de cambio, entre otros, para entender el comportamiento económico de los países. También se hablará de la política económica y el papel del estado y de los bancos centrales.\u003cspan lang=\"ES\"\u003e\u003c/span\u003e\u003cspan lang=\"ES\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eFinalmente, el curso le permitirá entender el impacto de las políticas públicas y su efecto en el bienestar de los ciudadanos.\u003c/p\u003e554:T5c8,\u003cp\u003eLos datos están en el corazón de nuestra economía digital, la ciencia de datos ha sido clasificada como la profesión más popular del siglo XXI. Ya seas nuevo en el mercado laboral o estés ya trabajando y quieras mejorar tus habilidades, este programa de Certificación Profesional de cinco cursos de Ciencia de datos con Python tiene como objetivo prepararte para"])</script><script>self.__next_f.push([1," una carrera profesional en la ciencia de datos y el aprendizaje automático. ¡No se requiere experiencia previa en programación!\u003c/p\u003e\r\n\r\n\u003cp\u003eComenzarás aprendiendo Python, el lenguaje más popular para la ciencia de datos. Luego desarrollarás habilidades para el análisis de datos y la visualización de datos, y también obtendrás una introducción práctica al aprendizaje automático. Finalmente, aplicarás y demostrarás tu conocimiento de la ciencia de datos y el aprendizaje automático con un proyecto final que usa un problema comercial de la vida real.\u003c/p\u003e\r\n\r\n\u003cp\u003eEste programa es impartido por expertos y se enfoca en el aprendizaje práctico y la preparación para el trabajo. Como tal, trabajarás con conjuntos de datos reales y se te dará acceso sin cargo a herramientas como las notebooks Jupyter en IBM Cloud. Utilizarás kits de herramientas y bibliotecas populares de Python como pandas, numpy, matplotlib, seaborn, folium, scipy, scikitlearn y más.\u003c/p\u003e\r\n\r\n\u003cp\u003e¡Comienza a desarrollar habilidades analíticas y de datos hoy, empieza tu carrera profesional en la ciencia de datos!\u003c/p\u003e555:T659,\u003cp\u003e\u003cspan lang=\"EN-SG\"\u003eThe New Space sector is a fast-growing market, boosting the historical institutional space exploration economy. The roughly 366 billion USD global space industry in 2019 is estimated to surge to over $1 trillion by 2040. The current growth in the development of space infrastructures and production of data from space is opening a full range of new applications for new customers. New business models for space encompass launchers, satellite manufacturers, in-orbit servicing, telecommunication, cybersecurity, earth observation analytics, and many more.\u003c/span\u003e\u003cspan lang=\"EN-SG\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eSpace4Impact, eSpace, E2MC and Space Innovation joined forces to launch this online course on New Space Economy. The course contains more than 30 videos from space experts from various space domains, including professors, scientists, representatives of public institutions and international organizations, entrepre"])</script><script>self.__next_f.push([1,"neurs, and investors.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eJoin the new space boom. Whether you are an entrepreneur, student or career jumper, this course gives you a detailed overview of the new space economy. \u003cspan lang=\"EN-SG\"\u003eYou will learn about current and future space infrastructures that enable various applications, such as telecommunication, broadcasting, geolocation, Earth Observation or in space manufacturing. You will learn how space data can enable new products and services to final customers on Earth. Last but not least, you will learn about how space can make Earth more sustainable and how to make sure to keep space sustainable in return.\u003c/span\u003e\u003c/p\u003e556:T94b,"])</script><script>self.__next_f.push([1,"\u003cp\u003eQuesto corso è rivolto agli utilizzatori di Tableau che hanno maturato una solida conoscenza del software nei corsi di livello base e intermedio. Nei precedenti moduli, abbiamo avuto modo di imparare ad analizzare i dati e a costruire delle visualizzazioni che aiutano nella scoperta di informazioni utili. Una volta apprese queste conoscenze, possiamo estenderci verso una comprensione più approfondita della Business Intelligence, vista sotto la lente di Tableau e non solo. Questo corso, di livello avanzato, introduce cinque nuovi moduli, suddivisi come descritto di seguito.\u003c/p\u003e\n\u003cp\u003eIl modulo 1 è dedicato alle Dashboard e alle Story. Una volta che abbiamo creato le nostre visualizzazioni di dati nei fogli di lavoro, possiamo combinarle in modo interattivo in questi due ambienti, in modo da far emergere informazioni utili sotto diversi punti di vista.\u003c/p\u003e\n\u003cp\u003eIl modulo 2 è pensato per individuare tutti gli elementi relativi alla data visualization. Come sappiamo, la rappresentazione dell’informazione non riguarda aspetti puramente grafici, ma include tutte le scelte che un’analista deve essere in grado di fare per comunicare al meglio i risultati delle proprie analisi. Per questo, il modulo mescola conoscenze teoriche e applicazioni pratiche dei principi della data visualization, fondamentali per la progettazione, la costruzione e la comunicazione dei dati in ambito BI.\u003c/p\u003e\n\u003cp\u003eNel modulo 3 avremo modo di imparare a costruire visualizzazioni avanzate su Tableau. Infatti, le visualizzazioni native di Tableau sono solo alcune di quelle utilizzabili per rappresentare i dati. Combinando creatività e conoscenza approfondita dello strumento, possiamo costruire visualizzazioni originali ed efficaci.\u003c/p\u003e\n\u003cp\u003eIl modulo 4 introduce le conoscenze utili a lavorare con Tableau Server e Tableau Online. Una volta creata una dashboard potremmo voler condividere il lavoro fatto con altri membri della nostra organizzazione. Ci spostiamo quindi dall’ambiente di creazione delle analisi (Tableau Desktop) a quello di condivisione.\u003c/p\u003e\n\u003cp\u003eInfine, il modulo 5 è indirizzato all’apprendimento di Tableau Prep, il prodotto della suite Tableau dedicato alla preparazione dei dati. Basato sulla programmazione visuale, Tableau Prep permette agli utenti di disegnare passo dopo passo una data source ottimizzata a seconda delle proprie necessità analitiche.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"557:T107f,"])</script><script>self.__next_f.push([1,"\u003cp\u003eVocê sabe qual é o seu estilo de liderança? Gostaria de desenvolver ou fortalecer habilidades de liderança com uma abordagem de gênero? Você sabe como os países da América Latina e o Caribe estão se saindo em termos de liderança feminina ou qual a importância de mais mulheres em cargos de tomada de decisão na região? Você gostaria de saber quais são os desafios e as oportunidades para as mulheres nesta era e quais habilidades e estratégias você deve desenvolver para fortalecer suas competências de liderança?\u003c/p\u003e\n\u003cp\u003eEmbora as mulheres representem 60% dos formandos do ensino superior e das universidades na América Latina e o Caribe (UNESCO, 2018), apenas 8,3% dos cargos de diretoria corporativa são ocupados por mulheres. Nesse contexto, é mais importante do que nunca aprimorar suas habilidades de liderança para não ficar para trás. \u003cspan lang=\"EN-US\"\u003e\u003ca href=\"https://idbg.sharepoint.com/sites/Proyectoe-learningMujeresyliderazgo/Shared%20Documents/General/Portugues/Edici%C3%B3n%202023/07%20-%20Producci%C3%B3n/01.%20Traducciones/02.%20Material%20revisado%20por%20COF/06.%20CAP%20Liderazgo%20femenino_pt-BR.docx#_msocom_1\"\u003e[AC1]\u003c/a\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eNeste curso, você analisará o tipo de liderança que o ambiente atual em constante mudança exige e será capaz de autoavaliar como está gerenciando seu estilo e ajustá-lo quando necessário. Você também descobrirá como pode se beneficiar de um processo de mentoria para ajudá-la a projetar sua carreira. Você terá à sua disposição ferramentas práticas para se conhecer melhor, o que a ajudará a desenvolver sua \"marca pessoal\" e usá-la em espaços que lhe proporcionarão novas oportunidades de desenvolvimento. Para aumentar seu sucesso no curso, você aprenderá a desenvolver sua inteligência emocional e suas habilidades de comunicação a fim de promover sua assertividade, autoconfiança e imagem pública, bem como sua capacidade de se adaptar às mudanças, persuadir os outros e superar as adversidades.\u003c/p\u003e\n\u003cp\u003eAproveite a oportunidade para impulsionar sua carreira e se tornar uma líder. Com apenas 2 horas por semana, de onde e quando for conveniente, você pode aprender a desenvolver suas habilidades de liderança e dar um impulso à sua carreira.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEsse curso é \"ao seu próprio ritmo\"\u003cem\u003e(self-paced)\u003c/em\u003e , portanto, você pode se inscrever a qualquer momento, mesmo que tenha sido aberto alguns dias antes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eo Se você optar pela \u003cstrong\u003emodalidade \"\u003c/strong\u003e \u003cstrong\u003eAssistente/Ouvinte\"\u003c/strong\u003e , terá acesso ilimitado ao conteúdo do curso, mas não poderá realizar as atividades avaliadas nem obter o certificado.\u003c/p\u003e\n\u003cp\u003eo Se optar pela \u003cstrong\u003emodalidade “Certificado verificado”\u003c/strong\u003e , você terá acesso ilimitado ao curso e poderá concluir as avaliações pontuadas até a data de encerramento, 18 de fevereiro de 2022, com o pagamento de uma taxa de US$ 29. Assim, se for aprovada, além do \u003cstrong\u003ecertificado verificado\u003c/strong\u003e , você receberá uma \u003cspan lang=\"EN-US\"\u003e\u003ca href=\"https://cursos.iadb.org/es/indes/credenciales-digitales\"\u003ecredencial digital\u003c/a\u003e\u003c/span\u003e **** que permite transformar a maneira como você compartilha suas conquistas acadêmicas e profissionais, por exemplo, em suas redes sociais.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVocê conhece os apoios financeiros para ter acesso ao certificado verificado?\u003c/strong\u003e ****\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"es\"\u003e1.\u003c/span\u003e \u003c/strong\u003e\u003cstrong\u003eAjuda programa IDBx\u003c/strong\u003e ****\u003c/p\u003e\n\u003cp\u003eVocê sabia que 74,48% das alunas aumentaram sua liderança e influência em seu ambiente de trabalho? Se você concluir o curso e atender a determinados requisitos relacionados às necessidades específicas de treinamento ou de acesso a recursos de determinados públicos do Banco, poderá receber a bolsa de estudos do IDBx para obter o certificado verificado e o crachá digital gratuitamente!\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Ajuda da edX\u003c/strong\u003e : a edX oferece assistência financeira aos alunos que têm dificuldades para fazer o pagamento. Registre-se no curso e preencha esta \u003cspan lang=\"EN-US\"\u003e\u003ca href=\"https://courses.edx.org/login?next=/financial-assistance/\"\u003e\u003cspan lang=\"PT-BR\"\u003esolicitação de assistência financeira\u003c/span\u003e\u003c/a\u003e.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"558:T591,\u003cp\u003eDid you know your best opportunities for growth may not lie solely in developing new \u0026ldquo;blockbuster\u0026rdquo; products or services? They may instead be found by focusing on your existing best customers \u0026mdash; and finding new customers with similar behavioral tendencies. Created by Professor Peter Fader, a world-renowned thought leader on marketing analytics and co-founder of Zodiac, a predictive analytics solution built on the breakthrough consumer behavior models developed by Professor Fader, this marketing course is designed to help you identify your most valuable customers and maximize their strategic value.\u003c/p\u003e\r\n\u003cp\u003eYou might have the data and the technology to track your best customers, but how can you meaningfully differentiate them from the rest? How do you align your operations around them? And how do you create and sustain competitive advantages from such practices? In this course, you\u0026rsquo;ll radically rethink how you develop and implement customer-centric strategies that you can apply to your existing customers today. You\u0026rsquo;ll also gain valuable insights into how to apply performance metrics and rethink product development processes in order to meet the needs of your most valuable customers.\u003c/p\u003e\r\n\u003cp\u003e\u003cem\u003eThis course is part of Wharton's Digital Marketing Professional Certificate. For more information, \u003ca href=\"https://www.edx.org/digital-marketing-professional\"\u003esee here\u003c/a\u003e.\u003c/em\u003e\u003c/p\u003e559:T86c,"])</script><script>self.__next_f.push([1,"\u003cp\u003eEn un futuro, el concepto de Internet de las Cosas en la industria tendera a ser tan grande que se espera que en el 2035 hayan mas de 50 billones de dispositivos inteligentes interconectados entre si a traves de multiples redes de comunicación, creando un mercado para la industria de entre 14 y 15 trillones de dolares (Khodadadi, Dastjerdi, \u0026amp; Buyya, 2016). Con el gran crecimiento de la tecnología en los ultimos años, las industrias se encuentran en una constante transformación tecnológica.\u003c/p\u003e\n\u003cp\u003eEl paradigma de Internet de las Cosas hace parte de las recientes tecnologías que propenden ayudar a mejorar la atención médica y los procesos industriales que requieren una monitorización en tiempo real. Para lograr este objetivo, es fundamental integrar de manera efectiva la información proveniente de las fuentes heterogéneas (dispositivos) de tal forma que se puedan compartir los datos obtenidos manteniendo su seguridad y privacidad, utilizar herramientas de análisis de datos avanzados y extraer información relevante con el fin de obtener el mejoramiento continuo en diferentes campos de la ciencia y tecnología (Dridi, Sassi, \u0026amp; Faiz, 2018).\u003c/p\u003e\n\u003cp\u003eEste curso online busca que las personas interesadas se enfrenten a la experiencia de un aprendizaje activo en temas relacionados con la ciencia, tecnologia, desarrollo de sistemas escalables y el uso de software libre utilizando el concepto de Internet de las Cosas; promoviendo conceptos fundamentales que las personas deberan poseer en un futuro para ser competitivos en sus trabajos. El curso busca que los estudiantes se enfrenten a problemáticas reales, a las cuales se les dara una solución implementando adecuadamente el concepto de Internet de las Cosas.\u003c/p\u003e\n\u003cp\u003eEste curso mostrara el alcance que tiene el Internet de las cosas en la actualidad. Este curso esta diseñado para que en cada una de las secciones se presente un componente teórico acompañado de una práctica real en donde el participante del curso pueda aplicar los conocimientos adquiridos en cada sección. Este curso ayuda a desarrollar las competencias que requieren las industrias 4.0.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"55a:T720,\u003cp\u003eGrâce à ce cours, à travers des vidéos et des cas réels, vous renforcerez votre capacité à utiliser, comprendre et interpréter les données. Vous apprendrez ainsi à définir le problème de recherche, à identifier les méthodologies de collecte et d'analyse des données, à interpréter les graphiques et à acquérir les bases pour soutenir le processus de prise de décision en matière de gestion publique avec des données vérifiables.\u003c/p\u003e\n\u003cp\u003eCe cours est \" \u003cem\u003eself-paced\u003c/em\u003e \", vous pouvez donc vous inscrire à tout moment, même s'il est ouvert depuis un certain temps.\u003c/p\u003e\n\u003cp\u003eSi vous optez pour le \u003cstrong\u003emode assistant\u003c/strong\u003e , vous pourrez le suivre gratuitement, mais vous n'aurez pas accès aux activités notées et vous ne pourrez pas obtenir de certificat à l'issue du cours.\u003c/p\u003e\n\u003cp\u003eSi vous optez pour le \u003cstrong\u003emode certificat vérifié\u003c/strong\u003e , vous pouvez accéder au cours de manière illimitée et effectuer les évaluations notées jusqu'à la date de clôture, après avoir payé un montant de 25 USD. De cette manière, si vous réussissez, en plus du certificat vérifié, vous obtiendrez un badge numérique qui vous permettra de transformer la manière dont vous partagez vos réalisations académiques et professionnelles, par exemple sur les médias sociaux.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConnaissez-vous l'aide financière d'edX pour obtenir le certificat vérifié et le badge numérique?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEdX offre une aide financière aux étudiants qui ont des difficultés à payer. Inscrivez-vous au cours et remplissez le \u003cstrong\u003e\u003ca href=\"https://courses.edx.org/financial-assistance/\" rel=\"noopener\" target=\"_blank\"\u003eformulaire de demande d'aide financière\u003c/a\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eVous trouverez plus d'informations dans la section \"Foire aux questions\" ci-dessous.\u003c/li\u003e\n\u003c/ul\u003e55b:T622,\u003cp\u003eAs the world becomes more data driven, future-focused professionals need to develop the quantitative skills to inform corporate decision-making and managerial strategy. Guided by experts from the London "])</script><script>self.__next_f.push([1,"School of Economics and Political Science (LSE), this online certificate course empowers you with the knowledge and practical tools to understand, interpret, and communicate data relevant to your role and organization. \u003c/p\u003e\n\u003cp\u003eDevelop an understanding of how data-driven models can improve your ability to make decisions in a fast-paced world. Over the course of eight weeks, you’ll participate in a capstone project and apply the techniques and concepts covered to extract business insights from a real data set. You’ll also gain experience using Tableau —– a leading business intelligence and data analytics software —– to visualize and report on insights extracted from data sets. \u003c/p\u003e\n\u003cp\u003eThis LSE course will equip managers and analysts in a variety of industries with the skills to make data-driven decisions. Marketing and sales analysts will gain the ability to extract and interpret key business insights for competitive advantage. Similarly, finance, HR, and business analysts will develop data analysis skills that can be directly applied in their role and organization. There are no formal prerequisites for this course, but some numerical literacy is advantageous, as well as a basic working knowledge of Microsoft Excel. You’ll be granted a student license to download and use Tableau free of charge, for the duration of the course.