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src="/_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"/><div class="font-normal"><span class="text-sm lg:text-base block">CS50's Introduction to Computer Science</span><span class="text-xs lg:text-sm block">HarvardX | Course</span></div></a></li><li class="m-0 px-3 py-2 ProductSearch_searchListItem__5Bj11"><a href="/executive-education/massachusetts-institute-of-technology-artificial-intelligence-implications-for-business-strategy?correlationId=b9e0b793-3af8-485d-9be1-7e8e0fb0d72d" class="no-underline flex items-center"><img alt="Artificial Intelligence: Implications for Business Strategy" 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%2F32ab61e5-44b4-4316-ad59-9f04fc876e0a-aeb25306d62b.small.jpg&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%2F32ab61e5-44b4-4316-ad59-9f04fc876e0a-aeb25306d62b.small.jpg&w=96&q=75 2x" src="/_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%2F32ab61e5-44b4-4316-ad59-9f04fc876e0a-aeb25306d62b.small.jpg&w=96&q=75"/><div class="font-normal"><span class="text-sm lg:text-base block">Artificial Intelligence: Implications for Business Strategy</span><span class="text-xs lg:text-sm block">MIT Sloan School of Management | Executive Education</span></div></a></li><li class="m-0 px-3 py-2 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6.06819C10.2439 5.89245 10.2439 5.60753 10.0682 5.43179L7.81819 3.18179C7.73379 3.0974 7.61933 3.04999 7.49999 3.04999C7.38064 3.04999 7.26618 3.0974 7.18179 3.18179L4.93179 5.43179ZM10.0682 9.56819C10.2439 9.39245 10.2439 9.10753 10.0682 8.93179C9.89245 8.75606 9.60753 8.75606 9.43179 8.93179L7.49999 10.8636L5.56819 8.93179C5.39245 8.75606 5.10753 8.75606 4.93179 8.93179C4.75605 9.10753 4.75605 9.39245 4.93179 9.56819L7.18179 11.8182C7.35753 11.9939 7.64245 11.9939 7.81819 11.8182L10.0682 9.56819Z" fill="currentColor" fill-rule="evenodd" clip-rule="evenodd"></path></svg></button><select aria-hidden="true" tabindex="-1" style="position:absolute;border:0;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0, 0, 0, 0);white-space:nowrap;word-wrap:normal"></select></div><div data-state="active" data-orientation="horizontal" role="tabpanel" aria-labelledby="radix-:R1cjjttrkva:-trigger-Course" id="radix-:R1cjjttrkva:-content-Course" tabindex="0" class="mt-2 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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 Python Courses" id="featured-python-courses"></a><div class="flex flex-col gap-2 pb-10"><div class="Default_content__HO8we"><div id=""><h3>What is covered in a Python tutorial?</h3><p>For those who want to learn Python programming, a typical introductory course will cover the fundamentals of object-oriented programming and Python syntax. Some of the Python trainings likely covered will include concepts such as “hello world,” variables, data types, basic algorithms, and debugging Python code.</p><p>Once comfortable with Python basics, more experienced programmers can progress to intermediate or advanced Python instruction. A more in-depth Python course can include:</p><ul><li><p>Data analysis with packages such as pandas and NumPy</p></li><li><p>Data visualization using matplotlib or Seaborn</p></li><li><p>Machine learning with Scikit-Learn, Keras, SciPy, PyTorch and TensorFlow</p></li><li><p>Natural language processing using nltk</p></li></ul><p>Other advanced Python programming courses will cover topics such as decorators, regular expressions, interfacing with web applications using Django and Flask, and conducting statistical analysis. </p></div></div><a class="subnav-item -mt-1" name="Explore Python jobs" id="explore-python-jobs"></a><div class="Default_content__HO8we"><div id=""><h2>Explore Python jobs</h2><p>Python skills can lead to many different careers, from the tech industry to data roles in other industries. Python jobs exist in areas such as analytics, marketing, research, communications and more.</p><p>Jobs that use Python include:</p><ul><li><p>Python Developer</p></li><li><p>Data Scientist</p></li><li><p>Machine Learning Engineer</p></li><li><p>Full Stack Developer</p></li><li><p>Data or Quantitative Analyst</p></li><li><p>Technical Writer</p></li></ul><p>Entry-level Python jobs may seek applicants with a few years of coding experience, but open-source contributions and short course work may be alternative options to traditional experience.</p><p>For those interested in pursuing a Python career that requires formal training, a <a class="text-link underline" href="https://www.edx.org/boot-camps/coding">coding boot camp</a> or <a class="text-link underline" href="https://www.edx.org/bachelors">bachelor’s degree in computer science or data science</a> might be a good fit. Additionally, a <a class="text-link underline" href="https://www.edx.org/masters">master’s degree in computer science, analytics or data science</a> could be an option for a student looking to switch fields or become an expert.</p><h3>How to become a Python developer online</h3><p>A Python developer is an expert in the language, and is usually responsible for coding, designing and deploying back-end web applications for an organization in Python.<sup><a href="#5FzkLQvOLpQb2xrsThQR0O">4</a></sup> While a data scientist or machine learning engineer may use Python as a means to complete a project, a Python developer will be creating Python code to power other tools.</p><p>To learn Python programming, programmers should work on acquiring deep knowledge of the language. Some of the core aspects of Python for a developer to learn include:</p><ul><li><p><b>Python libraries:</b> a collection of pre-written code that can be reused by developers to achieve complex tasks, such as pandas or NumPy.</p></li><li><p><b>Python frameworks:</b> modules or packages that help developers while creating a web application, such as Django, Flask, and CherryPy.</p></li><li><p><b>Object-relational Mapper (ORM) libraries: </b>abstraction on a relational database that allows a programmer to write in Python instead of SQL, such as SQLAlchemy or Django ORM.</p></li></ul><p>A Python developer may also be able to achieve these skills in formal learning environments, such as a <a class="text-link underline" href="https://www.edx.org/boot-camps/">boot camp</a>. However, more extended knowledge may be gained from a <a class="text-link underline" href="https://www.edx.org/bachelors/computer-data-sciences">bachelor’s in computer science</a> or even a <a class="text-link underline" href="https://www.edx.org/masters">master’s</a> degree.</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="Python FAQ" id="python-f-a-q"></a><a class="subnav-item" id="frequently-asked-questions"></a><div class="flex flex-col py-6 lg:py-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 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="">Why learn Python programming online?<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-putty-dark transition-transform duration-200"><path 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clip-rule="evenodd"></path></svg></button></h3><div data-state="closed" id="radix-:Rlasjjttrkva:" hidden="" role="region" aria-labelledby="radix-:R5asjjttrkva:" 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-:Rnasjjttrkva:" aria-expanded="false" data-state="closed" data-orientation="vertical" id="radix-:R7asjjttrkva:" class="flex flex-1 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="">How long does it take to learn Python?<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-putty-dark 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 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="">Should you learn Python 2 or Python 3?<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-putty-dark 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-:Rpasjjttrkva:" hidden="" role="region" aria-labelledby="radix-:R9asjjttrkva:" 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 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Retrieved July 5, 2022.</p></li><li id="2CpoF7VBfAlda5XilBGCjZ"><p><a class="text-link underline" href="https://www.geeksforgeeks.org/python-features/">Python Features</a>. (2022). <i>GeeksforGeeks</i>. Retrieved July 5, 2022.</p></li><li id="3Qb0QWK5pFQkn1GjveA8Pa"><p><a class="text-link underline" href="https://insights.stackoverflow.com/survey/2021#section-most-popular-technologies-programming-scripting-and-markup-languages">2021 Developer Survey</a>. (2021). <i>Stack Overflow</i>. Retrieved July 5, 2022.</p></li><li id="5FzkLQvOLpQb2xrsThQR0O"><p><a class="text-link underline" href="https://www.indeed.com/career/python-developer">What Does a Python Developer do?</a> (2022). <i>Indeed</i>. 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(2022). \",[\"$\",\"i\",\"2\",{\"children\":\"GeeksforGeeks\"}],\". Retrieved July 5, 2022.\"]}]}],[\"$\",\"li\",\"2\",{\"id\":\"3Qb0QWK5pFQkn1GjveA8Pa\",\"children\":[\"$\",\"p\",null,{\"children\":[[\"$\",\"a\",\"0\",{\"className\":\"text-link underline\",\"href\":\"https://insights.stackoverflow.com/survey/2021#section-most-popular-technologies-programming-scripting-and-markup-languages\",\"children\":\"2021 Developer Survey\"}],\". (2021). \",[\"$\",\"i\",\"2\",{\"children\":\"Stack Overflow\"}],\". Retrieved July 5, 2022.\"]}]}],[\"$\",\"li\",\"3\",{\"id\":\"5FzkLQvOLpQb2xrsThQR0O\",\"children\":[\"$\",\"p\",null,{\"children\":[[\"$\",\"a\",\"0\",{\"className\":\"text-link underline\",\"href\":\"https://www.indeed.com/career/python-developer\",\"children\":\"What Does a Python Developer do?\"}],\" (2022). \",[\"$\",\"i\",\"2\",{\"children\":\"Indeed\"}],\". 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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.edx.org/course/introduction-computer-science-harvardx-cs50x\"\u003eCS50x\u003c/a\u003e itself focuses on computer science more generally as well as programming wit"])</script><script>self.__next_f.push([1,"h 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\u003e33:T4f0,\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\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\u003e34: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 "])</script><script>self.__next_f.push([1,"last computer science courses. This run features lecture 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\u003e35:T4c8,\u003cp\u003eAn introduction to programming using Scratch, a visual programming language via which aspiring programmers can write code by dragging and dropping graphical blocks (that resemble puzzle pieces) instead of typing out text. Used at the start of Harvard College's introductory course in computer science, CS50, Scratch was designed at MIT's Media Lab, empowering students with no prior programming experience to design their own animations, games, interactive art, and stories. Using Scratch, this course introduces students to fundamentals of programming, found not only in Scratch itself but in traditional text-based languages (like Java and Python) as well. Topics include: functions, which are instructions that perform tasks; return values, which are results that functions provide; conditions, via which programs can decide whether or not to perform some action; loops, via which programs can take action again and again; variables, via which programs can remember information; and more. Ultimately, this course prepares students for subsequent courses in programming.\u003c/p\u003e\n\u003cp\u003eScratch is developed by the Lifelong Kindergarten Group at the MIT Media Lab. See \u003ca href=\"ht"])</script><script>self.__next_f.push([1,"tps://scratch.mit.edu/\"\u003escratch.mit.edu\u003c/a\u003e.\u003c/p\u003e36: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) technology 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\u003e37:T5a2,\u003cp\u003eVarious programming languages have gained popularity since 1970. Starting with Assembly, C, C++, and moving towards Java, Python, and finally building of backend and frontend frameworks, all of these became popular a"])</script><script>self.