\u003c/p\u003e55c:T92c,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThe convergence of various fields, such as data mining, statistics, optimisation, and computing, has given rise to machine learning, which is rapidly gaining adoption across diverse industries. One of the key advantages of machine learning is its capability to handle large datasets and solve complex problems. From commercial applications like search engines and recommendation systems (think Netflix and Amazon) to financial institutions for predicting customer behaviour, compliance, risk, and algorithmic trading, machine learning is proving to be a game-changer.\u003c/p\u003e\n\u003cp\u003eIf you're looking to upskill in machine learning and its applications, the London School of Economics and Political Science (LSE) offers an eight-week online technical course that covers a comprehensive range of machine learning methods. Through practical case studies and hands-on exercises, you'll learn how to apply machine learning models to real-world problems and interpret the resulting predictions to make informed business decisions. The course is designed to help you build your expertise in modern business analytics and gain valuable insights into how machine learning is used today.\u003c/p\u003e\n\u003cp\u003eIf you are a mid to senior manager, data specialist, consultant, analyst, IT, or business professional looking to integrate machine learning techniques to improve data analytics in your organization, this online certificate course is designed for you. This course offers an in-depth exploration of core principles and machine learning methods, which will benefit those interested in practical applications. Whether you are looking to upskill, transition into a data science role, or improve your understanding of business applications of data science, this course will help develop and validate your practical machine learning skills and knowledge.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePrerequisites\u003c/strong\u003e\u003c/em\u003e \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThis course is technical in nature. It makes use of coding in R and covers the application of machine learning in business. Some algebraic and calculus knowledge is strongly advised, but is not required. Training in tertiary-level statistics and knowledge of a functional or object-oriented language are advantageous. HTML is not considered a programming language in this context. No specific software is required for this online certificate course.\u003c/em\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"55d:T6c8,\u003cp\u003eThanks to machine learning and data analytics, real estate professionals and investors can now make more accurate property assessments than ever before. This Data Science in Real Estate online short course from the MIT School of Architecture and Planning (MIT SA+P) focuses on growing your data science skills within the context of the built environment. During the program, esteemed MIT faculty and industry experts will teach you how to harness statistical techniques to reveal key insights into the factors that impact property investment and development opportunities.\u003c/p\u003e\n\u003cp\u003eOver six weeks, you’ll learn how to evaluate and tidy data, expand data sets, and generate a selection of models that can be used to explain industry trends and forecast real estate values.\u003c/p\u003e\n\u003cp\u003eThis MIT SA+P program provides learners with the data science skills to support decision making and grow a successful property portfolio. Those with experience in data analytics will benefit by learning to apply their skills to the real estate market, while real estate professionals and independent investors can increase their competitive edge by building data skills to improve analysis and valuation, helping them to make better decisions.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWhile there are no formal prerequisites for this course, it’s highly recommended that you have a basic understanding of programming, in particular using R. You should be familiar with different data types and basic data structures, including objects, functions, vectors, matrices, data frames, and factors; and have used them practically in the past. If you don’t yet have an understanding of these concepts, you can explore the bridging resources made available in the course brochure.\u003c/em\u003e\u003c/p\u003e55e:T48c,\u003cp\u003eAs the amount of data in the world grows, so does the demand for skilled executives who are equipped with the knowledge to lead a data-driven organization. This program aims to teach managers and executives the strategic, technical, and managerial skills to integrate data analytics into an "])</script><script>self.__next_f.push([1,"organization. Drawing on case studies from the sports industry, which has led the way in performance analytics, you’ll learn how to use data in your organization for improved decision making, goal setting, selection of appropriate technologies, structuring of teams, and communication. Guided by MIT faculty and industry thought leaders, this program helps you navigate the process of data transformation in your business or organization, from concept to implementation.\u003c/p\u003e\n\u003cp\u003eThis MIT Sloan course aims to guide you to lead a data-driven transformation in your company. With an emphasis on process, management, and innovation, this program is designed to help business owners, leaders, and aspiring analysts understand the core elements of data analytics from a business perspective and harness it to gain a competitive edge. No technical experience is required.\u003c/p\u003e55f:T66f,\u003cp\u003eIn a world that’s increasingly data-driven, organizations need professionals who can extract meaningful insights from data to make better business decisions. \u003c/p\u003e\n\u003cp\u003eOn the Data Science with Python online short course from the University of Cape Town (UCT), you’ll have the opportunity to develop practical data science and analysis skills for use in everyday business scenarios. Over the course of eight weeks, you’ll cover widely applicable Python libraries and learn how these methods can be, and are, used in day-to-day business situations. \u003c/p\u003e\n\u003cp\u003eGain an introduction into statistical learning, which will provide a foundation on the mechanics of machine learning. You’ll explore supervised learning using tree-based models and neural networks, as well as unsupervised learning using K-means and hierarchical clustering. You’ll also learn about the process of revealing more robust patterns to ensure models are useful. \u003c/p\u003e\n\u003cp\u003eThis course is aimed at professionals who want to close any gaps they may have in their data science skills and knowledge. IT professionals who need to rapidly enhance their data science toolkit with demonstrable and pract"])</script><script>self.__next_f.push([1,"ical skills would benefit from the technical nature of the content. Professionals working in a variety of industries will learn how to increase efficiencies and identify new opportunities for their organization with key data and programming skills. \u003c/p\u003e\n\u003cp\u003eThis course is technical in nature. It is strongly recommended that you have a basic understanding of mathematics, statistics, and at least one programming language if you wish to reap the full benefits of the course.\u003c/p\u003e560:T610,\u003cp\u003eAs the increase in data changes the way in which organizations operate, professionals with a hybrid blend of analytical skills and domain-specific expertise are required to propel data-driven business decisions. In the Rice University Data Analysis and Visualization online short course, you’ll develop the skills and knowledge to effectively analyze, visualize, and communicate data insights within your organization. \u003c/p\u003e\n\u003cp\u003eGuided by industry experts, you’ll discover how to navigate technical tools such as Tableau, SQL, and VBA. Learn to harness the fundamentals of data analysis such as advanced Excel functions, databases, and practical statistics. Through data-driven insights and effective visualization techniques, you’ll learn to communicate effectively with stakeholders through data storytelling.\u003c/p\u003e\n\u003cp\u003eThis course is designed for professionals in data-driven roles, for decision makers who need to leverage data effectively, and for those interested in moving into these spaces. Functional analysts will further their ability to inform specific functions and business decisions, while managers will benefit from the ability to use analytics tools in their decision making. Any professional interested in taking the first steps toward an analytical role will also benefit from a new understanding of data analysis and visualization.\u003c/p\u003e\n\u003cp\u003eA basic proficiency in Excel is recommended, and while there are no prerequisites for this course, it’s recommended that students have a basic understanding of SQL before registering.\u003c/p\u003e561:T684,"])</script><script>self.__next_f.push([1,"\u003cp\u003eWith more and more businesses relying on statistical analysis to drive decision-making, the ability to extract meaning from data has become a highly valued skill. Data analysts play a key role in helping business leaders make informed choices, distinguish between effective and ineffective practices, cut costs, and solve key problems — they are, therefore, an integral part of any successful organizational team.\u003c/p\u003e\n\u003cp\u003eThe Data Analysis online short course from the University of Cape Town (UCT) will introduce you to the fundamentals of data analysis and the broader potential of analytics in business. Over the course of eight modules, you’ll learn how data is collected, stored, organized, analysed, and interpreted, and you’ll be given the chance to practice data analysis techniques on real-world data sets. Ultimately, you’ll walk away with practical skills that you can apply immediately in your organization to address specific business needs in the areas of finance, sales, marketing, operational management, and human resource management.\u003c/p\u003e\n\u003cp\u003eAnyone interested in moving into the realms of data science and analytics will find this UCT online short course useful. The content is geared towards those eager to update their skill sets to remain relevant, as well as established professionals looking to unlock new opportunities within or outside their current organizations. Data analysts, and others who already work with data, can add to existing certifications by learning how to better leverage numbers to achieve results and drive growth. Students will need access to Microsoft Excel to complete the practical components of the course.\u003c/p\u003e562:Ta49,"])</script><script>self.__next_f.push([1,"\u003cp\u003eDuration: 6 weeks (excluding orientation)\u003c/p\u003e\n\u003cp\u003eEffective data visualisation and storytelling has the power to transform complex data into impactful insights that guide informed decision-making. This crucial skill enables businesses to spot patterns, make strategic decisions, and predict future trends, helping them to build data-driven growth and success.\u003c/p\u003e\n\u003cp\u003eUnearth the power of data with the \u003cstrong\u003eApplied Data Visualisation and Analysis for Business\u003c/strong\u003e online certificate course from the London School of Economics and Political Science (LSE). Over six weeks, you'll learn the art of converting raw data into captivating visual narratives using Tableau. You’ll also be armed with a suite of econometric and statistical modelling tools in Microsoft Excel. Guided by experts, you’ll gain a deep understanding of the technical details that support effective data visualisations and the expertise needed to thrive in a data-centric environment. \u003c/p\u003e\n\u003cp\u003eThis course is intended for business professionals seeking to boost their technical data visualisation and analysis skill set and grow their ability to influence decision-makers through data storytelling. It's also appropriate for those wishing to enhance their mastery of data visualisation using Tableau and data modelling using Excel. \u003c/p\u003e\n\u003cp\u003eThose interested in the course should ideally be familiar with Tableau basics (the interface, connecting to datasets, and creating basic visualisations) and introductory statistical concepts. There will, however, be a light refresher on these concepts in the course material.\u003c/p\u003e\n\u003cp\u003eIf you don’t have these skills yet, or feel you could benefit from a more-in depth refresher, the LSE \u003ca href=\"https://www.getsmarter.com/products/lse-data-analysis-for-management-online-certificate-course\" target=\"_blank\"\u003eData Analysis for Management\u003c/a\u003e online certificate course can provide you with Tableau fundamentals.\u003c/p\u003e\n\u003cp\u003e\u003cimg alt=\"\" src=\"https://www.getsmarter.com/disk/public/tVYZwNpzsTmzB9pbhBDXkJM1/cpd_accreditation_logo.png\" /\u003e \u003c/p\u003e\n\u003cp\u003eThis Applied Data Visualisation and Analysis for Business online certificate course is certified by the United Kingdom CPD Certification Service, and may be applicable to individuals who are members of, or are associated with, UK-based professional bodies. The course has an estimated 54 hours of learning.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: should you wish to claim CPD activity, the onus is on you. The London School of Economics and Political Science (LSE) and GetSmarter accept no responsibility, and cannot be held responsible, for the claiming or validation of hours or points.\u003c/em\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"563:T68f,\u003cp\u003eThe transformative power of data and analytics has never been more important than it is today. Companies increasingly need to deliver insights on customer experience and business trends to remain competitive in the market.\u003c/p\u003e\n\u003cp\u003eThe Business Analytics online short course from the University of Cape Town (UCT) aims to equip you with advanced data analysis techniques to support business decision making and critical thinking in times of ambiguity and uncertainty.\u003c/p\u003e\n\u003cp\u003eThis course focuses on practical business application by showing you how to harness data visualization and storytelling to interpret number sets, identify patterns, and uncover business trends critical to driving growth in your organization.\u003c/p\u003e\n\u003cp\u003eBy practically engaging with various analytical tools (such as SQL and Tableau, as well as Python on an introductory level), this course will empower you to tap into the power of business intelligence to transform your data and deliver actionable insights.\u003c/p\u003e\n\u003cp\u003eAs this is an advanced course, it’s ideal for practicing analysts who want to update their knowledge, grow their careers, and improve their ability to impact business decisions with data insights. It’s also designed for those who aspire to become data scientists and for managers and senior decision makers who want to learn how to leverage data to make informed choices and add more value to their organizations. While there are no prerequisites for this course, it’s recommended that learners have a basic understanding of statistics and analytics before registering. Notably, students will engage with and run code via an IDE notebook but won’t have to write code themselves.\u003c/p\u003e564:T993,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIn today’s fast-paced world, businesses that fail to leverage technology and adapt to changing conditions are less likely to reach their objectives. This has led to a greater demand for professionals who can identify the need for change and facilitate the design and implementation of process solutions. Additionally, business systems analysts play a vital role in bridging IT and leadership teams by guiding management toward understanding technical solutions, and equipping IT professionals to identify organisational goals.\u003c/p\u003e\n\u003cp\u003eThe University of Cape Town (UCT) Business Systems Analysis online short course explores how a variety of business systems analysis theories can be used to examine existing processes and implement change in a practical, effective way. Gain the skills to leverage IT business systems and technological tools as an analyst, and impact your organisation’s overall success and efficiency. Over the course of 10 weeks, you’ll learn the fundamentals of business systems analysis, identify system requirements, and translate these into project specifications and user-friendly solutions that support the strategic objectives of a business.\u003c/p\u003e\n\u003cp\u003eThis course is suitable for individuals who are new to business systems analysis as well as experienced employees who would like to formalise their expertise and keep their skills current in a competitive environment. Established IT professionals and business systems analysts will be able to sharpen their tool kit and earn a certificate of completion online from Africa’s leading university.1 Additionally, those individuals who are outside of the field but eager to leverage technical and business-oriented strategies to transform the way their company operates will find this course especially valuable. \u003c/p\u003e\n\u003cp\u003eThrough this course, professionals can enable career development by building practical techniques such as data modelling to design IT systems, data analysis to understand vital trends, and stakeholder communication to align and achieve business objectives. \u003c/p\u003e\n\u003cp\u003eThere are no formal prerequisites for this course, and the flexible online learning framework ensures that students are adequately supported to complete their studies on a part-time basis. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1\u003ca href=\"https://www.topuniversities.com/university-rankings-articles/world-university-rankings/top-universities-africa\" target=\"_blank\"\u003e QS Top Universities\u003c/a\u003e (Apr, 2021).\u003c/em\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"565:T8c4,"])</script><script>self.