__next_f.push([1,"nd were or getting replaced with some other language or framework. Shell programming (scripting) is the only programming language that has been popular and the choice of programmers, testers, system administrators, etc., from 1970 to date (21st century). Even today, all of us rely heavily on shell programming or its variants for our day to day tasks, configuring software environments, and software automation. ****\u003c/p\u003e\n\u003cp\u003eScripting is a vehicle to automate and it is ubiquitous in any small to large scale software development. Apart from programming, various phases of software development include unit testing, producing software builds, configuration, integration testing, etc. In all these phases (from development to production), automation is an essential key to faster delivery, hence, having less waiting time. This course will introduce you to the programming aspects of the most popular Linux shell, which has been used for the last 50 years, older than any programming language that you have heard of. \u003c/p\u003e\n\u003cp\u003eParticipants taking this course will be able to use the terminal (shell commands) with confidence, thus, increasing their productivity and saving effort in performing tedious and redundant tasks.\u003c/p\u003e38:T5ca,\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 Machine Learning with Python course dives into the basics of machine learning using Python, an approachable and well-known programming language. You'll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.\u003c/p\u003e\n\u003cp\u003eWe'll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such as Train/Test Split, Root Mean Squared Error (RMSE), and Random Forests. Along "])</script><script>self.__next_f.push([1,"the way, you’ll look at real-life examples of machine learning and see how it affects society in ways you may not have guessed!\u003c/p\u003e\n\u003cp\u003eMost importantly, you will transform your theoretical knowledge into practical skill using hands-on labs. Get ready to do more learning than your machine!\u003c/p\u003e\n\u003cp\u003eWe'll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such asTrain/Test Split, Root Mean Squared Error and Random Forests.\u003c/p\u003e\n\u003cp\u003eMostimportantly, you will transform your theoretical knowledge into practical skill using hands-on labs. Get ready to do more learning than your machine!\u003c/p\u003e39:T53a,\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\u003eComputer Vision is one of the most exciting fields in Machine Learning, computer science and AI. It has applications in many industries such as self-driving cars, robotics, augmented reality, face detection in law enforcement agencies.\u003c/p\u003e\n\u003cp\u003eIn this intro-level course, you will learn about computer vision and its various applications across many industries. As part of this course, you will utilize Python, Watson AI, and OpenCV to process images and interact with image classification models. You will also build, train, and test your own custom image classifiers.\u003c/p\u003e\n\u003cp\u003eThis is a hands-on course and involves several labs and exercises. All the labs will be performed in the Cloud and you will be provided access to a Cloud environment completely free of charge.\u003c/p\u003e\n\u003cp\u003eAt the end of the course, you will create your own computer vision web app and deploy it to the Cloud.\u003c/p\u003e\n\u003cp\u003eThis course does not require any prior Machine Learning or Computer Vision experience, however, some knowledge of Python programming language is necessary.\u003c/p\u003e3a:T532,\u003cp\u003e\u003cem\u003ePlease Note: Learners "])</script><script>self.__next_f.push([1,"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\u003e3b: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,"])</script><script>self.__next_f.push([1," 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. 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In this hands-on, introductory course, you will examine all the forms in which data exists, learn tools that uncover relationships between data, and leverage basic algorithms to understand the world from a new perspective.\u003c/p\u003e\n\u003cp\u003eWhether you're a high school student or someone switching careers, all you need to get started in this course is a curiosity about the topic of machine learning and a willingness to tinker around with your computer.\u003c/p\u003e\n\u003cp\u003eThe course is taught by modules. Within each module, you'll have access to videos, short exercises, and a final capstone project. In Module 1, you'll begin by looking at different kinds of data. To help you explore the data, you'll dive right into some programming with the Python programming language. You don't need to have any programming background, we will guide you on how to leverage Python to explore and visualize any data.\u003c/p\u003e\n\u003cp\u003eOne kind of data you'll work with is data that relates one variable to another. Coming up with a relationship between two variables—one depending on the other—is at the center of Module 2. In that module, you'll build up some core concepts before seeing your first machine learning algorithm. The goal is to use programming to create models that describe mathematical relationships between data. You'll be able to see how good the model is and use it to make predictions about new data.\u003c/p\u003e\n\u003cp\u003eIn Module 3, you'll see a discussion about where imperfections in collected data might come from. You rarely have perfectly “clean” data sets, so it's important to understand how imperfections impact the model that an algorithm might come up with. To this end, we will introduce the notion of data distributions and build up to the concepts of biased and unbiased noise.\u003c/p\u003e\n\u003cp\u003eAnother kind of data you'll work with is data that belongs in different groups (or classes). Creating a model that predicts what group data belongs in is at the center of Module 4. You'll work through different ways of thinking about this problem and see three different ways of approaching making such groupings (classification).\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3d:T5b4,\u003cp\u003eThe building industry is exploding with data sources that impact the energy performance of the built environment and health and well-being of occupants. Spreadsheets just don’t cut it anymore as the sole analytics tool for professionals in this field. Participating in mainstream data science courses might provide skills such as programming and statistics, however the applied context to buildings is missing, which is the most important part for beginners. \u003c/p\u003e\n\u003cp\u003eThis course focuses on the development of data science skills for professionals specifically in the built environment sector. It targets architects, engineers, construction and facilities managers with little or no previous programming experience. An introduction to data science skills is given in the context of the building life cycle phases. Participants will use large, open data sets from the design, construction, and operations of buildings to learn and practice data science techniques. \u003c/p\u003e\n\u003cp\u003eEssentially this course is designed to add new tools and skills to supplement spreadsheets. Major technical topics include data loading, processing, visualization, and basic machine learning using the Python programming language, the Pandas data analytics and sci-kit learn machine learning libraries, and the web-based Colaboratory environment. In addition, the course will provide numerous learning paths for various built environment-related tasks to facilitate further growth.\u003c/p\u003e3e: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 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 b"])</script><script>self.__next_f.push([1,"rief 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\u003e3f: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,"40:T466,\u003cp\u003eThis course gives you a thorough introduction to the python programming language. You'll start by learning the definition of python, its history, semantics, and how it works. The first week concludes by writing a python script to solve a problem. In Week 2, you'll learn about Serverless Architectures and understand the differences between microservices, service-oriented and monolith architectures. You'll build a microservices architecture by learning how to use the following tools: AWS Lambda, Amazon API Gateway, and Cloud9. It's then time to go deep with AWS Databases. You'll see Amazon RDS and Amazon DynamoDB in action, visualizing the important role they play in developing applications in python. The course's final week will cover application integration services. Amazon's Simple Notification Service (SQS), Simple Queue Service (SQS), and EventBridge take center stage as important “glue” components for scalable, cloud-based applications. You'll close the course with a hands-on project, writing some code to use some hardcoded data to write to dynamodb and then convert that into a lambda function.\u003c/p\u003e41: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 Cer"])</script><script>self.__next_f.push([1,"tificate 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\u003e42: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,"43:T58b,\u003cp\u003eThis is Harvard’s famous CS50 course bundled together with CS50’s Introduction to Python as one program: Computer Science for Python Programming. Get an introduction to the intellectual enterprises of computer science and the art of programming using a language called Python.\u003c/p\u003e\r\n \r\n\u003cp\u003eIn this two-course Professional Certificate program you can kick off your programming journey with an Introduction to Computer Science (CS50x), which focuses on computer science more generally as well as an entry into programming with Python along with C, SQL, and JavaScript, plus HTML and CSS.\u003c/p\u003e\r\n\r\n\u003cp\u003eTo further your skills for programming with Python, you can then turn to course 2, CS50’s Introduction to Programming with Python (CS50P). In this course, you will learn how to read and write code as well as how to test and \"debug\" it. This course recaps lessons learned in CS50x and then dives all the more deeply into Python itself.\u003c/p\u003e\r\n \r\n\u003cp\u003eThe program features hands-on opportunities for 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.\r\nBoth courses in this program are entry-level courses for students with no programming experience or prior background, taught by \u003ca href=\"edx.org/bio/david-j-malan\"\u003eDavid J. Malan\u003c/a\u003e who teaches CS50 on the Harvard campus - one of Harvard University’s largest courses!\u003c/p\u003e44:T4f3,\u003cp\u003eWith the advent of systems like AWS Lambda, the term serverless gained much popularity. However, many people are still unsure what it is for, and how it can help them build applications faster than traditional approaches. Other potential users are turned off by the arbitrary limits and lock-in of cloud-based serverless products.\u003c/p\u003e\n\u003cp\u003eThis course will teach you what “serverless” means and how it can be made portable with open source frameworks. You will get a good overview of the CNCF serverless landscape, and a first-hand experience in building and deploying code, directly to a Kubernetes cluster."])</script><script>self.__next_f.push([1,"\u003c/p\u003e\n\u003cp\u003eYou will build functions using Python and learn how to configure them, manage secrets, add dependencies via pip, learn how to work with binary data, and add authentication. In the second part of the course you will explore the operational side of functions with Kubernetes. This will introduce auto-scaling, metrics, dashboards and how to expose them securely on the Internet with TLS.\u003c/p\u003e\n\u003cp\u003eIf you are a developer or an IT operator interested in exploring new approaches for building software, and prefer to be able to set your own limits when it comes to things like timeouts and choice of programming language, this is the course for you!\u003c/p\u003e45:Ta42,"])</script><script>self.__next_f.push([1,"\u003cp\u003eSpatial Computational Thinking is increasingly being recognised as a fundamental skill for various spatial disciplines. It involves idea formulation, algorithm development, solution exploration, with a focus on the manipulation of geometric and semantic datasets. In this Professional Certificate Program, you will learn the theoretical knowledge and practical skills required for leveraging computation for the manipulation of various types of spatial data.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe program consists of three courses, starting with the fundamentals and gradually increase in complexity.