__next_f.push([1,"\u003cp\u003eRapid digitization and questions of data privacy have led to the development of data protection laws across the globe, including the General Data Protection Regulation (GDPR). Designed to modernize laws that protect the personal information of individuals, these new data regulations impact how businesses and public sector organizations should handle customer and stakeholder information. The Data: Law, Policy and Regulation online certificate course from LSE explores the role of law in the digital space by contextualizing legal principles and concepts related to data and technology regulation. Ultimately, it empowers learners with the skills to navigate the changing legal environment shaping data law and policy. \u003c/p\u003e\n\u003cp\u003eThrough a socio-legal lens, you’ll engage critically with topical case studies and current data regulations, and gain insight into the legal challenges posed by digitization. Developed by leading academics from LSE’s Law School, the content draws on the strengths of the school’s innovative research and academic excellence to immerse you in pressing legal debates in data protection and algorithmic regulation. By the end of this online course, you’ll have gained the skills to think critically about the relationship between technology, policy, and the law. You’ll also walk away with a better understanding of the issues of digital data ownership and exploitation.\u003c/p\u003e\n\u003cp\u003eThis course is aimed at professionals looking to explore the global data protection concerns driving new internet and technology regulations. It’s designed to be open and applicable to people from various geographical or jurisdictional contexts eager to understand the core legal concepts and debates around data law and regulation, as well as data-driven decision making. Offering practical applications for both legal and business professionals, this course is relevant to business leaders, data analysts, and information technologists looking to gain expert perspectives on this current and critical issue. For legal consultants, public policy specialists, and law graduates, the course offers the opportunity to grow your knowledge in tech-driven policymaking, data privacy and protection, and data regulation issues.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"566:T560,\u003cp\u003eEase of use is now a vital feature of an effective digital environment. For a website or app to holistically serve its purpose, it needs to offer customers a seamless, intuitive user experience (UX). \u003c/p\u003e\n\u003cp\u003eThe University of Cape Town (UCT) User Experience Design online short course enables you to understand how users interact with online content so that you can design environments that meet their evolving needs. You’ll discover key UX research and design techniques, and learn how to work with stakeholders, run user tests, design wireframes, and develop prototypes. You’ll also analyze current trends in the field, and map out a professional journey for yourself so that you can better leverage your new skills to launch a career in UX.\u003c/p\u003e\n\u003cp\u003eThis course is suitable for anyone who wants to boost their understanding of online user behavior and walk away with tools to improve the usability of a website or app. The content is ideal for professionals who need to develop highly transferable, practical UX design skills in order to improve performance in their current position or who wish to prepare for future career growth. Those already in a design, digital marketing, product ownership, or web development role will further expand their knowledge and validate their existing expertise. No prior technical knowledge of coding is required for this course.\u003c/p\u003e567:T59f,\u003cp\u003eThe digital age has revolutionized the way that organizations do business. Unfortunately, however, it has also exposed companies, and their customers, to many new risks. With cyberattacks now a real threat in Africa and beyond, there’s a need across industries for professionals with the skills to address online security vulnerabilities.\u003c/p\u003e\n\u003cp\u003eThe University of Cape Town (UCT) Fundamentals of Cybersecurity online short course introduces you to the key elements of cybersecurity management, from both a South African and international perspective. Learn to become the defender of sensitive company data and learn how to make sound decisions to su"])</script><script>self.__next_f.push([1,"pport your organization in both thwarting attacks and responding appropriately to incidents.\u003c/p\u003e\n\u003cp\u003eThis course is beneficial for employees at all levels, both technical and managerial, who currently work within cybersecurity. Those new to the industry or within administrative or information security roles will gain the latest knowledge of cybersecurity and develop a competitive edge within the growing job market. Management professionals will explore the regulations and legislation that affects cybersecurity, and learn to develop dedicated cybersecurity training programs and incident policies. All professionals will have the opportunity to gain the confidence and skills to identify threats and implement the necessary measures to protect an organization from cyber risks.\u003c/p\u003e568:T54b,\u003cp\u003eArtificial intelligence (AI) is already having a major impact across the economy, coursing in short order across the global business environment. To truly understand the potential it holds, you first need to understand how the technology works. The Oxford Artificial Intelligence Programme aims to provide you with a sound framework of this technology: from its history, functionality, and capability, as well as its inherent ethical challenges. Over the course of six short weeks online you’ll develop an informed opinion about AI's applications with insights from University of Oxford faculty. Reflect on the implications and opportunities of AI, and build a business case for its implementation within your unique organizational context. \u003c/p\u003e\n\u003cp\u003eThis programme is designed for managers and business leaders across multiple functions and industries looking to understand the workings and possibilities of AI. It’s also applicable for technical working professionals such as CIOs, IT managers, and business analysts who want to better understand how artificial intelligence can be implemented within their organizations. \u003c/p\u003e\n\u003cp\u003eYou’ll get the opportunity to learn about artificial intelligence online, and develop a foundational understanding "])</script><script>self.__next_f.push([1,"of how it works. However, as this is a non-technical programme, you will not be required to code.\u003c/p\u003e569:T797,\u003cp\u003eCRISPR shares the same space as blockchain and IoT payments as one of the most disruptive innovations of recent years. This gene-editing tool has already impacted industries such as biofuel, agriculture, and healthcare, with its global market potential growing exponentially.\u003c/p\u003e\n\u003cp\u003eThe CRISPR: Gene-editing Applications online short course from Harvard's Office of the Vice Provost for Advances in Learning (VPAL), in association with HarvardX, provides you with a comprehensive understanding of the CRISPR Cas9 gene-editing technology, its vast applications, and the market opportunities that genetic engineering presents. With a specific industry focus and through real-world case studies, you’ll explore the applications of CRISPR in biotechnology and how it will assist the development of disease-resistant cultivars, improve food yields, and allow biofuels to become a viable alternative energy source. You’ll analyze the possibilities of curing inherited genetic disorders, treating infectious diseases, and advancing the fight against cancer. Equipped with industry insight into the ethical implications of gene-editing, this course will give you a holistic understanding of the CRISPR landscape and its future potential. \u003c/p\u003e\n\u003cp\u003eThis is not a technical course and no lab skills or prior knowledge of genetic engineering is required to participate. This course gives a comprehensive overview of the CRISPR DNA editing landscape for investors or entrepreneurs looking for the knowledge needed to make strategic business decisions about this disruptive technology. Those working in biotechnology or pharmaceutical product development, sales, or manufacturing will also find value as the content explores the future applications of CRISPR across the economy. Professionals involved in policy and law can also benefit from building a greater understanding of the ethical considerations regarding CRISPR and genetic modification"])</script><script>self.__next_f.push([1,".\u003c/p\u003e56a:T556,\u003cp\u003eThe modern world is host to an enormous web of devices interconnected through sensors and networks — known as the Internet of Things (IoT). While IoT has already made its presence felt in the home through smart appliances, it represents a rich opportunity for business. \u003c/p\u003e\n\u003cp\u003eThe Internet of Things: Business Implications and Opportunities online short course from the MIT Sloan School of Management steps beyond the bounds of technical jargon to help you learn about the potential benefits and impact of IoT adoption on an organizational level. Through case studies, interactive media, and peer-to-peer learning, you’ll develop an understanding of the technologies and conditions necessary for successful integration of IoT in a business context. Ultimately, you'll gain the skills and knowledge to create a practical roadmap for the real-world implementation of IoT, and earn a digital certificate of completion from MIT Sloan.\u003c/p\u003e\n\u003cp\u003eThis online program is ideal for managers and aspiring leaders looking to learn about the business opportunities that IoT can offer. Operations managers, entrepreneurs, and business analysts will benefit from understanding the impact and applications of IoT. Software engineers, data analysts, and IT engineers will learn to better communicate the benefits of IoT, while gaining methods for effective implementation.\u003c/p\u003e56b:T70c,\u003cp\u003eThe ability to use data effectively is crucial to the success of most modern organizations. This includes optimizing processes using operational data, finding new customers through consumer data, and understanding the immediate business environment using competitor data. But it’s not enough to simply have data; nor is it enough to employ a team of analysts to sift through the multiple haystacks of raw information in search of a needle of insight. To effectively use data, organizations need professionals who are able to communicate and disseminate data-driven insights in order to properly guide decision making.\u003c/p\u003e\n\u003cp\u003eThe MIT Sloan Communicating D"])</script><script>self.__next_f.push([1,"ata Through Storytelling online short course teaches you how to construct stories from data that persuade, motivate, and inspire action. Discover how to tailor logically sound presentations for a variety of audiences, as well as understand the best practices for presenting data with clarity rather than complexity. In the program, you’ll delve into the TOP-T (topic, orientation, point, transition) framework, developed by MIT Sloan faculty to make complex data more understandable.\u003c/p\u003e\n\u003cp\u003eThis course is ideal for any professional who regularly presents data-driven insights, whether that role is technical or not. If you’re an analyst or analytics manager, the program will enhance your ability to communicate insights, particularly when presenting specialist information to general audiences. If you’re a manager, decision maker, or occupy a functional role, it equips you with the skills needed to tell a clear story with data that can be shared with key stakeholders in the organization. If you’re a consultant or entrepreneur, you’ll have the opportunity to learn practical techniques for persuading and influencing clients.\u003c/p\u003e56c:T686,\u003cp\u003eTraining artificial intelligence (AI) models has traditionally required large sets of annotated data. As a result, small, uncurated data — which carries untapped value — is often left untouched. By leveraging cutting-edge developments in machine learning (ML), you can unlock the latent potential of data from unused sources to create competitive opportunities for your business. \u003c/p\u003e\n\u003cp\u003eThe \u003cstrong\u003eUnsupervised Machine Learning: Unlocking the Potential of Data\u003c/strong\u003e online short course from the MIT Sloan School of Management and the MIT Schwarzman College of Computing explores the technical and strategic considerations of unsupervised learning approaches. In just six weeks, you’ll study these approaches, their capabilities, use cases, limitations, and applications. You’ll learn to see the potential of your data — no matter its quantity or quality — and deploy AI sol"])</script><script>self.__next_f.push([1,"utions tailored to your unique data, problem, and business. \u003c/p\u003e\n\u003cp\u003eThis program is designed to help business decision makers leverage opportunities created by unsupervised ML. Understanding how to leverage uncurated data, managers and technology leads will be able to plan new data acquisition protocols. IT and tech professionals looking for up-to-date developments in unsupervised learning will benefit from this program material, as will data scientists and analysts looking to strengthen their knowledge of the application of computer vision technologies.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThe ability to code is not a prerequisite for this program. However, this program contains complex concepts that require an existing understanding and above-average knowledge of machine learning.\u003c/em\u003e\u003c/p\u003e56d:T591,\u003cp\u003eMachine learning offers an opportunity to gain a powerful competitive edge in business and is increasingly becoming a priority for managers and executives. In the Machine Learning in Business online short course from the MIT Sloan School of Management and the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL), you’ll be guided to discover the business potential of machine learning while developing strategies for effective implementation.\u003c/p\u003e\n\u003cp\u003eThe program focuses on the managerial implications of machine learning and touches on certain technical aspects to provide you with the deeper knowledge needed to craft an effective machine learning integration strategy. Explore the value and impact of this technology, with insights from esteemed MIT faculty and machine learning experts. \u003c/p\u003e\n\u003cp\u003eAlthough not technical in focus, this online short course will be useful for leaders and decision makers who want to gain a grounding in machine learning to successfully integrate it into their organization. It’s also relevant for managers, data specialists, consultants, and business professionals interested in exploring the technology’s strategic implications. You’ll walk away with a sound understanding of the applications of machine"])</script><script>self.__next_f.push([1," learning in business, without needing to code or program. You’ll also explore the opportunities, capabilities, and scope of this transformative technology.\u003c/p\u003e56e:T9fd,"])</script><script>self.__next_f.push([1,"\u003cp\u003eChanging tenant expectations, sustainability concerns, and where people choose to live due to work-from-home protocols are reshaping the commercial and residential real estate markets. It requires data and quantitative analysis skills to predict the full impact of this on the property sector, and investment and development decisions. \u003c/p\u003e\n\u003cp\u003eThe Real Estate Financial Analytics online short course from the MIT School of Architecture and Planning (MIT SA+P) will enable you to transform uncertainty (‘unknown unknowns’) into risk (‘known unknowns’). You’ll gain the technical skills and tools to assess the viability of residential and commercial real estate investments using rigorous analysis techniques, simulation modeling, and financial modeling. This six-week program introduces real estate price dynamics and teaches you to quantitatively model them using Microsoft Excel, in order to understand some of the challenges of real estate price indexing. It also explores the lure and limitations of forecasting and how to make more accurate predictions using Monte Carlo simulation. \u003c/p\u003e\n\u003cp\u003eIn addition, you’ll explore how flexibility in the context of uncertainty can add value to investments, as well as assess the quantitative and qualitative value of real estate investment decision flexibility using simulation modeling. With guidance from MIT academics and industry experts, you’ll discover the potential for new financial engineering tools based on real estate price indexing.\u003c/p\u003e\n\u003cp\u003eThis program is designed to help equip finance and built environment professionals with the knowledge and techniques to navigate real estate investment decisions successfully. Whether for personal investment management or in a professional capacity, this course aims to provide you with the skills and tools to make better recommendations and projections, identify high-return investments, and build an enduringly strong portfolio.\u003c/p\u003e\n\u003cp\u003eBusiness leaders, financial executives, real estate entrepreneurs, developers, and fund managers and advisers will build on their current skill set, and gain insight into the latest investment trends and structures. Analysts — especially those looking to hone in-demand, specialist real estate expertise — will benefit from the expert thought leadership in areas such as property price dynamics and valuation in the capital market. Due to the technical nature of this course, it’s best suited to those with existing knowledge of finance or economic analysis and experience using Microsoft Excel.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"56f:T63e,\u003cp\u003eAn effective pricing strategy offers your organization a key advantage over its competitors. Understanding how to implement one is a valuable tool in an increasingly interconnected business landscape with a growing number of parties wanting their share of the market. \u003c/p\u003e\n\u003cp\u003eThe Pricing: Using Data to Improve Pricing Performance online short course from MIT Sloan School of Management equips you with the skills and understanding to accurately and effectively price new or existing products or services, with a focus on providing economic value to the customer. In the course, you’ll learn to adopt a data-driven approach that explores regression analysis, survey techniques, and conjoint analysis. You’ll also discover the limitations of historical data in the context of pricing, and develop techniques and guidelines that will enable you to execute a successful pricing strategy for a variety of products and industries, enabling you to have a measurable impact on your organization. \u003c/p\u003e\n\u003cp\u003ePricing is a timeless and adaptable skill that can be used to improve business performance in a wide range of industries. Whether you are introducing new products, facing price competition, or looking to improve the value provided to customers, this course will provide practical skills for any professional who plays a hands-on role within pricing strategy. CEOs and entrepreneurs, finance and sales executives, analysts, and product managers looking to grow their business and increase sales through better pricing will gain the ability to have a measurable impact on their organizations.\u003c/p\u003e570:T4f8,\u003cp\u003eFinancial modeling skills are used to forecast the financial performance of a business, or to compare it to its industry competitors. The Financial Modelling and Analysis online short course from the University of Cape Town (UCT) introduces you to the fundamental concepts required for Excel-based modeling and forecasting. On it, you’ll develop the skills to create a mathematical model in Microsoft Excel that reflects the hi"])</script><script>self.__next_f.push([1,"storical, current, or projected value or financial performance of an asset, stock, or investment. Guided by expert faculty, you’ll learn to recommend strategic business decisions with the use of financial modeling, and create a discounted cash flow analysis using financial forecasts. \u003c/p\u003e\n\u003cp\u003eBy the end of this course, you’ll have explored how to build revenue models, determine realistic forecast assumptions, and learned how historic data is forecast into the future. \u003c/p\u003e\n\u003cp\u003eAimed at existing and aspiring financial analysts, this course offers critical thinking opportunities and an objective lens for which to understand the financial characteristics and interpretations of businesses. With the ability to calculate, forecast, or estimate financial numbers, you’ll hone the quantitative skills needed to excel in this field.\u003c/p\u003e571:T48a,\u003cp\u003eMaking informed investment decisions in commercial real estate hinges on understanding micro and macroeconomic theories and frameworks, and using practical financial analysis tools. The Commercial Real Estate Analysis and Investment online short course from the MIT School of Architecture and Planning (SA+P) draws on real-world examples of real estate investment and development projects, with a focus on unique tools, terms, and theories that have been pioneered and developed by the Faculty Director, David Geltner. On completion of this course, you’ll be armed with the skills needed for real estate valuation, investment analysis, and decision making, and you’ll earn an MIT SA+P digital certificate, validating your newfound real estate finance and development knowledge.\u003c/p\u003e\n\u003cp\u003eThis online short course is geared toward finance, property investment, and real estate professionals who want to understand how to apply financial tools to analyze the commercial real estate market and make informed investment decisions. This program has a technical focus, and previous financial or real estate investment experience will significantly benefit you.\u003c/p\u003e572:T6ec,\u003cp\u003eIn the context of an incr"])</script><script>self.__next_f.push([1,"easingly connected and complex world, it is vital to be able to analyze and anticipate the impact of public policy and evaluate interventions to make more informed, evidence-based policy decisions and recommendations.\u003c/p\u003e\n\u003cp\u003eThe Public Policy Analysis online certificate course from the London School of Economics and Political Science (LSE) merges research, theory, and practice, as it empowers learners with analytical frameworks for understanding the policy process. Over the course of 10 weeks, you’ll gain the quantitative tools and techniques needed to conduct evidence-based public policy impact evaluations and develop effective policy communication skills. \u003c/p\u003e\n\u003cp\u003eBenefit from this multidisciplinary approach to public policy and practice your skills by engaging with real-world, global case studies. Guided by expert LSE faculty with topical research experience across a variety of policy areas in Europe, Africa, and the United States, you’ll develop practical skills and strategies for immediate application in policy-related projects, or within your organization.\u003c/p\u003e\n\u003cp\u003eThis course, developed by LSE’s School of Public Policy, is relevant for professionals working in public policy. Policy makers, civil servants, NGO staff, and individuals in public and private sector organizations seeking to influence public policy and better understand their current roles within policy formulation will find it beneficial. Those in the public sector, non-profit, consultancies, and think tanks – who want to enhance their impact by critically engaging with public policy, employing evidence-based impact evaluations, and developing the insight to make policy recommendations and decisions – will also benefit from this course.\u003c/p\u003e573:T643,\u003cp\u003eArtificial intelligence (AI) and machine learning (ML) technologies are having a significant impact on innovation, with pharma and biotech being the latest industry to be affected. With the connection between drug discovery, technology, and business now clear, leaders and scientists need to "])</script><script>self.__next_f.push([1,"become better equipped to leverage new developments in AI and ML. \u003c/p\u003e\n\u003cp\u003eIn the Artificial Intelligence in Pharma and Biotech online short course from the MIT Sloan School of Management, you’ll gain insight into how technology is currently being applied in the pharmaceutical industry. Over six weeks, you’ll explore ways that ML is driving new drug discovery, learn how AI can be used to create more accurate biological and generative modeling, and explore how ML can be used to streamline the design and management of clinical trials. You’ll walk away with an understanding of the benefits and challenges of these technologies, allowing you to make informed, innovative decisions within this rapidly evolving space.\u003c/p\u003e\n\u003cp\u003eIf you work in the pharmaceutical and biotech industry as a scientist, researcher, or specialist, you’ll learn about various AI and ML tools and how to apply them in your research and work. If you currently hold a leadership position in the pharma business sector, this program provides practical, strategic learnings that can be applied to the areas of drug discovery, clinical trials, and pharmaceutical sales. By bridging the gap between analysis and application, the information presented in this course will also benefit data scientists and analytics professionals.\u003c/p\u003e574:T6df,\u003cp\u003eDigital technologies have reshaped marketing, leading to a drastic shift in the quality and quantity of information we can store, access, and analyze. This proliferation of data allows businesses to better understand and react to consumer patterns. However, it also increases the need to evolve measurement, planning, and implementation of their marketing activity. Companies require skilled professionals who are equipped to ensure optimal return on investment (ROI) for marketing spend, and to deliver valuable insights that drive better customer service. \u003c/p\u003e\n\u003cp\u003eThe MIT Sloan Digital Marketing Analytics online short course unpacks how to leverage measurement and analysis within your digital marketing strategy. With a focu"])</script><script>self.__next_f.push([1,"s on analytics-based marketing, you will assess the latest applications of artificial intelligence, machine learning, and predictive modeling that will help drive ROI for campaigns. \u003c/p\u003e\n\u003cp\u003eDigital media and analytics tools have transformed traditional marketing theory and practice, making this course valuable to anyone working within the field or aspiring to a marketing career. This MIT Sloan digital marketing program is designed to give you the opportunity to close the gaps in your analytical knowledge and skills. If you’re involved in the field of marketing, this online course will help to supplement your level of expertise and offer an opportunity for career growth, or increase your attractiveness to potential employers. If you’re the owner of a small business, this program aims to help develop your ability to generate sales and improve revenue. This program is also aimed at validating the data analytics skills of established marketing professionals with an MIT Sloan digital certificate of completion.\u003c/p\u003e575:T5ba,\u003cp\u003eShrinking margins and rising costs are driving public and private health systems to improve operational efficiencies and reduce expenses. Rigorous financial management could help to improve care provision and combat unnecessary spending.\u003c/p\u003e\n\u003cp\u003eThe \u003cstrong\u003eModelling Cost-Effectiveness in Healthcare\u003c/strong\u003e online certificate course from LSE equips you with marketable skills in cost-effectiveness modeling. This technical course has been designed by faculty and experts from the Department of Health Policy at LSE who have a solid grounding in fundamental policy and economic skills, and the knowledge and expertise to apprehend global health challenges. Over eight weeks, you’ll explore cost-effectiveness modeling techniques and discover methods of sourcing quality data. Throughout the course, you’ll learn how to conceptualize a model, inform resource allocation decisions, and analyze a cost-effectiveness study.\u003c/p\u003e\n\u003cp\u003eThis course is suited to both health and finance professionals who deal with"])</script><script>self.__next_f.push([1," modeling costs in the healthcare sector. Health administrators and medical workers will learn how to better budget, assign, and prioritize resources. They’ll also benefit from enhancing their decision-making and resource allocation skills. Finance and insurance professionals will learn how to get the most out of their health products or services budgets, and will benefit from gaining insight into product pricing and R\u0026amp;D resource allocation.\u003c/p\u003e576:T91c,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cstrong\u003eDuration\u003c/strong\u003e : 2 weeks \u003c/p\u003e\n\u003cp\u003eAlmost everyone works with data in some form. Understanding how to do so effectively is a critical skill in a data-rich world. But to get the most out of your numbers and present a clear and compelling narrative, you need the skills to draw out the story beneath the data and communicate it to different audiences. \u003c/p\u003e\n\u003cp\u003eData Storytelling and Visualisation, an online course from Economist Education, ensures you improve your skills and bolster your confidence in working with data. It teaches you how to develop a “data mindset”: a way to think about problems in business and the world at large, and recognise how data can help you make better decisions. Over two stimulating weeks, you will discover how best to visualise different data sets, and spot and avoid common pitfalls. \u003c/p\u003e\n\u003cp\u003ePeople new to data and chart-making will gain a set of practical, foundational skills. Those who work with data regularly will benefit from tips on honing their data-storytelling skills and making simple but powerful data visualisations. Both groups will learn how to create a culture of data-led problem-solving, guided by \u003cem\u003eThe Economist\u003c/em\u003e ’s award-winning data journalists, and receive detailed feedback on their charts from course tutors. \u003c/p\u003e\n\u003cp\u003eThis is a foundational course for people who review, analyse or prepare reports and presentations using data, as well as those who want to use data in decision-making. It will benefit professionals looking to build their confidence in communicating insights from data to various audiences. It will also be valuable to those with analytics experience who would benefit from advice on visualising and presenting data. This course is designed for leaders, managers and decision-makers interested in learning practical techniques, as well as analysts and analytics managers who need to communicate specialist information and insights to an audience with persuasive power. \u003c/p\u003e\n\u003cp\u003eThis course does not provide detailed, technical instruction in complex visualisations such as videographics or the most intricate infographics. However, even professionals with advanced data-visualisation skills stand to benefit from the course’s insights into unearthing the story beneath data and visualising the narrative clearly and beautifully.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"577:T839,"])</script><script>self.__next_f.push([1,"\u003cp\u003eCyberattacks and threats are no longer a case of if, but rather when. Cybercriminals can use phishing emails to gain passwords and credit card details, install malware that disrupts or destroys a computer network, or even use ransomware to hold information hostage. What can you do to protect your business? The Cybersecurity: Managing Risk in the Information Age online short course from Harvard’s Office of the Vice Provost for Advances in Learning (VPAL), in association with HarvardX, offers an in-depth exploration of this complex landscape. This course will help you grow your ability to guard the integrity and confidentiality of your company’s digital information. You’ll learn how to identify and manage operational, litigation, and reputational risk. As you progress through the program, you’ll gain the tools and confidence to conduct vulnerability assessments and locate weaknesses within your organization’s networks, systems, and data, and develop strategies for responding to a cyberattack.Cybersecurity isn’t an IT or tech department responsibility, it’s an organization-wide imperative. That’s why this cybersecurity course is aimed at business leaders and executives, as well as specialists in web security, security administration, or information security roles. In fact, it’s for any professional who wants to understand the full implications of a cyber breach, and have the skills to communicate this to their company and stakeholders. The C-suite or senior management team will learn how to lead their organization in a time of rapidly increasing cyber risk. These leaders will also gain insight into setting an appropriate budget that allows the experts to prepare for and mitigate cyberattacks. This program is also suitably in-depth for cybersecurity professionals – risk analysts, technology specialists, and information security professionals. It unpacks the most common types of cyberattacks, provides strategies for protecting an organization from a threat and its associated risks, and will help professionals develop and lead a cybersecurity plan.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"578:T866,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThe digital age provides businesses with a wealth of data about consumer choices. A major challenge organizations face, however, is an evident skills gap among marketing professionals to translate that information into meaningful and actionable insights. As a result, marketers are increasingly looking to engage, measure, adapt, and optimize every touchpoint of the customer experience. They need a strategy for their customer and performance data that moves in real time and at scale.\u003c/p\u003e\n\u003cp\u003eThe Marketing Analytics: Strategy and Decision-Making online short course from the University of Cape Town (UCT) addresses the why, where, and what of marketing analytics: Why is it needed, where are the main touchpoints, and what analytical techniques can be leveraged to drive marketing efficacy? Over eight weeks, you’ll answer these questions by exploring the current state of marketing analytics, including key challenges that restrict strategic choices. You’ll discover why big data has transformed marketing and learn how to design an analytics framework that leverages big data for marketing decision-making. Using a variety of real-world scenarios, you’ll engage with concepts and techniques to better understand your customers and create data-driven outcomes that optimize return on marketing investment. Finally, you’ll explore existing and proposed privacy frameworks, and the impact that emerging trends will have on the future of marketing.\u003c/p\u003e\n\u003cp\u003eThis course is designed for anyone working in the field of marketing or aspiring towards a career in marketing analytics. It will also be beneficial to business leaders and C-suite professionals looking to increase the efficiency and effectiveness of their marketing departments to boost their bottom lines and stay ahead of competitors. Middle to senior managers looking to update their knowledge and move their career in a new direction will benefit from the real-world marketing scenarios discussed in the course. Newly qualified and existing marketing professionals who want to deepen their understanding of technologies, tools, and tactics will gain an expert-led skill set.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"579:Tcef,"])</script><script>self.__next_f.push([1,"\u003cp\u003eImproving prediction is one of the key challenges the medical industry faces in advancing patient care. Enhancing diagnosis, individualizing treatments, and understanding disease progression are all matters of prediction, an area where machine learning (ML) and artificial intelligence (AI) excel.1\u003c/p\u003e\n\u003cp\u003eDiscover the impact AI innovations can have in medicine — on both traditional health care systems and decision-making approaches — with the Artificial Intelligence in Health Care online short course from the MIT Sloan School of Management and the MIT J-Clinic. \u003c/p\u003e\n\u003cp\u003eThrough industry case studies, you’ll better understand AI’s applications and limitations, examine the challenges AI can help overcome, and explore how it’s already been successfully deployed in the sector. \u003c/p\u003e\n\u003cp\u003eYou’ll gain insight and knowledge from MIT, an institution renowned for developing ML methods with applications in health care. Regina Barzilay, your Faculty Director, is also globally recognized for her work in AI and breast cancer detection. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1\u003ca href=\"https://www.technologyreview.com/s/613361/giving-medicine-a-dose-of-ai/\" target=\"_blank\"\u003eMIT Technology Review\u003c/a\u003e (Apr, 2019).