\r\n\u003cul\u003e\r\n\u003cli\u003eThe first course – Procedural Modelling – focuses on the fundamentals of procedural programming for generating spatial models. You will learn how to code, using functions, data structures and control-flow statements. You will write procedures for generating geometric models with attribute data. By the end of the course, you will be able to write your own procedures for generating spatial information models.\u003c/li\u003e\r\n\u003cli\u003eThe second course – Generative Modelling – focuses on generating spatial information models capturing various relationships and constraints. You will learn a set of geometric modelling concepts for generating spatial models. You will create multiple procedures that annotate and query your models using attribute data. By the end of the course, you will be able to create your own scripts consisting of multiple procedures working together to generate complex spatial information models.\u003c/li\u003e\r\n\u003cli\u003eThe third course – Performative Modelling – focuses on evaluating alternative spatial models to support evidence-based decision making. You will learn methods for calculating various spatial performance metrics related to the built environment. You will use these performance metrics to carry out comparative analysis of design options. By the end of the course, you will be able to create scripts that automate the process of generating and analysing alternative design options.\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eAll the courses will use a free browser-based software to write algorithms for generating and visualizing 3D models, called Möbius Modeller. The programming language uses a visual programming approach combining flowcharts with procedural programming. This will allow you to quickly learn the knowledge and skills required for writing complex computational procedures for generating, analysing, and visualizing complex 3D spatial information models. The programming knowledge you gain will be highly transferable if you later choose to use other languages such as JavaScript, Python, Java, and PHP.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"46: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,"47:T7b6,\u003cp\u003eBroadly speaking, functional programming is a style of programming in which the primary method of computation is the application of functions to arguments. Among other features, functional languages offer a compact notation for writing programs, powerful abstraction methods for structuring programs, and a simple mathematical basis that supports reasoning about programs.\u003c/p\u003e\n\u003cp\u003eFunctional languages represent the leading edge of programming language design, and the primary setting in which new programming concepts are introduced and studied. All contemporary programming languages such as Hack/PHP, C#, Visual Basic, F#, C++, JavaScript, Python, Ruby, Java, Scala, Clojure, Groovy, Racket, … support higher-order programming via the concept of closures or lambda expressions.\u003c/p\u003e\n\u003cp\u003eThis course will use Haskell as the medium for understanding the basic principles of functional programming. While the specific language isn't all that important, Haskell is a pure functional language so it is entirely appropriate for learning the essential ingredients of programming using mathematical functions. It is also a relatively small language, and hence it should be easy for you to get up to speed with Haskell.\u003c/p\u003e\n\u003cp\u003eOnce you understand the Why, What and How that underlies pure functional programming and learned to “think like a fundamentalist”, we will apply the concepts of functional programming to “code like a hacker” in mainstream programming languages, using Facebook’s novel Hack language as our main example. This course assumes no prior knowledge of functional programming, but assumes you have at least one year of programming experience in a regular programming language such as Java, .NET, Javascript or PHP.\u003c/p\u003e\n\u003cp\u003eLICENSE\u003cbr /\u003e\n\u003cem\u003eThe course materials of this course are Copyright Delft University of Technology and are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA) 4.0 International License.\u003c/em\u003e\u003c/p\u003e48:T5c2,\u003cp\u003eData engineering is a growth tech segment, with conside"])</script><script>self.__next_f.push([1,"rable 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\u003e49: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,"4a:Ta86,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIn this course, you will explore and learn the best methods and practices in recommender systems, which are an essential component of the online ecosystem. This course was developed by IVADO and HEC Montréal as part of a workshop that took place in Montreal. You will be accompanied throughout and given concrete examples by seven international experts from both Academia and Industry.\u003c/p\u003e\n\u003cp\u003eRecommender systems are algorithms that find patterns in user behaviour to improve personalized experiences and understand their environment. They are ubiquitous and are most often used to recommend items to users, for example, books, movies, but also possible friends, food recipes or even relevant documentation in large software projects, or papers of interest to scientists.\u003c/p\u003e\n\u003cp\u003eThe content of this MOOC is an introduction to the field of recommender systems. The outline includes: machine learning for recommender systems followed by an introduction to evaluation methods; advanced modelling; contextual bandits; ranking methods; and fairness and discrimination in recommender systems.\u003c/p\u003e\n\u003cp\u003eThe course is primarily intended for industry professionals and academics with basic (first-year undergraduate) knowledge in mathematics and programming (ideally Python). Graduate students in science and engineering (mainly those who are not yet familiar with machine learning and recommender systems) 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWe estimate that it takes 6 weeks to follow this class.\u003c/strong\u003e The course is divided into relevant segments that you may watch at your own pace. There are comprehensive quizzes at the end of each segment to evaluate your understanding of the content. You will also practice recommender systems algorithms thanks to a tutorial guided by an expert. Also, a second self-practice module will be offered to participants who will register for the course with the Verified Certificate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWe welcome you to this special learning journey of Recommender Systems: Behind the Screen!\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis course is brought to you by IVADO, HEC Montréal and Université de Montréal.\u003ca href=\"https://ivado.ca/en/\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eIVADO is a Québec-wide collaborative institute in the field of digital intelligence.\u003ca href=\"https://www.hec.ca/en/index.html\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eHEC Montréal is a French-language university offering internationally renowned management education and research.\u003ca href=\"https://www.umontreal.ca/en/\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eUniversité de Montréal is one of the world’s leading research universities.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4b: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,"4c:T5d3,\u003cp\u003eThe volume of data generated daily is staggering—more than 2.5 quintillion bytes every 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\u003e4d:T8ed,"])</script><script>self.__next_f.push([1,"\u003cp\u003eDo you have an interest in biology and quantitative tools? Do you know computational methods but do not realize how they apply to biological problems? Do you know biology but do not understand how scientists really analyze complicated data? 7.QBWx: Quantitative Biology Workshop is designed to give learners exposure to the application of quantitative tools to analyze biological data at an introductory level. The Biology Department of MIT has run this workshop-style course as part of a one-week outreach program for students from other universities. With 7.QBWx, we can give more learners from around the world the chance to discover quantitative biology. We hope that this series of workshops encourages learners to explore new interests and take more biology and computational courses.\u003c/p\u003e\n\u003cp\u003eWe expect that learners from 7.00x Introduction to Biology - The Secret of Life or an equivalent course can complete this workshop-based course without a background in programming. The course content will introduce programming languages but will not teach any one language in a comprehensive manner. The content of each week varies. We want learners to have an introduction to multiple languages and tools to find a topic that they would want to explore more. We recommend that learners try to complete each week to find what interests them the most. \u003c/p\u003e\n\u003cp\u003eThis workshop includes activities on the following biological topics: population biology, biochemical equilibrium and kinetics, molecular modeling of enzymes, visual neuroscience, global and single-cell gene expression, development, and genomics. The tools and programming languages include MATLAB, PyMOL, Python, and R. This course does not require learners to download MATLAB. All MATLAB activities run and are graded within the edX platform. We do recommend that participants download a few other free tools for the activities so that they learn how to use the same tools and programs that scientists use.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWorkshop Content Creators and Residential Leaders\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGregory Hale, Michael Goard, Ben Stinson, Kunle Demuren, Sara Gosline, Glenna Foight, Leyla Isik, Samir El-Boustani, Gerald Pho, and Rajeev Rikhye\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResidential Outreach Workshop Organizer and Creator\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMandana Sassanfar\u003c/p\u003e"])</script><script>self.__next_f.push([1,"4e: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\u003e4f: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 "])</script><script>self.__next_f.push([1,"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\u003e50: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 return 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"])</script><script>self.__next_f.push([1," 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\u003e51: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 the student how to create and fit their own models.\u003c/p\u003e52:T648,\u003cp\u003eThis eight-course Professional Certificate from IBM prepares you with job-ready skills for an entry level data analyst role. Position yourself competitively and power your data analyst career for a job in a thriving market or leverage foundational data skills to explore problems in an increasingly data-driven professional world. The U.S. Bureau of Labor Statistics projects a growth rate of 20% in the data analytics industry until 2028.\u003c/p\u003e\r\n\r\n\u003cp\u003eYou will learn the core principles of data analysis, participate in hands-on skills-based practice, and gain the knowledge to help companies make smarter business decisions. You will work with a variety of data sources, project scenarios, and data analysis tools, incl"])</script><script>self.__next_f.push([1,"uding Excel, SQL, Python, Jupyter Notebooks, and Cognos Analytics. These online learning tools will offer you practical experience with data manipulation and the application of analytical and data visualization techniques.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis Professional Certificate does not require any prior programming or statistical skills, and is suitable for learners with or without college degrees. A willingness to learn, basic computer literacy, comfort working with numbers, high school math, and a desire to enrich your profile with valuable skills is all you need to get started in this course.\u003c/p\u003e\r\n\r\n\u003cp\u003eWith the IBM Data Analyst Professional Certificate you will develop the confidence and portfolio to begin a career as an associate or junior data analyst by learning the essential skills that help professionals in a wide range of job functions and industries explore business problems.\u003c/p\u003e53:T5d1,\u003cp\u003eThe transformational impact of artificial intelligence (AI) is reshaping our world. Understanding its diverse applications, from creating intelligent machines to leveraging generative AI models, provides students, developers, and consultants a career edge.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe program consists of seven self-paced courses.\u003c/p\u003e\r\n\r\n\u003cp\u003eGain a firm understanding of AI and its applications and become familiar with key AI concepts, including deep learning, machine learning, neural networks, data science, and natural language processing.\u003c/p\u003e\r\n\r\n\u003cp\u003eYou'll delve deeper into generative AI, exploring foundation and large language models. 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-on Python skills to design, build, and deploy AI applicatio"])</script><script>self.__next_f.push([1,"ns 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\u003e54: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 experience 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\u003e55:T9cb,"])</script><script>self.__next_f.push([1,"\u003cp\u003eArtificial Intelligence (AI) and Machine Learning (ML) now impact every aspect of our daily lives and routines - whether in the workplace or at home. This program introduces you into this exciting and disruptive technology and arms you with applied AI skills.\u003c/p\u003e\r\n\r\n\u003cp\u003eML is fast-developing, disruptive, and sometimes controversial, but anyone can benefit from a guided tour of how the technology works, its benefits and drawbacks, along with its implications for society.\u003c/p\u003e \r\n\r\n\u003cp\u003eThis program is intended to cut through the hype, help you understand what AI really is, and what it isn’t. It covers why data is important and demonstrates the relationship between data, machine learning, and artificial intelligence to help you discuss and use these technologies with others.\u003c/p\u003e\r\n \r\n\u003cp\u003eThe program kicks off with a Google AI for Anyone course that covers the basics of AI and ML and how AI is used in the real world. Learning from Google AI experts and practitioners, you will explore what machine learning programming looks like at a high level learning techniques such as neural networks. Later in the course you'll acquire some hands-on experience teaching a computer to recognize images, sounds, and more.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe second course Google AI for JavaScript developers with TensorFlow.js builds upon the concepts introduced in course 1 but is still beginner friendly - you just need a basic knowledge of JavaScript, HTML, and CSS.\u003c/p\u003e \r\n\r\n\u003cp\u003eLearn how to give your web application superpowers to stand out from the crowd and create next generation web apps that can be used by billions of people in a single click - from computer vision to natural language processing.\u003c/p\u003e\r\n\r\n\u003cp\u003eYou'll learn how to use TensorFlow.js models and their APIs to create real working systems that solve problems that could impact real business ideas. Imagine offering a customer the ability to automatically block spam comments in a chat application or blog before they are even sent, or use state of the art models to build your very own smart security camera, and more.\u003c/p\u003e \r\n\r\n\u003cp\u003eBy leveraging TensorFlow.js - Google's open-source leading ML library for JavaScript - you will go deeper, learn how to use state of the art pre-made models, retrain existing models, and even take your first steps writing your own models to solve simple tasks taking your first steps as a Web Machine Learning Engineer.\u003c/p\u003e\r\n\r\n\u003cp\u003eKick off your Web AI and ML journey with this professional certificate program from Google today.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"56: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,"57: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,"58: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 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\u003e59: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 w"])</script><script>self.__next_f.push([1,"ill 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\u003e5a:T478,\u003cp\u003eLearn Python by doing! Codio courses are designed to engage and excite beginners. Instead of reading or watching a video, you’ll be asked to write and run code (don’t worry, we provide code snippets to help get you started!). Course materials are presented in smaller, easier-to-manage formats, making this an excellent course for learners with no prior programming experience.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program is intended for people without programming experience who seek to develop Python programming skills and learn about the underlying computer science concepts that will allow them to pick up other programming languages quickly. In these three courses, you will cover everything from fundamentals to object-oriented design.\u003c/p\u003e \r\n\r\n\u003cp\u003eThese topics will help prepare you to write basic programs that help them automate repetitive tasks such as modifying large files; architect larger applications into readable, maintainable, and scale-able code bases; understand the fundamentals of computer science presented in Python to ease learning of other programming languages; and develop a sense of comfort and confidence while programming.\u003c/p\u003e5b:T434,\u003cp\u003eCode and run your first Python program in minutes without installing anything! ****\u003c/p\u003e\n\u003cp\u003eThis course is design"])</script><script>self.__next_f.push([1,"ed for learners with limited coding experience, providing a solid foundation of not just Python, but core Computer Science and software development topics that can be transferred to other languages. The modules in this course cover object-oriented programming, inheritance, encapsulation, polymorphism, and other object-related topics. Completion of the prior 2 courses in this program is recommended.\u003c/p\u003e\n\u003cp\u003eTo allow for a truly hands-on, self-paced learning experience, this course is video-free. Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You’ll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to small, approachable coding exercises that take minutes instead of hours.\u003c/p\u003e5c:T402,\u003cp\u003eCode and run your first Python program in minutes without installing anything! ****\u003c/p\u003e\n\u003cp\u003eThis course is designed for learners with limited coding experience, providing a solid foundation of not just Python, but core Computer Science and software development topics that can be transferred to other languages. The modules in this course cover strings, functions, files, and recursion. Completion of Python: Basics Skills before taking this course is recommended.\u003c/p\u003e\n\u003cp\u003eTo allow for a truly hands-on, self-paced learning experience, this course is video-free. Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You’ll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to small, approachable coding exercises that take minutes instead of hours.\u003c/p\u003e5d:T552,\u003cp\u003ePython is one of the most popular and in-d"])</script><script>self.__next_f.push([1,"emand 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\u003e5e:T598,\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\u003cp\u003e- Writing scripts for automating tasks\u003c/p\u003e\n\u003cp\u003e-Web development\u003c/p\u003e\n\u003cp\u003e-Collecting data from websites (also known as “web scraping”)\u003c/p\u003e\n\u003cp\u003e-Scientific and numeric computing\u003c/p\u003e\n\u003cp\u003e-Data analysis\u003c/p\u003e\n\u003cp\u003e-Data visualization\u003c/p\u003e\n\u003cp\u003e-Machine learning\u003c/p"])</script><script>self.__next_f.push([1,"\u003e\n\u003cp\u003ePython is also a useful skill applicable to roles across a wide range of industries, including:\u003c/p\u003e\n\u003cp\u003e-Energy\u003c/p\u003e\n\u003cp\u003e-Finance\u003c/p\u003e\n\u003cp\u003e-Healthcare\u003c/p\u003e\n\u003cp\u003e-Marketing\u003c/p\u003e\n\u003cp\u003e-IT\u003c/p\u003e\n\u003cp\u003e-Retail\u003c/p\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.\u003c/p\u003e\n\u003cp\u003eThis Try It is offered in support of the Coding Boot Camp and Data Analytics Boot Camp with the Tecnológico de Monterrey. If you enroll in this Try It, your learner data will not be shared with the Tecnológico de Monterrey unless you enroll in the Coding Boot Camp or Data Analytics Boot Camp.\u003c/p\u003e5f:T59f,\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. 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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:\u003cbr /\u003e\n- Writing scripts for automating tasks\u003cbr /\u003e\n-Web development\u003cbr /\u003e\n-Collecting data from websites (also known as “web scraping”)\u003cbr /\u003e\n-Scientific and numeric computing\u003cbr /\u003e\n-Data analysis\u003cbr /\u003e\n-Data visualization\u003cbr /\u003e\n-Machine learning \u003c/p\u003e\n\u003cp\u003ePython is also a useful skill applicable"])</script><script>self.__next_f.push([1," to roles across a wide range of industries, including:\u003cbr /\u003e\n-Energy\u003cbr /\u003e\n-Finance\u003cbr /\u003e\n-Healthcare\u003cbr /\u003e\n-Marketing\u003cbr /\u003e\n-IT\u003cbr /\u003e\n-Retail \u003c/p\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. \u003c/p\u003e\n\u003cp\u003eThis Try It is offered in support of the Coding Boot Camp and Data Analytics Boot Camp with UC Irvine Division of Continuing Education. 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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:\u003cbr /\u003e\n- Writing scripts for automating tasks\u003cbr /\u003e\n-Web development\u003cbr /\u003e\n-Collecting data from websites (also known as “web scraping”)\u003cbr /\u003e\n-Scientific and numeric computing\u003cbr /\u003e\n-Data analysis\u003cbr /\u003e\n-Data visualization\u003cbr /\u003e\n-Machine learning \u003c/p\u003e\n\u003cp\u003ePython is also a useful skill applicable to roles across a wide range of industries, including:\u003cbr /\u003e\n-Ener"])</script><script>self.__next_f.push([1,"gy\u003cbr /\u003e\n-Finance\u003cbr /\u003e\n-Healthcare\u003cbr /\u003e\n-Marketing\u003cbr /\u003e\n-IT\u003cbr /\u003e\n-Retail \u003c/p\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. \u003c/p\u003e\n\u003cp\u003eThis Try It is offered in support of the Coding Boot Camp and Data Analytics Boot Camp with University of Richmond. 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No previous programming experience is required. \u003c/p\u003e\r\n\u003cp\u003eFrom analyzing large datasets to building web applications, Python can be used for a variety of projects including:\u003cbr /\u003e\r\n- Writing scripts for automating tasks\u003cbr /\u003e\r\n-Web development\u003cbr /\u003e\r\n-Collecting data from websites (also known as “web scraping”)\u003cbr /\u003e\r\n-Scientific and numeric computing\u003cbr /\u003e\r\n-Data analysis\u003cbr /\u003e\r\n-Data visualization\u003cbr /\u003e\r\n-Machine learning \u003c/p\u003e\r\n\u003cp\u003ePython is also a useful skill applicable to roles across a wide range of industries, including:\u003cbr /\u003e\r\n-Energy\u003cbr /\u003e\r\n-Finance\u003cbr /\u003e\r\n-Healthcare\u003cbr /\u003e\r\n-Marketing\u003cbr /\u003e\r\n-IT\u003cbr /\u003e\r\n-Retail \u003c/p\u003e\r\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. \u003c/p\u003e\r\n\u003cp\u003eThis Try It is offered in support of the Coding Boot Camp and Data Analytics Boot Camp with the University of Kansas. 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If you enroll in this Try It, your learner data will not be shared with The University of North Carolina at Chapel Hill unless you enroll in the Coding Boot Camp or Data Analytics Boot Camp.\u003c/p\u003e73:T591,\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. 