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWhether you work in business or medicine, this course will give you an understanding of the transformative role that AI and ML can play in the health care industry. Professionals working in the health sector will benefit from an understanding of the types of problems that AI techniques can help solve. This program covers key topics such as the basics of ML, neural networks, and deep learning. The knowledge gained throughout this course can be immediately and directly applied to roles within the health care sector. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMachine learning:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eML is a field of artificial intelligence that uses algorithms to learn from and make predictions on data. This course will introduce you to machine learning algorithms and how they are used in the health care industry. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeural networks:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MIT Sloan Artificial Intelligence in Health Care online course provides insights into the different types of neural networks and how they can be applied to health care data. Neural networks are a type of machine learning algorithm that are particularly well suited for tasks such as image recognition and classification. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeep learning:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdditionally, the AI in health care course will cover the basics of deep learning. Deep learning is the process of using artificial neural networks with many layers to learn complex patterns in data. It is a subfield of machine learning that is concerned with algorithms that learn from data that is unstructured or unlabelled. This course will introduce you to deep learning methods and how they are used in the health care industry. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComputer science professionals:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe course is taught by computer science experts from MIT and the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). CSAIL is one of the world’s leading centers for artificial intelligence research. The faculty members teaching this course are at the forefront of their field and are developing cutting-edge technology that is being used in multiple industries today.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"57a:T4c7,\u003cp\u003eWith more than half of the world’s population living in urban areas, there’s a growing interest in developing smarter, safer, and more sustainable cities that are responsive to their citizens. While smart city solutions typically focus on digital enhancements to existing urban infrastructure, the MIT Media Lab Beyond Smart Cities: Emerging Design and Technology online short course goes beyond optimizations. Over six weeks, you’ll explore ways in which disruptive technology can dramatically improve planning, design, and management of contemporary cities for a more resilient future. Guided by MIT faculty, you’ll discover how technologies like data analytics, artificial intelligence (AI), new urban systems, real-time simulations, and predictive urban design can be leveraged to realize more entrepreneurial, high-performance, and livable urban communities.\u003c/p\u003e\n\u003cp\u003eThis online course is designed for professionals interested in urban planning and design, who are seeking ways to transform cities for a more sustainable and vibrant future. With a focus on technology and data, the program is particularly relevant to those who are focused on designing, investing in, and delivering smart city solutions.\u003c/p\u003e57b:T6f0,\u003cp\u003eData, and the value it supplies, has become an invaluable business asset in today’s digital economy. Data monetization — converting data and analytics into financial gain — is now a crucial source of economic value for organizations across the globe.1\u003c/p\u003e\n\u003cp\u003eExplore how your organization can realize the full and future value of its data assets in the Data Monetization Strategy: Creating Value Through Data online short course from the MIT Sloan School of Management. Guided by renowned Faculty Director Dr. Barbara Wixom, you’ll learn to identify opportunities to create value from data to generate financial returns, drive process improvements, and leverage data-based insights to enrich products. In six weeks, you’ll gain a comprehensive understanding of data monetization using CISR-deve"])</script><script>self.__next_f.push([1,"loped frameworks to empower you and your teams to strategically maximize value creation through data. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1\u003ca href=\"https://cisr.mit.edu/publication/2019_1101_DataMonCapsPersist_WixomFarrell\" target=\"_blank\"\u003eMIT CISR\u003c/a\u003e (Nov, 2019).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe growing importance of data as a primary driver of business strategy, requires that data-driven business leaders elevate data and analytics strategies to advance a new vision of business problem-solving. This course is relevant to data-driven business leaders and analytics leaders intent on creating economic value from organizational data. People in these roles may include, but are not limited to, chief data officers, chief information officers, chief technology officers, and chief analytics officers. Technology, data, and information managers, not to mention non-tech leaders and business directors, will elevate any existing knowledge and benefit from the practical frameworks unpacked in the course.\u003c/p\u003e57c:T83d,"])</script><script>self.__next_f.push([1,"\u003cp\u003eData has become one of the most valuable assets in the business world. However, while properly utilized data can hold rich rewards for savvy companies, mismanagement can herald disaster. As a result, governments around the world have increasingly cast their eye towards data protection and privacy, while consumers are better educated than ever about their data rights. \u003c/p\u003e\n\u003cp\u003eIn this climate, organizations need professionals who can navigate local and international regulations, and who are able to guide data policy design, implementation, and monitoring. The University of Cape Town (UCT) Data Protection and Privacy online short course equips you with the skills to mitigate the financial, legal, and reputational risks of data management. Beginning with an exploration of South Africa’s Protection of Personal Information Act (POPIA) and its international counterparts, this course offers you the knowledge to take the lead on data protection in your organization. On the course, you’ll gain practical tools allowing you to monitor, review, report on, and improve data controls, ultimately validating your knowledge with a certificate of completion from UCT, Africa’s leading university.* \u003c/p\u003e\n\u003cp\u003e\u003cem\u003e*\u003ca href=\"https://www.topuniversities.com/university-rankings-articles/world-university-rankings/top-universities-africa\" target=\"_blank\"\u003eQS Top Universities\u003c/a\u003e (Apr, 2021).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis course is ideal for professionals who operate in the legal, risk, and compliance fields and wish to guide their organization’s data policy implementation. For early career professionals and those interested in a career in data protection, the course provides a practical, risk-based approach to their craft. Current data protection professionals and business leaders will gain an up-to-date perspective of regulations and laws, in both the South African and global context, to inform the strategic risks and opportunities faced by modern businesses. The course also provides the opportunity to learn to develop and implement data policies, while investigating modern trends in data protection.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"57d:T625,\u003cp\u003eTechnologies such as artificial intelligence are reshaping the way businesses think about their marketing strategies, as well as how they choose to harness different channels. \u003c/p\u003e\n\u003cp\u003eThe Oxford Digital Marketing: Disruptive Strategy Programme aims to provide you with the skills and confidence to respond to the forces driving disruption in the digital marketing landscape. You’ll investigate how consumer behavior and media consumption have evolved, and develop strategies for adapting your marketing approach to account for this. Led by faculty from Saïd Business School, University of Oxford, you’ll also learn how to capitalize on the power of social media, and effectively use emerging technology and digital marketing channels in your strategy.\u003c/p\u003e\n\u003cp\u003eDevelop a future-focused perspective on marketing as you learn to leverage data and analytics in your decision making and create value in the face of disruption. \u003c/p\u003e\n\u003cp\u003eThis Oxford Saïd programme is best suited to marketing managers working in leadership positions at digital agencies and other companies across industries. It’s also ideal for experienced professionals with a sound understanding of marketing principles who want to build on foundational training in digital and social media platforms with a course centered on future-focused strategy development in an age of disruption. The content is specifically designed for those who want to harness digital innovation in marketing and understand the opportunities that emerging trends and technologies will bring to organizations or clients.\u003c/p\u003e57e:T687,\u003cp\u003eDue to the rapid rise of digital technologies, new online and optimization strategies are required. As a modern professional or business owner, understanding how to harness the power of search engine optimization (SEO) to market your business is essential to supporting its online presence and ultimate success.\u003c/p\u003e\n\u003cp\u003eEnsure your business is as visible as possible in search engine results pages with the Search Engine Optimization online short course f"])</script><script>self.__next_f.push([1,"rom the University of Cape Town (UCT). \u003c/p\u003e\n\u003cp\u003eThis course aims to equip you with the skills to implement your own SEO tactics and strategies, giving you a competitive market advantage. You’ll gain fundamental principles and practical skills to drive more traffic to websites through SEO-optimized content. By practicing on a live eCommerce Wordpress website, you’ll walk away with the ability to offer in-demand SEO services for clients, and boost your website’s ranking.\u003c/p\u003e\n\u003cp\u003eThis course has been designed for both existing digital marketing professionals and those entering the field. Professionals within digital marketing will get the opportunity to refine and advance their skill set with the latest SEO practices, tools, and ethics. Professionals new to online marketing will gain a comprehensive understanding of the need for SEO, content strategies, website structures, and analytics tools. Business owners and entrepreneurs will also benefit from learning practical optimization strategies which they can immediately implement within their context. Marketers, web professionals, and those starting a career in SEO will have the opportunity to validate their skills with a digital certificate.\u003c/p\u003e57f:T764,\u003cp\u003eToday’s human resource (HR) function faces continuous evolution due to technological advances and an influx of data. HR now plays a critical role in ensuring that an organization's digital transformation not only considers technology, but the impact on employee experience, performance, and satisfaction as well. To drive business success, professionals in this industry need to transform their approach and develop the HR skills of the future. \u003c/p\u003e\n\u003cp\u003eGain the in-depth knowledge needed to lead adoption on the Digital Transformation in HR online certificate course from the London School of Economics and Political Science (LSE). Over eight weeks, you’ll develop a holistic overview of the key trends and methods for strategically implementing digital HR into your business to drive performance. Guided by industry l"])</script><script>self.__next_f.push([1,"eaders and expert LSE faculty, you’ll explore effective digital HR adoption and utilization strategies. You’ll also discover how to use a range of digital HR tools to enhance the management and productivity of your workforce. Learn how to use data and technology to enhance the capabilities of your HR function, and make more informed workforce and business decisions.\u003c/p\u003e\n\u003cp\u003eMiddle to senior HR professionals and managers, who focus on organizational performance, will benefit from the insights of digital human resource management (HRM) and people analytics presented on this course. Those at an executive level, who want to upskill or reskill, will gain the knowledge to introduce technology and analytics into their portfolio. For those already working as data scientists or analytics professionals, the course provides oversight of people analytics and HR knowledge in order to broaden your skills. Graduates of HR programmes that didn’t cover digital or analytics training, could enhance their knowledge in digital HR to specialize or remain relevant.\u003c/p\u003e580:T476,\u003cp\u003eDigital strategies, emerging technologies, and innovation have increasingly become the focus in modern marketing. It’s essential that businesses address any skills gaps and develop a fundamental understanding of how to generate value through customer-centric digital marketing.\u003c/p\u003e\n\u003cp\u003eDuring this eight-week program, you’ll explore how key digital marketing tools – including social media and mobile technology – can be leveraged to improve the consumer journey. Delve into the role experimentation and analytics play in enabling data-driven insights to empower decisions regarding the broader strategies of your organization.\u003c/p\u003e\n\u003cp\u003eThis program is aimed at business leaders, professionals, executives and entrepreneurs, who want to gain a better understanding of their customers, and learn how to use key digital marketing tools and an integrated digital marketing strategy to enhance business performance. In an ever-changing consumer and digital landscape, "])</script><script>self.__next_f.push([1,"this is essential for anyone who wishes to explore the role of digital marketing within an organization and how it can be utilized to inform decision-making strategy.\u003c/p\u003e581:T9a0,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThe sustainable use of energy in buildings has become an imperative — it’s key not just to tackling climate change, but to improving the performance of facilities and the competitiveness of businesses too. Consequently, it’s important that anyone involved in the construction, manufacturing, energy, and oil and transport industries today is well-versed in the field of energy efficiency. \u003c/p\u003e\n\u003cp\u003eThe University of Cape Town (UCT) Energy Efficiency and Sustainability online short course offers a holistic overview of the energy efficiency landscape and the economics of this field. Over the course of eight weeks, you’ll explore the latest developments and regulations in the energy industry, and investigate energy use in commercial and industrial contexts. You’ll also learn how to verify energy savings using industry-accepted techniques, examine how to assess the financial viability of energy-efficiency projects, and build a strong business case for interventions. \u003c/p\u003e\n\u003cp\u003eThis course, which is certified by the Engineering Council of South Africa (ECSA) for CPD points, is suited to practicing and consulting engineers, facility managers, architects, building designers, energy managers, energy auditors, manufacturing professionals, and environmental consultants. In essence, it’s designed for all professionals looking to learn how to identify energy conservation opportunities to promote efficiency in buildings and businesses. Additionally, professionals will gain the knowledge needed to specialize their business offerings and take advantage of this increasingly important trend. As students will be asked to apply formulas and perform calculations, basic competence in mathematics is required in order to complete this course. \u003c/p\u003e\n\u003cp\u003eThis course is approved by the University of Cape Town and is validated by the Engineering Council of South Africa (ECSA) for CPD credits. The course has an estimated 80 hours of learning and South African engineering professionals can earn up to eight CPD credits.* \u003c/p\u003e\n\u003cp\u003eInternational students need to consult their national professional engineering bodies to check whether this course is recognized for points towards maintaining professional status. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003e*Should you wish to claim CPD activity, the onus is upon you. The University of Cape Town and GetSmarter accept no responsibility. If you would like to claim validation or points, use the ECSA validation number UCTGSEES23.\u003c/em\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"582:Ta66,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIn an increasingly complex, algorithm-driven world, critical thinking is a vital skill for everyone in business. Professionals must use data discerningly and interrogate the assumptions and claims of others. Only then will they be able to capitalize on their competitive advantages over machines and over peers less able to harness Artificial Intelligence (AI). All this means developing tools and strategies to avoid cognitive bias and error, to make rigorous arguments and to seek truth from facts. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCritical Thinking: Problem-Solving and Decision-Making in a Complex World\u003c/strong\u003e , a two-week online course from Economist Education, shows learners how to interrogate assumptions, use data, and combat cognitive bias to make better decisions. The course’s first module explores how everyone falls prey to uncritical thinking, including narrative oversimplification and confirmation bias, and provides tools to recognize and counteract them. The second module emphasises the importance of rigorous arguments based on data and research, while guiding learners to reframe complex problems and harness diverse perspectives. \u003c/p\u003e\n\u003cp\u003eThe course is guided by Dr Tom Chatfield, an expert on critical thinking, as well as senior editors at The Economist, and features insights from guest speakers including Daniel Kahneman, a Nobel prizewinner. The curriculum uses real-world examples, including The Economist’s editorial processes, to demonstrate the crucial role of critical thinking in decision making. Realistic scenarios and case studies challenge you to overcome oversimplification and faulty reasoning. The course encourages an approach to critical thinking that you can apply in your work, by fostering strategic pauses, self-reflection, investigation, and collaboration. \u003c/p\u003e\n\u003cp\u003eBy the end of the course, you will not only understand your cognitive strengths and vulnerabilities but also gain the tools to make decisions informed by data, stress-test business ideas and interrogate assumptions in a technology-driven world.\u003c/p\u003e\n\u003cp\u003eThis course is designed for experienced professionals and emphasizes the value of human critical thinking in a machine-driven world. Learners from diverse fields, such as marketing, consulting, and financial services, will learn to interrogate assumptions, use data discerningly, and identify essential questions that AI cannot. By embracing critical thinking, experienced professionals and aspiring leaders will be better equipped to stress-test business ideas, enabling them to avoid costly errors and make better decisions. This course is a must for those looking to stay relevant in the age of AI.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"583:T931,"])</script><script>self.__next_f.push([1,"\u003cp\u003eArtificial Intelligence (AI) is developing rapidly, transforming the way we work and interact. Yet, AI is rife with ethical challenges — from potential biases in algorithmic recruiting technology to the privacy implications of work surveillance applications. In order to implement AI effectively, it is essential to understand and address these ethical dilemmas, and to stay up to date on trends and best practices. \u003c/p\u003e\n\u003cp\u003eThe \u003cstrong\u003eEthics of AI\u003c/strong\u003e three-week online course from the London School of Economics and Political Science (LSE) examines the societal challenges and opportunities presented by AI. Through a condensed learning format, you’ll build an analytical toolkit and enduring skill set to apply to real-world issues. Discover how technological change can impact the distribution of economic resources and productivity gains, learn how AI can exacerbate or combat discrimination and power imbalances in the workplace, and investigate how technology is changing everyday social interactions. \u003c/p\u003e\n\u003cp\u003eOver the course of this high-quality three-week master class experience, you’ll unpack the ethical tensions inherent in AI with insights from faculty at LSE’s Department of Philosophy, Logic and Scientific Method — internationally renowned for a type of philosophy that is both continuous with the sciences and socially relevant. Build your intellectual curiosity, and update your personal and professional skill set in a consolidated amount of time. \u003c/p\u003e\n\u003cp\u003eThis highly topical and applicable online course is designed for a range of professionals within the public sector, private sector, and civil society who are faced with ethical issues relating to AI, data, and general technological advancement. Leaders working in organizations that deal with big data or who are looking to implement AI technologies will be empowered to guide their organizations toward responsible innovation by gaining tools for managing the social impact of potential technological changes. Professionals within the tech industry will also benefit from enhancing their knowledge of ethics and AI as a way to further their career development and personal growth. This course is applicable not only to those looking for professional development, but also to anyone interested in understanding the impact of AI on individuals and society.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"584:T693,\u003cp\u003eDo you know what artificial intelligence (AI) means for your organization? It isn’t about robots taking jobs or replacing your workforce. AI is simply the process of programming a computer to make decisions for itself, which can make your business operations more efficient. Need a strategy to effectively implement AI technologies into your company? Then this artificial intelligence program is the one for you. The Artificial Intelligence: Implications for Business Strategy online short course from the MIT Sloan School of Management and the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) will show you how to capitalize on the value automation offers. It explores the potential of robotics, natural language processing, and machine learning (ML), and equips you with the knowledge and confidence to include them in your business strategy. Most importantly, this course will guide the creation of a road map for the implementation of AI and ML in your organization and provide you with the skills to drive innovation forward.This AI course looks at the organizational and managerial implications of the technologies. It’s aimed at managers, executives, and business leaders rather than those in tech or IT roles. So, you don’t need to be a software engineer or data scientist to keep up with the content. On this program, you’ll learn how AI and ML can be applied in the workplace to augment current operations. It’s about developing strategic decision-making skills about the use of these technologies in your business, analyzing their potential impact, and building the language to articulate your insights with your teams and organization.\u003c/p\u003e585:T808,"])</script><script>self.__next_f.push([1,"\u003cp\u003eAs sustainability becomes an increasingly critical part of global consciousness, businesses are faced with one of their biggest opportunities for innovation to date. Today, companies have the chance to transform their practices to drive not only progress, but profits as well. \u003c/p\u003e\n\u003cp\u003eThe Corporate Sustainability Management: Risk, Profit, and Purpose online program from the Yale School of Management Executive Education provides a practical guide for incorporating sustainability into your business strategy. The program features a wide variety of on-the-job resources, insights from industry leaders, case studies, discussions, and real-world applications. At the end of six weeks, you’ll walk away with an understanding of the potential financial impact of sustainability issues, and be equipped with the best practices and tools for leveraging opportunities in sustainability to gain a competitive edge. \u003c/p\u003e\n\u003cp\u003eThis program benefits business leaders and executives looking to develop a competitive advantage by offering them the tools needed to give strategic direction on sustainability. It also provides valuable resources for professionals and specialists who want to drive positive change in their role, as well as those working in the risk management and business continuity space. In addition, the program equips consultants with the insight needed to better advise clients, and provides public sector policymakers and regulators with an in-depth understanding of the impact of sustainability measures on business.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHow does this program differ from the Sustainable Finance and Investment online program?\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Corporate Sustainability Management: Risk, Profit, and Purpose online program is designed to help you embed sustainability and sustainable practices into the core of your business strategies. The Sustainable Finance and Investment online program, on the other hand, focuses primarily on equipping you to make more informed analyses and decisions when investing in sustainable assets and products.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"586:T499,In today's dynamic business landscape, data-driven decision-making is key. That's why we've designed a skills-based learning program that not only equips you with the latest analytics tools and techniques but also connects you with industry micro-credentials that are in high demand.\r\nBecome a data analyst ready to analyze and to visualize data for strategic decision making \r\nStudy contemporary data analyst skills from content derived from leading, global organizations\r\nStudy consumers’ buying habits to help businesses make more strategic decisions about how they market their products and services. \r\nMaster the process of gleaning insights from data to inform better business decisions\r\nLearn in-demand job skills from first day of instruction and bring them into the workplace\r\nThe Masters of Science in Business Analytics is designed to prepare learners for data analyst careers. A data analyst finds a solution to a problem or provides an answer to a question. This career tasks a data analyst to gather, purify, and analyze data sets. Contemporary data analysts work in a variety of fields, including government, business, finance, law enforcement, and science.587:T62c,\u003cp\u003eThe metaverse. The “internet of things.” Augmented reality. Automation. Driverless cars. Robotics. Enhanced security and modeling. These are just a few of the innovations, technologies, and trends in which companies are investing as they prepare for a more tech-forward future. And they’re all powered by artificial intelligence (AI) and related tools and concepts. \u003c/p\u003e\r\n\r\n\u003cp\u003eThere are boundless opportunities to discover in AI. In fact, LinkedIn listed AI practitioners among its top 15 “Jobs on the Rise” for 2021.* Yet even as this field is set to expand dramatically, experts project a shortage of qualified AI professionals and practitioners — a talent gap that some firms worry could potentially slow innovation and lead to other unexpected issues.** Businesses and organizations operating in the field need brave AI practitioners "])</script><script>self.__next_f.push([1,"and experts to step up and take the lead. \u003c/p\u003e\r\n\r\n\u003cp\u003eWith your 100% online Master of Science in Artificial Intelligence from Maryville University, you can help meet that need as you prepare to thrive in the future of AI. Maryville is a leader in tech-forward, future-focused education, and we’re here to help you learn the concepts and techniques to develop and operate state-of-the-art artificial intelligence and machine learning tools, apply critical thinking and leadership skills, and help build a more technologically advanced world. \u003c/p\u003e\r\n\r\n\u003cp\u003e*LinkedIn, “LinkedIn Jobs on the Rise: 15 opportunities that are in demand and hiring now” \u003c/p\u003e\r\n\r\n\u003cp\u003e**The Hill, “The reality of America’s AI talent shortages”\u003c/p\u003e588:T648,Stand out as a dynamic leader in business analytics, with a graduate degree in business from the University of Wisconsin–Madison. Learn to communicate data insights to top executives in your organization — and use those insights to power future-forward business solutions.\r\n\r\nThe world-renowned Wisconsin School of Business at the University of Wisconsin–Madison helps future-focused business leaders build a healthier, more sustainable, and more equitable world. Founded in 1900, the Wisconsin School of Business was one of the first five business schools in the nation, and has been trusted to lead for more than 100 years. It continues this tradition of excellence and innovation today with an online Master of Science-Business in Data, Insights, and Analytics available to all, regardless of their location. \r\n\r\nThe Wisconsin School of Business designed the Master of Science-Business in Data, Insights, and Analytics program to set learners on a path for continued professional growth. \r\n\r\nDelivered by expert faculty, the 15-course curriculum invites learners to meet business challenges with innovation. Discover a flexible, affordable program built to enhance your data analytic skills and leadership prowess. \r\n\r\nAs a learner in the program, you will:\r\nEngage in a fully asynchronous curriculum de"])</script><script>self.__next_f.push([1,"signed to offer you the flexibility you need to balance studies with a busy career and personal life.\r\nMaster essential data analytic tools, including Python, R, SQL, Tableau, AWS, and Snowflake.\r\nLearn a range of relevant topics such as machine learning, data visualization, and cloud computing.589:T639,\u003cp\u003eIf you’re a leader at heart, you’re in the right place. Management and \r\nleadership skills are in demand across industries, and that means there’s \r\nplenty of opportunity for people like you to step up and build the skills \r\nto help maintain an efficient and effective work environment.\u003c/p\u003e\r\n\r\n\u003cp\u003eReal leadership starts with one brave decision, and we want to embolden you \r\nto make your next big career move. Earn your online Master of Arts in Management and Leadership degree from Maryville, and you can be the catalyst behind stronger, more \r\nsuccessful, more organized teams.\u003c/p\u003e\r\n\r\n\u003cp\u003eWe designed our program to be accessible regardless of your academic and \r\nprofessional background. We’ll help you establish a strong foundation in \r\nbusiness, then focus on developing in-demand leadership skills and \r\nqualities like communication, team-building, critical thinking, \r\ndecision-making, and problem-solving.\u003c/p\u003e\r\n\r\n\u003cp\u003eIf you’re ready to move forward in your career, start here. Our program \r\nfocuses on practical, real-world learning opportunities, and it features eight concentrations and/or certificates in Cybersecurity, Health Administration, Human Resource Management, \r\nInformation Technology, Marketing, Project Management, Software \r\nDevelopment, or Data Analytics. You’ll learn from experienced faculty in a \r\nprogram designed with input from top employers, so you can build practical, \r\nrelevant skills that you can apply to your career.\u003c/p\u003e\r\n\r\n\u003cp\u003eDon’t just prepare for the next step in your career. Prepare for every step.\u003c/p\u003e\r\n\r\n\u003cp\u003eGet Curriculum Details\u003c/p\u003e58a:T559,\u003cp\u003eOur online MBA program was created for you.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe business world needs bold and visionary leaders. That’s why Maryville Universi"])</script><script>self.__next_f.push([1,"ty created the online Master of Business Administration — a faster, more convenient, and affordable way to earn an MBA.\u003c/p\u003e\r\n\r\n\u003cp\u003eIn as little as 12 months, you can get the online MBA degree you need to qualify for top-level leadership roles in any organization, in virtually any field. When you enroll in the St. Louis, Missouri-based online MBA program at Maryville, you will enjoy the same high-quality education as our on-campus students. You’ll take the same rigorous courses rooted in reality and research, and you’ll learn from the same expert faculty who are passionate about helping you advance in your career. Plus you’ll have the flexibility to complete your coursework from anywhere in the U.S.\u003c/p\u003e\r\n\r\n\u003cp\u003eWhat’s more, you’ll have the ability to customize your online MBA degree with a choice of 12 concentrations in some of today’s hottest fields.\u003c/p\u003e\r\n\r\n\u003cp\u003eSee for yourself. Everything about our online MBA program is designed to help you redefine your role in today’s business world. If you are ready to take the next brave step toward fulfilling your potential as a capable and confident business leader — on your own time, on your terms — then \r\nMaryville University is open for business.\u003c/p\u003e58b:T5bc,The online Master of Science in Education from Pepperdine University isn’t just for teachers. This comprehensive program equips professionals from all sectors with inclusive leadership expertise and advanced learning theory — preparing them to seek educational leadership positions in public, private, and not-for-profit organizations.\r\n\r\nNote: This program does not prepare teachers for licensure. \r\n\r\nFour concentration options: Choose between Leadership in Learning Design and Technology, Leadership in Pre-K to 12 Education, Leadership in Higher Education, and Organizational Leadership and Learning.\r\n\r\nBecome an educational leader: Develop seamless, connected, and inclusive learning experiences, and learn how to drive change in your industry through educational initiatives.\r\n\r\nDoctoral pathway: Tak"])</script><script>self.__next_f.push([1,"e advantage of our doctoral pathway; upon graduation, some units can apply to your choice of the Graduate School of Education and Psychology’s four doctoral programs. \r\n\r\nAbout Pepperdine University \r\n\r\nWe believe the purpose of higher education is to develop and equip people of value and virtue, who, in turn, create value and virtue in their communities, nations, and the world. That’s why we are committed to the highest standards of academic excellence and Christian values. No matter where our students come from or how they study with us — on campus or online — we welcome them to our community of global scholars who boldly seek to GO THERE.58c:T886,"])</script><script>self.__next_f.push([1,"Creative, client-facing, analytical, practical or more: whatever marketing specialism you aspire to, our MSc Strategic Marketing aims to equip you with the fundamental concepts, models, digital tools and processes that are necessary for gaining sustainable competitive advantage in the marketplace.\r\n\r\nYou'll gain comprehensive strategic, digital marketing and social media knowledge and skills, which will give you advanced understanding of contemporary marketing principles. Digital and strategic marketing skills are vital for marketing in a data-driven world, and this course will give you the knowledge and skills you need to make informed marketing decisions in today’s dynamic marketing world. You will develop as an agile and entrepreneurial marketing professional and will be an asset for future employers.\r\n\r\nAs an MSc student of Strategic Marketing, you’ll be based in Surrey Business School and be part of a vibrant community focused on improving business practice and creating a sustainable and positive change.\r\n\r\nAt Surrey Business School, we emphasise innovation and leadership, and provide an in-depth and hands-on approach to teaching. Our MSc Strategic Marketing builds on the established research strengths and teaching capabilities of the Department of Marketing which will equip you with the attributes, ways of thinking and behaviour of a contemporary marketing professional.\r\n\r\nStudents will learn advanced analytics techniques adopted by the leading organisations to assess and manage customer service experience.\r\n\r\nWe’ll bring out your creativity and problem-solving abilities and develop your skills as a successful marketer. We’ll teach you the ‘ins’ and ‘outs’ of marketing strategies, processes and techniques. You’ll also learn about communication, digital and social media marketing, marketing research, and customer-facing functions.