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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\u003cp\u003e- Writing scripts for automating tasks\u003c/p\u003e\n\u003cp\u003e-Web development\u003c/p\u003e\n\u003cp\u003e-Collecting data from websites (also known as “web scraping”)\u003c/p\u003e\n\u003cp\u003e-Scientific and numeric computing\u003c/p\u003e\n\u003cp\u003e-Data analysis\u003c/p\u003e\n\u003cp\u003e-Data visualization\u003c/p\u003e\n\u003cp\u003e-Machine learning\u003c/p\u003e\n\u003cp\u003ePython is also a useful skill applicable to roles across a wide range of industries, including:\u003c/p\u003e\n\u003cp\u003e-Energy\u003c/p\u003e\n\u003cp\u003e-Finance\u003c/p\u003e\n\u003cp\u003e-Healthcare\u003c/p\u003e\n\u003cp\u003e-Marketing\u003c/p\u003e\n\u003cp\u003e-IT\u003c/p\u003e\n\u003cp\u003e-Retail\u003c/p\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.\u003c/p\u003e\n\u003cp\u003eThis Try It is offered in support of the Coding Boot Camp with the University of New Hampshire. 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Through hands-on projects, you’ll gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence.\u003c/p\u003e\r\n\r\n\u003cp\u003eBy course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own. Enroll now to gain expertise in one of the fastest-growing domains of computer science from the creators of one of the most popular computer science courses ever.\u003c/p\u003e78:T693,\u003cp\u003eThe demand for software engineers doubled according to the “State of Software Engineers” report by the tech specialist "])</script><script>self.__next_f.push([1,"Hired. Nearly every industry uses software to accelerate its growth, providing a vast number of opportunities. With the importance of software engineers and the technical skillset required, it isn’t surprising that according to Indeed.com, “The good starting software engineer salary in the US is considered to be $113,211 per year. Competitive starting salaries will vary depending on location and cost of living.”\u003c/p\u003e\n\u003cp\u003eThis course is suitable for anyone wanting to start in a software development related role, including those aspiring to be Software Engineers, IT Product and Project Managers, Scrum Masters, Information Developers, UI/UX Designers, QA Engineers, as well as Sales Executives, and Marketing Managers in the Tech industry. No prior programming experience or degree is required.\u003c/p\u003e\n\u003cp\u003eThis course teaches you about the power of the Software Development Lifecycle (SDLC) and software development methodologies such as Agile. You will explore fundamental programming principles and foundations of design, architecture, and deployment, investigate skills that a software engineer needs and identify job opportunities with hands-on projects. You will also learn about programming basics and software development tools and stacks.\u003c/p\u003e\n\u003cp\u003eThroughout this course, you’ll hear first-hand experiences from expert software engineers to geta feel for the job and their perspectives. After completing this introductory course, you will be familiar with the software engineering tools, technologies, terminology, processes, and practices.\u003c/p\u003e79:T7f6,\u003cp\u003eAre you a web engineer, designer, or creative thinker looking to apply AI or use Machine Learning in your next web application but are unsure where to begin? Or maybe you’re overwhelmed by other courses that focus more on the mathematical proofs than actually enabling you to use these new technologies for real world applications? This course offers a solution and the knowledge to be the \"missing manual\" for JavaScript users without a background in Machine Learning.\u003c/p\u003e\n\u003cp\u003eM"])</script><script>self.__next_f.push([1,"achine Learning (ML) on the web is growing faster than ever so now is the time to take your first steps too. Learn what the difference is between Artificial Intelligence, Machine Learning, and Deep Learning but also how to use such techniques practically through real examples using TensorFlow.js - Google's leading ML library for JavaScript.\u003c/p\u003e\n\u003cp\u003eSupercharge your next web app with superpowers - from classifying text in a blog post comment to automatically block spam, to using sensors like a webcam on your mobile device to alert you when your dog is on the couch after you left the house. The knowledge you learn could be applied to any business OR creative idea you have for your next project no matter what industry you may be working in.\u003c/p\u003e\n\u003cp\u003eBetter yet, JavaScript is one of few programming languages that can run everywhere enabling you to leverage the knowledge from this course and apply it client side, server side, via native apps, and even IoT devices allowing you to reuse what you learn across multiple environments.\u003c/p\u003e\n\u003cp\u003eThis course aims to educate, inspire, and enable you to rapidly create your next ML powered idea in this rapidly emerging industry while providing you with a solid foundation to understand the field and confidence to explore the industry further.\u003c/p\u003e\n\u003cp\u003eWeb applications are evolving, so sign up, join the fun, and get an edge over the competition. No background in ML is required to take the course. A basic, working knowledge of web technologies such as HTML, CSS, and JavaScript is highly recommended.\u003c/p\u003e7a: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 exe"])</script><script>self.__next_f.push([1,"cute, their features and limitations and how data scientists use these tools 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\u003e7b:T5fc,\u003cp\u003eThis course is a variant of HarvardUniversity's introduction to computer science, CS50, designed especially for lawyers (and law students). Whereas CS50 itself takes a bottom-up approach, emphasizing mastery of low-level concepts and implementation details thereof, this course takes a top-down approach, emphasizing mastery of high-level concepts and design decisions related thereto. Ultimately, it equips students with a deeper understanding of the legal implications of technological decisions made by clients. \u003c/p\u003e\n\u003cp\u003eThrough a mix of technical instruction and discussion of case studies, this course empowers students to be informed contributors to technology-driven conversations. In addition, it prepares students to formulate technology-informed legal arguments and opinions. Along the way, it equips students with hands-on experience with Python and SQL, languages via which they can mine data for answers themselves. \u003c/p\u003e\n\u003cp\u003eTopics include algorithms, cloud computing, databases, networking, privacy, programming, scalability, security, and more, with a particular emphasis on understanding how the work developers do and the technological solutions they employ may impact clients. Students emerge from this course with first-hand appreciation of how it all works and all the more confident in the factors that should guide their decision-making. \u003c/p\u003e\n\u003cp\u003eKeywords:law firm, computer programming, programming skills, computer programmers, patent attorney, legal pra"])</script><script>self.__next_f.push([1,"ctice, legal services, legal education, patent law\u003c/p\u003e7c:Tca6,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThe demand for software engineers and full stack developers is set to grow 25% p.a. (U.S. Bureau of Labor Statistics). This comprehensive MicroBachelors program not only gives you job-ready skills and practical experience employers are looking for, but also gives you valuable college credits towards your degree.\u003c/p\u003e\r\n\r\n\u003cp\u003eWithin just 6-8 months, you could be applying for your first full stack development job. No prior programming experience is required to get started. Why do businesses need full stack developers? With their enviable ability to combine frontend and backend programming expertise, full stack engineers create dynamic web and cloud solutions that contribute to business success.\r\n\u003c/p\u003e\r\n\r\n\u003cp\u003eWhether you're an adaptable professional seeking to transition into tech or a creative student/graduate eager to begin an IT career, this IBM Full Stack Application Development MicroBachelors is your ideal path for entering the field.\u003c/p\u003e\r\n\r\n\u003cp\u003eGuided by IBM experts, you’ll learn the tools and technologies businesses use to build, deploy, test, run, and manage full stack cloud-native applications. You’ll build cloud-based applications, participate in hands-on labs, and complete projects that develop the job-ready skills employers check for on a resume.\u003c/p\u003e\r\n\r\n\u003cp\u003eAs you learn, you'll dive into technical subjects such as cloud infrastructure, cloud native practices, and agile software development. You’ll work with HTML, CSS, and JavaScript, and explore CI/CD, containers, Docker, and Kubernetes. You’ll learn about OpenShift, Istio, Python programming, and get hands-on with databases, NoSQL, SQL, Django, and Bootstrap. Plus, you’ll explore application security, microservices, and serverless computing. And you'll be immersed in essential topics like GitHub, Node.js, React, and DevOps, building the skills and knowledge you need for a successful career in software engineering. In the final capstone project, you’ll develop a cloud- native application using resources like GitHub, IBM Cloud services, and various open-source frameworks, and you’ll apply your knowledge of cloud-native languages, database management, AI/machine learning, and CI/CD. This will give you valuable, verified experience you can talk about in interviews. If you’re looking to build a rewarding career in full stack development, this IBM Full Stack Application Development MicroBachelors will give you the job- ready knowledge, practical skills, industry-recognized credentials, and valuable college credits that open doors to highly promising career opportunities.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ci\u003e\"We're delighted that IBM is our first corporate partner to offer a MicroBachelors program on edX. Not only is IBM a longtime partner in delivering online education that equips learners with real skills for the workplace, but they are innovating with us as we meet the needs of learners without a college degree with our MicroBachelors programs. This particular program is incredible because it's for people with no prior programming or cloud experience, and prepares them for an entry-level role as a full stack developer with the option to get college credit from our university credit partner.\" - Anant Agarwal, edX Founder and CEO\u003c/i\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"7d:T49d,\u003cp\u003eA good algorithm usually comes together with a set of good data structures that allow the algorithm to manipulate the data efficiently. In this course, part of the Algorithms and Data Structures MicroMasters program, we consider the common data structures that are used in various computational problems. You will learn how these data structures are implemented in different programming languages and will practice implementing them in our programming assignments. This will help you to understand what is going on inside a particular built-in implementation of a data structure and what to expect from it. You will also learn typical use cases for these data structures.\u003c/p\u003e\n\u003cp\u003eA few examples of questions that we are going to cover in this course are:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eWhat is a good strategy of resizing a dynamic array?\u003c/li\u003e\n\u003cli\u003eHow priority queues are implemented in C++, Java, and Python?\u003c/li\u003e\n\u003cli\u003eHow to implement a hash table so that the amortized running time of all operations is O(1) on average?\u003c/li\u003e\n\u003cli\u003eWhat are good strategies to keep a binary tree balanced?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eWe look forward to seeing you in this course! We know it will make you a better programmer.\u003c/p\u003e7e:T481,\u003cp\u003eThis course is an introduction to microservices and the TARS framework for beginners. TARS is a new generation distributed microservice applications framework designed to support multiple programming languages, including C++, Golang, Java, Node.js, PHP, and Python, which allows developers and enterprises to quickly build stable and reliable applications that run at scale.\u003c/p\u003e\n\u003cp\u003eThis course will show you why more and more developers and companies have chosen TARS to build their services. It is designed for engineers working in microservices, as well enterprise managers interested in exploring internal technical architectures, especially working for digital transmission in the traditional industry. In addition, anyone interested in software architecture will benefit from this course.\u003c/p\u003e\n\u003cp\u003eYou will learn how to efficiently develop micro"])</script><script>self.__next_f.push([1,"services programs using different programming languages and quickly deploy the corresponding services into applications. Upon completion, you will wield a powerful skill for understanding microservices architecture and should be able to quickly build stable and reliable applications based on TARS.\u003c/p\u003e7f:T4e6,\u003cp\u003eBuilding on your prior knowledge of variables and operators, this course gets into the meat of programming. Organized into five chapters, this course starts by covering the fundamentals of what control structures are and what they do, then moves on to four common control structures in Python. Conditionals let you modify what your program does based on the values of incoming variables. Loops let you repeat tasks for multiple values or while certain conditions hold true. Functions let you encapsulate complex reasoning into reusable chunks of code. Error handling lets you intelligently recover from anticipated and unanticipated glitches. \u003c/p\u003e\n\u003cp\u003eBy the end of this course, you'll be able to write complex programs in Python that perform useful reasoning. For example, you could write a program that calculates your weight on other planets, calculates the standard deviation of a series of numbers, or checks for the validity of an incoming password. \u003c/p\u003e\n\u003cp\u003eStructurally, the course is comprised of several parts. Instruction is delivered via a series of short (2-3 minute) videos. In between those videos, you'll complete both multiple choice questions and coding problems to demonstrate your knowledge of the material that was just covered.\u003c/p\u003e80:T471,\u003cp\u003eThe web is a crucial part of our everyday lives. We rely on websites not just for entertainment and social networking, but for our professions, our finances, our education, and even aspects of our health care. The technologies that run these services are intricate and varied, but there are frameworks and principles that use common languages like HTML and Python that can give you a jump start in building your own web apps.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis professional certificate series "])</script><script>self.__next_f.push([1,"combines CS50’s legendary Introduction to Computer Science course with a new program that takes a deep dive into the design and implementation of web apps with Python, JavaScript, and SQL using frameworks like Flask, Django, and Bootstrap.\u003c/p\u003e\r\n\r\n\u003cp\u003eThrough hands-on projects, you'll learn to write and use APIs, create interactive UIs, and leverage cloud services like GitHub and Heroku. You'll emerge with knowledge and experience in principles, languages, and tools that empower you to design and deploy applications. Join now to program your own web applications and gain critical skills in database design, scalability, security, and user experience.\u003c/p\u003e81:Ta4c,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis is \u003cstrong\u003eCS50x\u003c/strong\u003e , Harvard University's introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors alike, with or without prior programming experience. An entry-level course taught by David J. Malan, \u003cstrong\u003eCS50x\u003c/strong\u003e teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, Python, SQL, and JavaScript plus CSS and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. The on-campus version of \u003cstrong\u003eCS50x\u003c/strong\u003e , CS50, is Harvard's largest course.\u003c/p\u003e\n\u003cp\u003eStudents who earn a satisfactory score on 9 problem sets (i.e., programming assignments) and a final project are eligible for a certificate. This is a self-paced course–you may take \u003cstrong\u003eCS50x\u003c/strong\u003e on your own schedule.\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=\"https://vpal.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,"82:T839,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cb\u003eThis Xseries is only for students in high school.\u003c/b\u003e Students not in high school should register for \u003ca href=\"https://www.edx.org/course/cs50s-understanding-technology\"\u003eCS50T\u003c/a\u003e and \u003ca href=\"https://www.edx.org/course/cs50s-introduction-to-computer-science\"\u003e CS50x\u003c/a\u003e separately.\u003c/p\u003e\r\n\r\nThis is CS50 AP, Harvard University's introduction to the intellectual enterprises of computer science and the art of programming for students in high school, which satisfies the College Board's \u003ca href=\"https://apcentral.collegeboard.org/courses/ap-computer-science-principles\"\u003eAP Computer Science Principles\u003c/a\u003e (CSP) curriculum framework. Students in high school may receive AP credit for this course provided their school approves the credit and administers the College Board's \u003ca href=\"https://apcentral.collegeboard.org/courses/ap-computer-science-principles/exam\"\u003eCreate Performance Task\u003c/a\u003e by Friday, April 30, 2021, 11:59 PM Eastern Time, and \u003ca href=\"https://apcentral.collegeboard.org/courses/ap-computer-science-principles/exam\"\u003eEnd-of-Course Multiple-Choice Exam\u003c/a\u003e on Thursday, May 13, 2021, 8:00 AM local time. \u003cb\u003eStudents should make all such arrangements in advance with their school directly.\u003c/b\u003e Students who earn a satisfactory score on all problem sets and a final project are also eligible to receive a verified certificate from HarvardX.\u003c/p\u003e\r\n\r\n\u003cp\u003eAn entry-level course for students with no prior background, taught by \u003ca href=\"https://www.edx.org/bio/david-j-malan\"\u003e David J. Malan\u003c/a\u003e, CS50 AP teaches students how to think algorithmically and solve problems efficiently. Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, and software engineering. Languages include C, Python, and SQL plus students’ choice of: HTML, CSS, and JavaScript (for web development); Java or Swift (for mobile app development); or Lua (for game development). Problem sets inspired by the arts, humanities, social sciences, and sciences. Course culminates in a final project.\u003c/p\u003e \r\n\r\n\u003cp\u003eCS50 itself is Harvard University's largest course on campus.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"83: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\u003e84: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,"85: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\u003e86: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 "])</script><script>self.__next_f.push([1,"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 practical 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\u003e87:T503,\u003cp\u003eOur data boot camp is a 24-week, part-time program that gives you the knowledge and skills to conduct robust analytics on a host of real-world problems. 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The “internet of things.” Augmented reality. Automa"])</script><script>self.__next_f.push([1,"tion. 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 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. 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The program includes lectures, lab work, and projects.\n-Programming languages: Students learn to write code in various programming languages, such as Java, Python, and C++, and to apply programming constructs such as loops, functions, and conditionals to solve problems.\n-Data structures and algorithms: Students learn how to represent and manipulate data efficiently using structures such as arrays, lists, stacks, queues, and trees, and how to design and analyze algorithms for searching, sorting, and graph traversal.\n-Software engineering principles: Students learn how to apply principles of software engineering, such as design patterns, testing, and version control, to build maintainable and scalable software systems.\n-Computer organization and architecture: Students learn how computers work at a low level, including topics such as digital logic, instruction set architecture, and memory hierarchy.\n-Operating systems: Students learn about the design and implementation of operating systems, including topics such as process management, memory management, and file systems.\n-Databases: Students learn how to design and use databases to store, manipulate, and retrieve data using query languages such as SQL and to work with different database management systems such as MySQL and Oracle.\n-Computer networks: Students learn how to design and manage computer networks, including topics such as TCP/IP, routing, and network security.\n-Artificial intelligence: Students learn about the principles and applications of artificial intelligence, including machine learning, natural language processing, and computer vision.\n-Mathematics: Students learn mathematical concepts and methods that are foundational to computer science, such as discrete mathematics, calculus, and linear algebra.\n-Web development: Students learn how to design and develop web applications using languages and tools such as HTML, CSS, JavaScript, and web frameworks such as Django and Ruby on Rails."])</script><script>self.__next_f.push([1,"a0:T9f5,"])</script><script>self.__next_f.push([1,"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-In an undergraduate computer science degree, students learn programming, software engineering, computer organization, databases, computer networks, artificial intelligence, and mathematics. The program includes lectures, lab work, and projects.\n-Programming languages: Students learn to write code in various programming languages, such as Java, Python, and C++, and to apply programming constructs such as loops, functions, and conditionals to solve problems.\n-Data structures and algorithms: Students learn how to represent and manipulate data efficiently using structures such as arrays, lists, stacks, queues, and trees, and how to design and analyze algorithms for searching, sorting, and graph traversal.\n-Software engineering principles: Students learn how to apply principles of software engineering, such as design patterns, testing, and version control, to build maintainable and scalable software systems.\n-Computer organization and architecture: Students learn how computers work at a low level, including topics such as digital logic, instruction set architecture, and memory hierarchy.\n-Operating systems: Students learn about the design and implementation of operating systems, including topics such as process management, memory management, and file systems.\n-Databases: Students learn how to design and use databases to store, manipulate, and retrieve data using query languages such as SQL and to work with different database management systems such as MySQL and Oracle.\n-Computer networks: Students learn how to design and manage computer networks, including topics such as TCP/IP, routing, and network security.\n-Artificial intelligence: Students learn about the principles and applications of artificial intelligence, including machine learning, natural language processing, and computer vision.\n-Mathematics: Students learn mathematical concepts and methods that are foundational to computer science, such as discrete mathematics, calculus, and linear algebra.\n-Web development: Students learn how to design and develop web applications using languages and tools such as HTML, CSS, JavaScript, and web frameworks such as Django and Ruby on Rails."])</script><script>self.__next_f.push([1,"a1:Ta0b,"])</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 computer science degree, students learn programming, software engineering, computer organization, databases, computer networks, artificial intelligence, and mathematics. The program includes lectures, lab work, and projects.\n-Programming languages: Students learn to write code in various programming languages, such as Java, Python, and C++, and to apply programming constructs such as loops, functions, and conditionals to solve problems.\n-Data structures and algorithms: Students learn how to represent and manipulate data efficiently using structures such as arrays, lists, stacks, queues, and trees, and how to design and analyze algorithms for searching, sorting, and graph traversal.