\r\n\r\nYou’ll study a range of core concepts from other management disciplines, including innovation management. You can tailor our course to suit your career aspirations by choosing from distinctive optional modules, covering different topics such as global strategy, marketing analytics and more."])</script><script>self.__next_f.push([1,"58d:T4a4,I didn’t want theory — I could do theory all day. I wanted something I \r\ncould use immediately when I walk out the door, and that’s what Maryville’s \r\ncourses provided.” — Felecia W., Maryville Grad The digital world runs on \r\ndata. So can your career. We live in a digital world — one in which \r\nexamples of data science and analysis can be found everywhere. Consider \r\nyour product recommendations from Amazon. Think about how companies in \r\ndifferent industries like Boeing, Walmart, and Disney use data to drive \r\ncritical business decisions, and insurance companies depend on this \r\nanalysis to forecast risk or banks to evaluate loan applications. Now \r\nimagine the career potential if you had the skills to help them do it. Earn \r\nyour bachelor’s in data science online from Maryville University, and \r\nyou’ll do more than study the tools and techniques used to dig deeper into \r\ndata. Graduates can build the skills to explore, analyze, monitor, manage, \r\nand visualize large data sets using the latest technology. Our innovative \r\nprogram also features a business minor, which can help prepare you for top \r\ndata jobs in nearly any field. Get Curriculum Details58e:T7ac,Cleveland State University partners with world-class hospitals, Fortune 500 companies, government research centers, and cultural institutions to give its students academic, research, and job opportunities. With more than 175+ undergraduate degrees to choose from, Cleveland State Global students are prepared to succeed in the global workforce.\n-An undergraduate degree in Information Systems teaches students how to design, develop, and manage information systems that support business processes and decision-making.\n-Students learn about the principles of project management, including planning, scheduling, and budgeting.\n-They study the analysis and design of information systems, using techniques such as data modeling and process mapping.\n-Information Systems students learn about databases, including design, implementation, and manage"])</script><script>self.__next_f.push([1,"ment of database systems.\n-They study enterprise resource planning (ERP) systems, which integrate business processes and functions across different departments and areas of an organization.\n-Students learn about the principles of data analytics, including data mining, machine learning, and statistical analysis, and their applications in business decision-making.\n-They also learn about the legal and ethical issues surrounding the use of information systems in organizations, including privacy, security, and intellectual property.\n-An undergraduate degree in Information Systems teaches students how to communicate technical information to non-technical stakeholders, including writing technical reports and giving presentations.\n-Students learn about emerging technologies and their potential impact on organizations and society, preparing them to be innovative and responsible technology professionals.\n-Finally, Information Systems students develop skills in teamwork and collaboration, as well as leadership and management, to prepare them for careers in various fields such as business, healthcare, and government.58f:T707,UMass Boston, located in America’s most celebrated college city, combines the resources of a major research university and the accessibility of a public institution. With 65+ courses of study and a prestigious Honors College, UMass Boston gives you access to career opportunities, research projects, and a strong alumni network. With 11% of the student body representing 140+ countries and speaking 60+ languages, UMass Boston is truly global.\n-An undergraduate degree in Mathematics teaches students the principles of pure and applied mathematics, including calculus, algebra, and analysis.\n-Students learn about mathematical proof and how to construct rigorous and logical arguments.\n-They study the foundations of geometry, topology, and number theory.\n-Mathematics students develop problem-solving skills, critical thinking, and logical reasoning that are useful in a wide range of fields.\n-They learn about the"])</script><script>self.__next_f.push([1," applications of mathematics in fields such as physics, engineering, finance, and computer science.\n-Students study the principles of probability theory and statistics, including methods of statistical inference and data analysis.\n-Mathematics students learn how to use mathematical software and computer programming languages to solve mathematical problems and simulate real-world scenarios.\n-They also study the history and philosophy of mathematics, exploring the development of mathematical ideas and their relationship to other fields of study.\n-Mathematics students learn how to communicate mathematical ideas and findings through technical writing and presentations.\n-Finally, an undergraduate degree in Mathematics prepares students for careers in various fields such as academia, industry, and government, as well as for further study in graduate and professional programs.590:T85d,"])</script><script>self.__next_f.push([1,"Western New England University offers a hands-on and personalized educational experience, with small class sizes and nurturing faculty. The 215-acre campus offers a vibrant community where students can explore cutting-edge research, entrepreneurial prospects, and creative pursuits, all while building a wide professional network. With degree programs in high-demand fields such as engineering, health, pharmaceuticals, and business, Western New England University prepares students to get started on their career journey. Learn from industry professionals as you develop key skills and gain in-depth knowledge that will help you stand out to employers in the US, or anywhere in the world.\n-An undergraduate degree in Mathematical Sciences teaches students the principles of pure and applied mathematics, including calculus, algebra, and analysis.\n-Students learn about the use of mathematical models to solve real-world problems in fields such as engineering, physics, and finance.\n-They study probability theory and statistics, including methods of statistical inference and data analysis.\n-Mathematical Sciences students learn about the foundations of computer science, including algorithms, programming, and data structures.\n-They also study the principles of mathematical logic and the foundations of mathematics, including set theory and topology.\n-Students develop skills in problem-solving, critical thinking, and logical reasoning, which are useful in a wide range of fields.\n-They learn about the use of mathematical software and computer programming languages to solve mathematical problems and simulate real-world scenarios.\n-Mathematical Sciences students study the history and philosophy of mathematics, exploring the development of mathematical ideas and their relationship to other fields of study.\n-They learn how to communicate mathematical ideas and findings through technical writing and presentations.\n-Finally, an undergraduate degree in Mathematical Sciences prepares students for careers in various fields such as academia, industry, and government, as well as for further study in graduate and professional programs."])</script><script>self.__next_f.push([1,"591:Ta6f,"])</script><script>self.__next_f.push([1,"Gonzaga University’s humanistic heritage focuses on educating the mind, body, and spirit, and developing personal, academic, and professional growth through critical thought and creative innovation. Gonzaga University’s humanistic heritage focuses on educating the mind, body, and spirit, and developing personal, academic, and professional growth through critical thought and creative innovation.\n-In an undergraduate Applied Mathematics degree, students learn mathematical theories and modeling techniques and apply them to solve real-world problems in various fields. The curriculum typically includes courses in calculus, linear algebra, differential equations, probability theory, statistics, numerical analysis, and optimization. The degree program includes projects, research opportunities, and internships, and students may also study advanced topics in mathematics.\n-Calculus: Students learn calculus, including differentiation and integration, which is used to model real-world problems and analyze changes in physical and mathematical systems.\n-Linear algebra: Students learn linear algebra, including matrix algebra and eigenvalue analysis, which is used to solve systems of linear equations and in data analysis.\n-Differential equations: Students learn differential equations, which are used to model physical phenomena and systems that change over time.\n-Probability theory: Students learn probability theory, including random variables and distributions, which is used in data analysis and modeling of random phenomena.\n-Statistics: Students learn statistical methods and techniques, including regression analysis, hypothesis testing, and sampling theory, which are used in data analysis and empirical research.\n-Numerical analysis: Students learn numerical methods for solving mathematical problems that are difficult or impossible to solve analytically, such as systems of differential equations or optimization problems.\n-Optimization: Students learn optimization techniques, including linear and nonlinear programming, which are used to solve problems and model decision-making in various fields.\n-Mathematical modeling: Students learn how to model real-world problems using mathematical tools and techniques, and how to interpret and communicate the results.\n-Computer programming: Students learn computer programming and algorithm design, which is used to solve mathematical problems and implement numerical methods.\n-Advanced topics: Students may study advanced topics in mathematics, such as abstract algebra, topology, real analysis, and partial differential equations, to gain a deeper understanding of mathematical theories and applications."])</script><script>self.__next_f.push([1,"592:T636,- #147 National Universities - U.S. News \u0026 World Report, 2019\n- A true campus experience just minutes from the cultural and financial capital of the world\n- 240+ major companies recruit on campus Apple, IBM, AIG, Pepsi and Merrill Lynch\n-An undergraduate degree in Mathematics teaches students the principles of pure and applied mathematics, including calculus, algebra, and analysis.\n-Students learn about mathematical proof and how to construct rigorous and logical arguments.\n-They study the foundations of geometry, topology, and number theory.\n-Mathematics students develop problem-solving skills, critical thinking, and logical reasoning that are useful in a wide range of fields.\n-They learn about the applications of mathematics in fields such as physics, engineering, finance, and computer science.\n-Students study the principles of probability theory and statistics, including methods of statistical inference and data analysis.\n-Mathematics students learn how to use mathematical software and computer programming languages to solve mathematical problems and simulate real-world scenarios.\n-They also study the history and philosophy of mathematics, exploring the development of mathematical ideas and their relationship to other fields of study.\n-Mathematics students learn how to communicate mathematical ideas and findings through technical writing and presentations.\n-Finally, an undergraduate degree in Mathematics prepares students for careers in various fields such as academia, industry, and government, as well as for further study in graduate and professional programs.593:T7ae,Florida International University is one of the largest public research universities in the US. The FIU Global First Year program prepares you for academic, social, and professional success. Florida International University offers 110+ undergraduate programs. International business, hospitality, engineering, and criminal justice are among FIU’s top fields of study.\n-An undergraduate degree in Mathematics and Statistics teaches students"])</script><script>self.__next_f.push([1," the principles of pure and applied mathematics, including calculus, algebra, and analysis, as well as statistical theory and methods.\n-Students learn about mathematical proof and how to construct rigorous and logical arguments.\n-They study the foundations of probability theory and statistics, including methods of statistical inference and data analysis.\n-Mathematics and Statistics students develop problem-solving skills, critical thinking, and logical reasoning that are useful in a wide range of fields.\n-They learn about the applications of mathematics and statistics in fields such as physics, engineering, finance, and data science.\n-Students study the principles of mathematical modeling and simulation, using mathematical and statistical tools to solve real-world problems.\n-Mathematics and Statistics students learn how to use mathematical and statistical software and computer programming languages to solve mathematical and statistical problems.\n-They also study the history and philosophy of mathematics and statistics, exploring the development of mathematical and statistical ideas and their relationship to other fields of study.\n-Mathematics and Statistics students learn how to communicate mathematical and statistical ideas and findings through technical writing and presentations.\n-Finally, an undergraduate degree in Mathematics and Statistics prepares students for careers in various fields such as academia, industry, and government, as well as for further study in graduate and professional programs.594:Ta16,"])</script><script>self.__next_f.push([1,"University of the Pacific's undergraduate programs are offered at the Stockton, California campus and offer world-class instruction in a supportive and challenging atmosphere. University of the Pacific offers 80+ undergraduate majors, small class sizes, a diverse campus experience, and internship opportunities.\n-In an undergraduate Applied Mathematics degree, students learn mathematical theories and modeling techniques and apply them to solve real-world problems in various fields. The curriculum typically includes courses in calculus, linear algebra, differential equations, probability theory, statistics, numerical analysis, and optimization. The degree program includes projects, research opportunities, and internships, and students may also study advanced topics in mathematics.\n-Calculus: Students learn calculus, including differentiation and integration, which is used to model real-world problems and analyze changes in physical and mathematical systems.\n-Linear algebra: Students learn linear algebra, including matrix algebra and eigenvalue analysis, which is used to solve systems of linear equations and in data analysis.\n-Differential equations: Students learn differential equations, which are used to model physical phenomena and systems that change over time.\n-Probability theory: Students learn probability theory, including random variables and distributions, which is used in data analysis and modeling of random phenomena.\n-Statistics: Students learn statistical methods and techniques, including regression analysis, hypothesis testing, and sampling theory, which are used in data analysis and empirical research.\n-Numerical analysis: Students learn numerical methods for solving mathematical problems that are difficult or impossible to solve analytically, such as systems of differential equations or optimization problems.\n-Optimization: Students learn optimization techniques, including linear and nonlinear programming, which are used to solve problems and model decision-making in various fields.\n-Mathematical modeling: Students learn how to model real-world problems using mathematical tools and techniques, and how to interpret and communicate the results.\n-Computer programming: Students learn computer programming and algorithm design, which is used to solve mathematical problems and implement numerical methods.\n-Advanced topics: Students may study advanced topics in mathematics, such as abstract algebra, topology, real analysis, and partial differential equations, to gain a deeper understanding of mathematical theories and applications."])</script><script>self.__next_f.push([1,"595:T951,"])</script><script>self.__next_f.push([1,"The University of Dayton is a top-tier research university dedicated to academic excellence, community leadership, entrepreneurship, and creating a positive global impact. University of Dayton offers international students 80+ undergraduate degree options. The University of Dayton’s popular and highly ranked programs include engineering, entrepreneurship, business, and aerospace and aviation engineering.\n-In an undergraduate Applied Mathematical Economics degree, students learn mathematical modeling techniques to analyze economic systems and data, including courses in calculus, linear algebra, probability theory, statistics, optimization, game theory, and econometrics. They also apply these tools to real-world economic problems and may study microeconomics, macroeconomics, international trade, and development economics. \n-Mathematical modeling: Students learn mathematical modeling techniques to represent and analyze economic systems and data.\n-Calculus: Students learn calculus, including differentiation and integration, which is used extensively in economic analysis and optimization.\n-Linear algebra: Students learn linear algebra, including matrix algebra and eigenvalue analysis, which is used to solve systems of linear equations and in data analysis.\n-Differential equations: Students learn differential equations, which are used to model dynamic economic systems.\n-Probability theory: Students learn probability theory, including random variables and distributions, which is used in econometric analysis and financial modeling.\n-Statistics: Students learn statistical methods and techniques, including regression analysis, hypothesis testing, and sampling theory, which are used in data analysis and empirical research.\n-Optimization: Students learn optimization techniques, including linear and nonlinear programming, which are used to solve economic problems and model decision-making.