\n-Software engineering principles: Students learn how to apply principles of software engineering, such as design patterns, testing, and version control, to build maintainable and scalable software systems.\n-Computer organization and architecture: Students learn how computers work at a low level, including topics such as digital logic, instruction set architecture, and memory hierarchy.\n-Operating systems: Students learn about the design and implementation of operating systems, including topics such as process management, memory management, and file systems.\n-Databases: Students learn how to design and use databases to store, manipulate, and retrieve data using query languages such as SQL and to work with different database management systems such as MySQL and Oracle.\n-Computer networks: Students learn how to design and manage computer networks, including topics such as TCP/IP, routing, and network security.\n-Artificial intelligence: Students learn about the principles and applications of artificial intelligence, including machine learning, natural language processing, and computer vision.\n-Mathematics: Students learn mathematical concepts and methods that are foundational to computer science, such as discrete mathematics, calculus, and linear algebra.\n-Web development: Students learn how to design and develop web applications using languages and tools such as HTML, CSS, JavaScript, and web frameworks such as Django and Ruby on Rails."])</script><script>self.__next_f.push([1,"a2:T9fa,"])</script><script>self.__next_f.push([1,"The University of South Carolina is a globally recognized, high-impact research university committed to a superior student experience and dedicated to innovation in learning, research, and community engagement. UofSC offers 350+ degree programs and is one of only 40 public universities to receive Carnegie Classifications for both top-tier research activity and community engagement.\n-In an undergraduate computer science degree, students learn programming, software engineering, computer organization, databases, computer networks, artificial intelligence, and mathematics. The program includes lectures, lab work, and projects.\n-Programming languages: Students learn to write code in various programming languages, such as Java, Python, and C++, and to apply programming constructs such as loops, functions, and conditionals to solve problems.\n-Data structures and algorithms: Students learn how to represent and manipulate data efficiently using structures such as arrays, lists, stacks, queues, and trees, and how to design and analyze algorithms for searching, sorting, and graph traversal.\n-Software engineering principles: Students learn how to apply principles of software engineering, such as design patterns, testing, and version control, to build maintainable and scalable software systems.\n-Computer organization and architecture: Students learn how computers work at a low level, including topics such as digital logic, instruction set architecture, and memory hierarchy.\n-Operating systems: Students learn about the design and implementation of operating systems, including topics such as process management, memory management, and file systems.\n-Databases: Students learn how to design and use databases to store, manipulate, and retrieve data using query languages such as SQL and to work with different database management systems such as MySQL and Oracle.\n-Computer networks: Students learn how to design and manage computer networks, including topics such as TCP/IP, routing, and network security.\n-Artificial intelligence: Students learn about the principles and applications of artificial intelligence, including machine learning, natural language processing, and computer vision.\n-Mathematics: Students learn mathematical concepts and methods that are foundational to computer science, such as discrete mathematics, calculus, and linear algebra.\n-Web development: Students learn how to design and develop web applications using languages and tools such as HTML, CSS, JavaScript, and web frameworks such as Django and Ruby on Rails."])</script><script>self.__next_f.push([1,"a3:Tb2b,"])</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-In an undergraduate computer science degree, students learn programming, software engineering, computer organization, databases, computer networks, artificial intelligence, and mathematics. The program includes lectures, lab work, and projects.\n-Programming languages: Students learn to write code in various programming languages, such as Java, Python, and C++, and to apply programming constructs such as loops, functions, and conditionals to solve problems.\n-Data structures and algorithms: Students learn how to represent and manipulate data efficiently using structures such as arrays, lists, stacks, queues, and trees, and how to design and analyze algorithms for searching, sorting, and graph traversal.\n-Software engineering principles: Students learn how to apply principles of software engineering, such as design patterns, testing, and version control, to build maintainable and scalable software systems.\n-Computer organization and architecture: Students learn how computers work at a low level, including topics such as digital logic, instruction set architecture, and memory hierarchy.\n-Operating systems: Students learn about the design and implementation of operating systems, including topics such as process management, memory management, and file systems.\n-Databases: Students learn how to design and use databases to store, manipulate, and retrieve data using query languages such as SQL and to work with different database management systems such as MySQL and Oracle.\n-Computer networks: Students learn how to design and manage computer networks, including topics such as TCP/IP, routing, and network security.\n-Artificial intelligence: Students learn about the principles and applications of artificial intelligence, including machine learning, natural language processing, and computer vision.\n-Mathematics: Students learn mathematical concepts and methods that are foundational to computer science, such as discrete mathematics, calculus, and linear algebra.\n-Web development: Students learn how to design and develop web applications using languages and tools such as HTML, CSS, JavaScript, and web frameworks such as Django and Ruby on Rails."])</script><script>self.__next_f.push([1,"a4:T99c,"])</script><script>self.__next_f.push([1,"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-In an undergraduate computer science degree, students learn programming, software engineering, computer organization, databases, computer networks, artificial intelligence, and mathematics. The program includes lectures, lab work, and projects.\n-Programming languages: Students learn to write code in various programming languages, such as Java, Python, and C++, and to apply programming constructs such as loops, functions, and conditionals to solve problems.\n-Data structures and algorithms: Students learn how to represent and manipulate data efficiently using structures such as arrays, lists, stacks, queues, and trees, and how to design and analyze algorithms for searching, sorting, and graph traversal.\n-Software engineering principles: Students learn how to apply principles of software engineering, such as design patterns, testing, and version control, to build maintainable and scalable software systems.\n-Computer organization and architecture: Students learn how computers work at a low level, including topics such as digital logic, instruction set architecture, and memory hierarchy.\n-Operating systems: Students learn about the design and implementation of operating systems, including topics such as process management, memory management, and file systems.\n-Databases: Students learn how to design and use databases to store, manipulate, and retrieve data using query languages such as SQL and to work with different database management systems such as MySQL and Oracle.\n-Computer networks: Students learn how to design and manage computer networks, including topics such as TCP/IP, routing, and network security.\n-Artificial intelligence: Students learn about the principles and applications of artificial intelligence, including machine learning, natural language processing, and computer vision.\n-Mathematics: Students learn mathematical concepts and methods that are foundational to computer science, such as discrete mathematics, calculus, and linear algebra.\n-Web development: Students learn how to design and develop web applications using languages and tools such as HTML, CSS, JavaScript, and web frameworks such as Django and Ruby on Rails."])</script><script>self.__next_f.push([1,"a5:T9d9,"])</script><script>self.__next_f.push([1,"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-In an undergraduate computer science degree, students learn programming, software engineering, computer organization, databases, computer networks, artificial intelligence, and mathematics. The program includes lectures, lab work, and projects.\n-Programming languages: Students learn to write code in various programming languages, such as Java, Python, and C++, and to apply programming constructs such as loops, functions, and conditionals to solve problems.\n-Data structures and algorithms: Students learn how to represent and manipulate data efficiently using structures such as arrays, lists, stacks, queues, and trees, and how to design and analyze algorithms for searching, sorting, and graph traversal.\n-Software engineering principles: Students learn how to apply principles of software engineering, such as design patterns, testing, and version control, to build maintainable and scalable software systems.\n-Computer organization and architecture: Students learn how computers work at a low level, including topics such as digital logic, instruction set architecture, and memory hierarchy.\n-Operating systems: Students learn about the design and implementation of operating systems, including topics such as process management, memory management, and file systems.\n-Databases: Students learn how to design and use databases to store, manipulate, and retrieve data using query languages such as SQL and to work with different database management systems such as MySQL and Oracle.\n-Computer networks: Students learn how to design and manage computer networks, including topics such as TCP/IP, routing, and network security.\n-Artificial intelligence: Students learn about the principles and applications of artificial intelligence, including machine learning, natural language processing, and computer vision.\n-Mathematics: Students learn mathematical concepts and methods that are foundational to computer science, such as discrete mathematics, calculus, and linear algebra.\n-Web development: Students learn how to design and develop web applications using languages and tools such as HTML, CSS, JavaScript, and web frameworks such as Django and Ruby on Rails."])</script><script>self.__next_f.push([1,"a6:Ta37,"])</script><script>self.__next_f.push([1,"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-In an undergraduate computer science degree, students learn programming, software engineering, computer organization, databases, computer networks, artificial intelligence, and mathematics. The program includes lectures, lab work, and projects.\n-Programming languages: Students learn to write code in various programming languages, such as Java, Python, and C++, and to apply programming constructs such as loops, functions, and conditionals to solve problems.\n-Data structures and algorithms: Students learn how to represent and manipulate data efficiently using structures such as arrays, lists, stacks, queues, and trees, and how to design and analyze algorithms for searching, sorting, and graph traversal.\n-Software engineering principles: Students learn how to apply principles of software engineering, such as design patterns, testing, and version control, to build maintainable and scalable software systems.\n-Computer organization and architecture: Students learn how computers work at a low level, including topics such as digital logic, instruction set architecture, and memory hierarchy.\n-Operating systems: Students learn about the design and implementation of operating systems, including topics such as process management, memory management, and file systems.\n-Databases: Students learn how to design and use databases to store, manipulate, and retrieve data using query languages such as SQL and to work with different database management systems such as MySQL and Oracle.