\n-Game theory: Students learn game theory, which is used to analyze strategic interactions between individuals or groups in economic systems.\n-Econometrics: Students learn econometric methods, which are used to estimate and test economic models using data.\n-Microeconomics and macroeconomics: Students study microeconomic and macroeconomic concepts, including consumer and producer behavior, market structures, national income accounting, monetary policy, and fiscal policy."])</script><script>self.__next_f.push([1,"596:T9a9,"])</script><script>self.__next_f.push([1,"- #78 National Universities - U.S. News \u0026 World Report, 2019\n- #5 Best U.S. Cities for Jobs - Fortune, 2018\n- Create meaningful change in America’s vibrant political, historical \u0026 cultural capital city\n-In an undergraduate Applied Mathematics degree, students learn mathematical theories and modeling techniques and apply them to solve real-world problems in various fields. The curriculum typically includes courses in calculus, linear algebra, differential equations, probability theory, statistics, numerical analysis, and optimization. The degree program includes projects, research opportunities, and internships, and students may also study advanced topics in mathematics.\n-Calculus: Students learn calculus, including differentiation and integration, which is used to model real-world problems and analyze changes in physical and mathematical systems.\n-Linear algebra: Students learn linear algebra, including matrix algebra and eigenvalue analysis, which is used to solve systems of linear equations and in data analysis.\n-Differential equations: Students learn differential equations, which are used to model physical phenomena and systems that change over time.\n-Probability theory: Students learn probability theory, including random variables and distributions, which is used in data analysis and modeling of random phenomena.\n-Statistics: Students learn statistical methods and techniques, including regression analysis, hypothesis testing, and sampling theory, which are used in data analysis and empirical research.\n-Numerical analysis: Students learn numerical methods for solving mathematical problems that are difficult or impossible to solve analytically, such as systems of differential equations or optimization problems.\n-Optimization: Students learn optimization techniques, including linear and nonlinear programming, which are used to solve problems and model decision-making in various fields.\n-Mathematical modeling: Students learn how to model real-world problems using mathematical tools and techniques, and how to interpret and communicate the results.\n-Computer programming: Students learn computer programming and algorithm design, which is used to solve mathematical problems and implement numerical methods.\n-Advanced topics: Students may study advanced topics in mathematics, such as abstract algebra, topology, real analysis, and partial differential equations, to gain a deeper understanding of mathematical theories and applications."])</script><script>self.__next_f.push([1,"597:T80d,"])</script><script>self.__next_f.push([1,"The University of Utah is located on a picturesque campus in Salt Lake City. Utah creates global leaders by placing a strong emphasis on entrepreneurship, innovation, and quality of life. The Utah College of Engineering - ranked #61 in Undergraduate Engineering by U.S. News \u0026 World Report (2020) - prepares students to improve the productivity, health, safety, and enjoyment of human life through leading-edge research and tech development.\n-An undergraduate degree in Information Systems teaches students how to design, develop, and manage information systems that support business processes and decision-making.\n-Students learn about the principles of project management, including planning, scheduling, and budgeting.\n-They study the analysis and design of information systems, using techniques such as data modeling and process mapping.\n-Information Systems students learn about databases, including design, implementation, and management of database systems.\n-They study enterprise resource planning (ERP) systems, which integrate business processes and functions across different departments and areas of an organization.\n-Students learn about the principles of data analytics, including data mining, machine learning, and statistical analysis, and their applications in business decision-making.\n-They also learn about the legal and ethical issues surrounding the use of information systems in organizations, including privacy, security, and intellectual property.\n-An undergraduate degree in Information Systems teaches students how to communicate technical information to non-technical stakeholders, including writing technical reports and giving presentations.\n-Students learn about emerging technologies and their potential impact on organizations and society, preparing them to be innovative and responsible technology professionals.\n-Finally, Information Systems students develop skills in teamwork and collaboration, as well as leadership and management, to prepare them for careers in various fields such as business, healthcare, and government."])</script><script>self.__next_f.push([1,"598:T6d8,Louisiana State University is a Top 100 Public University with a dedicated focus on student involvement, research opportunities, and experiential learning. With more than 330 fields of study and 70 majors, LSU offers students opportunities for hands-on experience working alongside world-class faculty. Top-ranked and popular programs include business, engineering, petroleum engineering, and STEM.\n-An undergraduate degree in Mathematics teaches students the principles of pure and applied mathematics, including calculus, algebra, and analysis.\n-Students learn about mathematical proof and how to construct rigorous and logical arguments.\n-They study the foundations of geometry, topology, and number theory.\n-Mathematics students develop problem-solving skills, critical thinking, and logical reasoning that are useful in a wide range of fields.\n-They learn about the applications of mathematics in fields such as physics, engineering, finance, and computer science.\n-Students study the principles of probability theory and statistics, including methods of statistical inference and data analysis.\n-Mathematics students learn how to use mathematical software and computer programming languages to solve mathematical problems and simulate real-world scenarios.\n-They also study the history and philosophy of mathematics, exploring the development of mathematical ideas and their relationship to other fields of study.\n-Mathematics students learn how to communicate mathematical ideas and findings through technical writing and presentations.\n-Finally, an undergraduate degree in Mathematics prepares students for careers in various fields such as academia, industry, and government, as well as for further study in graduate and professional programs.599:T740,- #147 National Universities - U.S. News \u0026 World Report, 2019\n- A true campus experience just minutes from the cultural and financial capital of the world\n- 240+ major companies recruit on campus Apple, IBM, AIG, Pepsi and Merrill Lynch\n-An undergraduate degree in Information S"])</script><script>self.__next_f.push([1,"ystems teaches students how to design, develop, and manage information systems that support business processes and decision-making.\n-Students learn about the principles of project management, including planning, scheduling, and budgeting.\n-They study the analysis and design of information systems, using techniques such as data modeling and process mapping.\n-Information Systems students learn about databases, including design, implementation, and management of database systems.\n-They study enterprise resource planning (ERP) systems, which integrate business processes and functions across different departments and areas of an organization.\n-Students learn about the principles of data analytics, including data mining, machine learning, and statistical analysis, and their applications in business decision-making.\n-They also learn about the legal and ethical issues surrounding the use of information systems in organizations, including privacy, security, and intellectual property.\n-An undergraduate degree in Information Systems teaches students how to communicate technical information to non-technical stakeholders, including writing technical reports and giving presentations.\n-Students learn about emerging technologies and their potential impact on organizations and society, preparing them to be innovative and responsible technology professionals.\n-Finally, Information Systems students develop skills in teamwork and collaboration, as well as leadership and management, to prepare them for careers in various fields such as business, healthcare, and government.59a:T6a9,Auburn University prepares you for success with its prestigious academic programs, emphasis on hands-on learning experiences, and family spirit. Auburn University offers more than 150 undergraduate degrees, including top-ranked programs in engineering, business, supply chain management, journalism, architecture and design, and fisheries/aquaculture.\n-An undergraduate degree in Mathematics teaches students the principles of pure and applied mathematics, incl"])</script><script>self.__next_f.push([1,"uding calculus, algebra, and analysis.\n-Students learn about mathematical proof and how to construct rigorous and logical arguments.\n-They study the foundations of geometry, topology, and number theory.\n-Mathematics students develop problem-solving skills, critical thinking, and logical reasoning that are useful in a wide range of fields.\n-They learn about the applications of mathematics in fields such as physics, engineering, finance, and computer science.\n-Students study the principles of probability theory and statistics, including methods of statistical inference and data analysis.\n-Mathematics students learn how to use mathematical software and computer programming languages to solve mathematical problems and simulate real-world scenarios.\n-They also study the history and philosophy of mathematics, exploring the development of mathematical ideas and their relationship to other fields of study.\n-Mathematics students learn how to communicate mathematical ideas and findings through technical writing and presentations.\n-Finally, an undergraduate degree in Mathematics prepares students for careers in various fields such as academia, industry, and government, as well as for further study in graduate and professional programs.59b:T6c5,The University of Illinois Chicago provides a hands-on learning experience in a supportive, diverse environment. Located in downtown Chicago, UIC offers you the opportunity to live, learn, and excel in the third-largest city in the US. UIC’s top-ranked programs include engineering, business, architecture, design, education, health sciences, public health, and public affairs.\n-An undergraduate degree in Mathematics teaches students the principles of pure and applied mathematics, including calculus, algebra, and analysis.\n-Students learn about mathematical proof and how to construct rigorous and logical arguments.\n-They study the foundations of geometry, topology, and number theory.\n-Mathematics students develop problem-solving skills, critical thinking, and logical reasoning that are"])</script><script>self.__next_f.push([1," useful in a wide range of fields.\n-They learn about the applications of mathematics in fields such as physics, engineering, finance, and computer science.\n-Students study the principles of probability theory and statistics, including methods of statistical inference and data analysis.\n-Mathematics students learn how to use mathematical software and computer programming languages to solve mathematical problems and simulate real-world scenarios.\n-They also study the history and philosophy of mathematics, exploring the development of mathematical ideas and their relationship to other fields of study.\n-Mathematics students learn how to communicate mathematical ideas and findings through technical writing and presentations.\n-Finally, an undergraduate degree in Mathematics prepares students for careers in various fields such as academia, industry, and government, as well as for further study in graduate and professional programs.59c:T67f,- #78 National Universities - U.S. News \u0026 World Report, 2019\n- #5 Best U.S. Cities for Jobs - Fortune, 2018\n- Create meaningful change in America’s vibrant political, historical \u0026 cultural capital city\n-An undergraduate degree in Statistics teaches students the principles of statistical theory and methods, including probability theory, data analysis, and statistical inference.\n-Students learn how to design experiments and studies, collect and analyze data, and draw conclusions from statistical analysis.\n-They study statistical modeling and regression analysis, using mathematical and statistical tools to model relationships between variables.\n-Statistics students develop skills in data visualization and communication, including graphing, charting, and presenting data.\n-They learn about the applications of statistics in fields such as biology, economics, psychology, and public health.\n-Students study the principles of machine learning and data mining, using statistical and computational methods to analyze large datasets.\n-Statistics students learn how to use statistical software and c"])</script><script>self.__next_f.push([1,"omputer programming languages to solve statistical problems and analyze data.\n-They also study the history and philosophy of statistics, exploring the development of statistical ideas and their relationship to other fields of study.\n-Statistics students learn how to communicate statistical ideas and findings through technical writing and presentations.\n-Finally, an undergraduate degree in Statistics prepares students for careers in various fields such as academia, industry, and government, as well as for further study in graduate and professional programs.59d:T66c,The University of Kansas is a top-tier public research university with a commitment to a research-focused academic curriculum and career support. At KU, you can choose from over 190 fields of study, including top-ranked business, engineering, pharmacy, education, and architecture programs.\n-An undergraduate degree in Mathematics teaches students the principles of pure and applied mathematics, including calculus, algebra, and analysis.\n-Students learn about mathematical proof and how to construct rigorous and logical arguments.\n-They study the foundations of geometry, topology, and number theory.\n-Mathematics students develop problem-solving skills, critical thinking, and logical reasoning that are useful in a wide range of fields.\n-They learn about the applications of mathematics in fields such as physics, engineering, finance, and computer science.\n-Students study the principles of probability theory and statistics, including methods of statistical inference and data analysis.\n-Mathematics students learn how to use mathematical software and computer programming languages to solve mathematical problems and simulate real-world scenarios.\n-They also study the history and philosophy of mathematics, exploring the development of mathematical ideas and their relationship to other fields of study.\n-Mathematics students learn how to communicate mathematical ideas and findings through technical writing and presentations.\n-Finally, an undergraduate degree in Math"])</script><script>self.__next_f.push([1,"ematics prepares students for careers in various fields such as academia, industry, and government, as well as for further study in graduate and professional programs.59e:T4c2,Technology powers our world, touching every aspect of our lives. So it’s no \r\nwonder that qualified computer science professionals are in such high \r\ndemand.\r\n\r\nNow you can start your technology career strong with an online Bachelor of \r\nScience in Computer Science from Maryville University. Designed with input \r\nfrom top employers, along with experienced instructors from our John E. \r\nSimon School of Business and College of Arts and Sciences, our flexible, \r\nstate-of-the-art program can help you gain the technical, problem-solving, \r\nand critical thinking skills you need to pursue top jobs in tech.\r\n\r\nBuild your foundation in key competencies such as computer architecture, \r\nsecurity, programming, and web design. Benefit from experiential learning \r\nopportunities so you can apply what you’ve learned in real-world \r\nprofessional settings. And tailor your education to your interests and \r\ngoals with a choice of six computer science certificates built right into \r\nthe curriculum.\r\n\r\nWhen you earn your B.S. in computer science degree from Maryville, you put \r\nyourself in a position to build the world of tomorrow and embark on an \r\nexciting and financially rewarding career.\r\n\r\nGet Curriculum Details59f:T639,The world has a passion for sports. They entertain, excite, and unite us. \r\nThey also hold a major-league position in the global economy: According to \r\nthe annual PricewaterhouseCoopers Sports Outlook, the sports industry in \r\nNorth America is on track to eclipse $80 billion by 2022. And the modern \r\nsports industry needs experts to help drive ticket sales, drive sponsorship \r\ndeals, build awareness for brand-name apparel and merchandise, and discover \r\nefficiencies in data to help organizations get a competitive edge.It’s an exciting time to build a future in the business of sports \r\nmanagement. And that’s exactly what Maryville’"])</script><script>self.__next_f.push([1,"s Rawlings Sport Business \r\nManagement program can help you do. We named our program for our core \r\npartner, Rawlings Sporting Goods, to reflect a strong academic and \r\nprofessional partnership that is the only one of its kind in the world. \r\nThanks to the support, influence, industry knowledge, and resources of a \r\nglobally recognized and respected brand like Rawlings, we built our program \r\nto address the most pressing challenges in the field — and anticipate \r\nfuture needs.Earn your bachelor’s in sport business management with us, and you can \r\nchoose from two in-demand concentrations: Results-Centered Sales and Sport \r\nData Analytics. Develop proven skills and techniques through experiential, \r\nmarket-relevant coursework backed by leaders in the sports management \r\nindustry, and you can maximize your marketability and put your passion for \r\nsports to work. We can help you do it. 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