\n-Computer networks: Students learn how to design and manage computer networks, including topics such as TCP/IP, routing, and network security.\n-Artificial intelligence: Students learn about the principles and applications of artificial intelligence, including machine learning, natural language processing, and computer vision.\n-Mathematics: Students learn mathematical concepts and methods that are foundational to computer science, such as discrete mathematics, calculus, and linear algebra.\n-Web development: Students learn how to design and develop web applications using languages and tools such as HTML, CSS, JavaScript, and web frameworks such as Django and Ruby on Rails."])</script><script>self.__next_f.push([1,"a7:Ta33,"])</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-In an undergraduate computer science degree, students learn programming, software engineering, computer organization, databases, computer networks, artificial intelligence, and mathematics. The program includes lectures, lab work, and projects.\n-Programming languages: Students learn to write code in various programming languages, such as Java, Python, and C++, and to apply programming constructs such as loops, functions, and conditionals to solve problems.\n-Data structures and algorithms: Students learn how to represent and manipulate data efficiently using structures such as arrays, lists, stacks, queues, and trees, and how to design and analyze algorithms for searching, sorting, and graph traversal.\n-Software engineering principles: Students learn how to apply principles of software engineering, such as design patterns, testing, and version control, to build maintainable and scalable software systems.\n-Computer organization and architecture: Students learn how computers work at a low level, including topics such as digital logic, instruction set architecture, and memory hierarchy.\n-Operating systems: Students learn about the design and implementation of operating systems, including topics such as process management, memory management, and file systems.\n-Databases: Students learn how to design and use databases to store, manipulate, and retrieve data using query languages such as SQL and to work with different database management systems such as MySQL and Oracle.\n-Computer networks: Students learn how to design and manage computer networks, including topics such as TCP/IP, routing, and network security.\n-Artificial intelligence: Students learn about the principles and applications of artificial intelligence, including machine learning, natural language processing, and computer vision.\n-Mathematics: Students learn mathematical concepts and methods that are foundational to computer science, such as discrete mathematics, calculus, and linear algebra.\n-Web development: Students learn how to design and develop web applications using languages and tools such as HTML, CSS, JavaScript, and web frameworks such as Django and Ruby on Rails."])</script><script>self.__next_f.push([1,"a8:T945,"])</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 computer science degree, students learn programming, software engineering, computer organization, databases, computer networks, artificial intelligence, and mathematics. The program includes lectures, lab work, and projects.\n-Programming languages: Students learn to write code in various programming languages, such as Java, Python, and C++, and to apply programming constructs such as loops, functions, and conditionals to solve problems.\n-Data structures and algorithms: Students learn how to represent and manipulate data efficiently using structures such as arrays, lists, stacks, queues, and trees, and how to design and analyze algorithms for searching, sorting, and graph traversal.\n-Software engineering principles: Students learn how to apply principles of software engineering, such as design patterns, testing, and version control, to build maintainable and scalable software systems.\n-Computer organization and architecture: Students learn how computers work at a low level, including topics such as digital logic, instruction set architecture, and memory hierarchy.\n-Operating systems: Students learn about the design and implementation of operating systems, including topics such as process management, memory management, and file systems.\n-Databases: Students learn how to design and use databases to store, manipulate, and retrieve data using query languages such as SQL and to work with different database management systems such as MySQL and Oracle.\n-Computer networks: Students learn how to design and manage computer networks, including topics such as TCP/IP, routing, and network security.\n-Artificial intelligence: Students learn about the principles and applications of artificial intelligence, including machine learning, natural language processing, and computer vision.\n-Mathematics: Students learn mathematical concepts and methods that are foundational to computer science, such as discrete mathematics, calculus, and linear algebra.\n-Web development: Students learn how to design and develop web applications using languages and tools such as HTML, CSS, JavaScript, and web frameworks such as Django and Ruby on Rails."])</script><script>self.__next_f.push([1,"a9:T9d2,"])</script><script>self.__next_f.push([1,"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-In an undergraduate computer science degree, students learn programming, software engineering, computer organization, databases, computer networks, artificial intelligence, and mathematics. The program includes lectures, lab work, and projects.\n-Programming languages: Students learn to write code in various programming languages, such as Java, Python, and C++, and to apply programming constructs such as loops, functions, and conditionals to solve problems.\n-Data structures and algorithms: Students learn how to represent and manipulate data efficiently using structures such as arrays, lists, stacks, queues, and trees, and how to design and analyze algorithms for searching, sorting, and graph traversal.\n-Software engineering principles: Students learn how to apply principles of software engineering, such as design patterns, testing, and version control, to build maintainable and scalable software systems.\n-Computer organization and architecture: Students learn how computers work at a low level, including topics such as digital logic, instruction set architecture, and memory hierarchy.\n-Operating systems: Students learn about the design and implementation of operating systems, including topics such as process management, memory management, and file systems.\n-Databases: Students learn how to design and use databases to store, manipulate, and retrieve data using query languages such as SQL and to work with different database management systems such as MySQL and Oracle.\n-Computer networks: Students learn how to design and manage computer networks, including topics such as TCP/IP, routing, and network security.\n-Artificial intelligence: Students learn about the principles and applications of artificial intelligence, including machine learning, natural language processing, and computer vision.\n-Mathematics: Students learn mathematical concepts and methods that are foundational to computer science, such as discrete mathematics, calculus, and linear algebra.\n-Web development: Students learn how to design and develop web applications using languages and tools such as HTML, CSS, JavaScript, and web frameworks such as Django and Ruby on Rails."])</script><script>self.__next_f.push([1,"aa:Ta13,"])</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 computer science degree, students learn programming, software engineering, computer organization, databases, computer networks, artificial intelligence, and mathematics. The program includes lectures, lab work, and projects.\n-Programming languages: Students learn to write code in various programming languages, such as Java, Python, and C++, and to apply programming constructs such as loops, functions, and conditionals to solve problems.\n-Data structures and algorithms: Students learn how to represent and manipulate data efficiently using structures such as arrays, lists, stacks, queues, and trees, and how to design and analyze algorithms for searching, sorting, and graph traversal.\n-Software engineering principles: Students learn how to apply principles of software engineering, such as design patterns, testing, and version control, to build maintainable and scalable software systems.\n-Computer organization and architecture: Students learn how computers work at a low level, including topics such as digital logic, instruction set architecture, and memory hierarchy.\n-Operating systems: Students learn about the design and implementation of operating systems, including topics such as process management, memory management, and file systems.\n-Databases: Students learn how to design and use databases to store, manipulate, and retrieve data using query languages such as SQL and to work with different database management systems such as MySQL and Oracle.\n-Computer networks: Students learn how to design and manage computer networks, including topics such as TCP/IP, routing, and network security.\n-Artificial intelligence: Students learn about the principles and applications of artificial intelligence, including machine learning, natural language processing, and computer vision.\n-Mathematics: Students learn mathematical concepts and methods that are foundational to computer science, such as discrete mathematics, calculus, and linear algebra.\n-Web development: Students learn how to design and develop web applications using languages and tools such as HTML, CSS, JavaScript, and web frameworks such as Django and Ruby on Rails."])</script><script>self.__next_f.push([1,"ab:T9ea,"])</script><script>self.__next_f.push([1,"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-In an undergraduate computer science degree, students learn programming, software engineering, computer organization, databases, computer networks, artificial intelligence, and mathematics. The program includes lectures, lab work, and projects.\n-Programming languages: Students learn to write code in various programming languages, such as Java, Python, and C++, and to apply programming constructs such as loops, functions, and conditionals to solve problems.\n-Data structures and algorithms: Students learn how to represent and manipulate data efficiently using structures such as arrays, lists, stacks, queues, and trees, and how to design and analyze algorithms for searching, sorting, and graph traversal.\n-Software engineering principles: Students learn how to apply principles of software engineering, such as design patterns, testing, and version control, to build maintainable and scalable software systems.\n-Computer organization and architecture: Students learn how computers work at a low level, including topics such as digital logic, instruction set architecture, and memory hierarchy.\n-Operating systems: Students learn about the design and implementation of operating systems, including topics such as process management, memory management, and file systems.\n-Databases: Students learn how to design and use databases to store, manipulate, and retrieve data using query languages such as SQL and to work with different database management systems such as MySQL and Oracle.\n-Computer networks: Students learn how to design and manage computer networks, including topics such as TCP/IP, routing, and network security.\n-Artificial intelligence: Students learn about the principles and applications of artificial intelligence, including machine learning, natural language processing, and computer vision.\n-Mathematics: Students learn mathematical concepts and methods that are foundational to computer science, such as discrete mathematics, calculus, and linear algebra.\n-Web development: Students learn how to design and develop web applications using languages and tools such as HTML, CSS, JavaScript, and web frameworks such as Django and Ruby on Rails."])</script><script>self.__next_f.push([1,"ac:T9b2,"])</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 computer science degree, students learn programming, software engineering, computer organization, databases, computer networks, artificial intelligence, and mathematics. 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