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Earning verified certificates in statistics and other mathematics disciplines may also help you stand out from other applicants when applying for roles in which you’ll need to perform statistical analysis.</p><p>Individuals who perform statistical analysis as a core function of their jobs need to be skilled in problem-solving, critical thinking, analytics programming, and research. Regardless of industry, these skills can make you an attractive candidate for roles in this field.</p></div></div><a class="subnav-item -mt-1" name="Careers in statistics" id="careers-in-statistics"></a><div class="Default_content__HO8we"><div id=""><h2>Careers in statistics</h2><p>Governments, hospitals, banks, educational institutions, and countless other types of organizations rely on statistics for their general operations. For example, the U.S. Census Bureau specializes in the collection and analysis of data from U.S. households that helps to determine everything from the resources needed for underserved communities to the number of representatives each state has in Congress. Statistics have power; learning how to apply statistical analysis to data can be one way to harness that power to benefit any professional setting where you choose to work. </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></div></article></main></div><div class="bg-primary text-primary-foreground pt-16"><footer class="flex justify-between max-w-screen-xl mx-auto px-4 pb-4 "><div class="flex flex-col w-full"><div class="flex flex-wrap justify-between mb-6 w-full"><a href="/" class="mb-6"><img alt="edX 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During this course, you will focus on the interpretation of tables and results, and how to approach statistical problems effectively.\u003c/span\u003e\u003c/p\u003e337:Tb4d,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis course teaches the R programming language in the context of statistical data and statistical analysis in the life sciences.\u003c/p\u003e\n\u003cp\u003eWe will learn the basics of statistical inference in order to understand and compute p-values and confidence intervals, all while analyzing data with R code. We provide R programming examples in a way that will help make the connection between concepts and implementation. Problem sets requiring R programming will be used to test understanding and ability to implement basic data analyses. We will use visualization techniques to explore new data sets and determine the most appropriate approach. We will describe robust statistical techniques as alternatives when data do not fit assumptions required by the standard approaches. By using R scripts to analyze data, you will learn the basics of conducting reproducible research.\u003c/p\u003e\n\u003cp\u003eGiven the diversity in educational background of our students we have divided the course materials into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. We start with simple calculations and descriptive statistics. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.\u003c/p\u003e\n\u003cp\u003eThese courses make up two Professional Certificates and are self-paced:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis for Life Sciences:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistics-and-r\"\u003ePH525.1x: Statistics and R for the Life Sciences\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-linear-models-and-matrix-algebra\"\u003ePH525.2x: Introduction to Linear Models and Matrix Algebra\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistical-inference-and-modeling-for-high-throug\"\u003ePH525.3x: Statistical Inference and Modeling for High-throughput Experiments\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/high-dimensional-data-analysis\"\u003ePH525.4x: High-Dimensional Data Analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eGenomics Data Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-bioconductor-annotation-and-analys\"\u003ePH525.5x: Introduction to Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/case-studies-in-functional-genomics\"\u003ePH525.6x: Case Studies in Functional Genomics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/advanced-bioconductor\"\u003ePH525.7x: Advanced Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis class was supported in part by NIH grant R25GM114818.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"338:T10c8,"])</script><script>self.__next_f.push([1,"\u003cp\u003eData scientists bring value to organizations across industries because they are able to solve complex challenges with data and drive important decision-making processes. Not only is there a huge demand, but there is a significant shortage of qualified data scientists with 54% of the most rigorous data science positions requiring a degree higher than a bachelor’s.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis MicroMasters® program in Statistics and Data Science (SDS) was developed by MITx and the \u003ca href=\"https://idss.mit.edu/academics/micromasters-program-in-statistics-and-data-science-sds/\"\u003eMIT Institute for Data, Systems, and Society (IDSS)\u003c/a\u003e. It is a multidisciplinary approach comprised of four separate tracks with four online courses each and a virtually proctored exam. Each track focuses on a combination of methods-centered courses and domain analysis courses to provide you with foundational knowledge and hands-on training. All learners complete the Probability and Machine Learning courses, two other courses determined by the chosen track, and the Capstone Exam.\r\n\r\n\u003cp\u003eData scientists bring value to organizations across industries because they are able to solve complex challenges with data and drive important decision-making processes. Not only is there a huge demand, but there is a significant shortage of qualified data scientists with 54% of the most rigorous data science positions requiring a degree higher than a bachelor’s.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis MicroMasters® program in Statistics and Data Science (SDS) was developed by MITx and the MIT Institute for Data, Systems, and Society (IDSS). It is a multidisciplinary approach comprised of four separate tracks with four online courses each and a virtually proctored exam. Each track focuses on three methods-centered courses and a domain analysis course to provide you with foundational knowledge and hands-on training in a discipline of your choice. All learners complete the Probability and Machine Learning courses. The track you choose determines your third methods course and a final domain analysis course.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eGeneral Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you to become an informed and effective practitioner of data science who adds value to your organization across industries.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-general-track\"\u003eYou are currently exploring the General track\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eMethods Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you with in-depth knowledge of data science and time series analysis and will enable you to conduct rigorous analysis, inform decision-making processes, and contribute to evidence-based practices across industries.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-methods-track\"\u003eExplore the Methods track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eSocial Sciences Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you to extract meaningful insights from social, cultural, economic, and policy-related data and equip you to tackle complex real-world problems and contribute to cutting-edge advancements in AI and data-driven solutions within all social sciences.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-social-sciences-track\"\u003eExplore the Social Sciences track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eTime Series and Social Sciences Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will equip you to analyze the impact of interventions on time series data, preparing you for roles in economics, public policy, and social sciences where understanding temporal dynamics is crucial for informed decision-making and policy formulation.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-time-series-and-social-sciences-track\"\u003eExplore the Time Series and Social Sciences track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eAll tracks are taught by MIT faculty and administered by IDSS at a similar pace and level of rigor as an on-campus course at MIT. The program is designed for learners who want to acquire sophisticated and rigorous training in data science without leaving their day job but without compromising quality. There is no application process, but college-level calculus and comfort with mathematical reasoning and Python programming are highly recommended if you want to excel.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"339:T6e3,\u003cp\u003eThis Statistics for Data Science course is designed to introduce you to the basic principles of statistical methods and procedures used for data analysis. After completing this course you will have practical knowledge of crucial topics in statistics including - data gathering, summarizing data using descriptive statistics, displaying and visualizing data, examining relationships between variables, probability distributions, expected values, hypothesis testing, introduction to ANOVA (analysis of variance), regression and correlation analysis. You will take a hands-on approach to statistical analysis using Python and Jupyter Notebooks – the tools of choice for Data Scientists and Data Analysts.\u003c/p\u003e\n\u003cp\u003eAt the end of the course, you will complete a project to apply various concepts in the course to a Data Science problem involving a real-life inspired scenario and demonstrate an understanding of the foundational statistical thinking and reasoning. The focus is on developing a clear understanding of the different approaches for different data types, developing an intuitive understanding, making appropriate assessments of the proposed methods, using Python to analyze our data, and interpreting the output accurately. This course is suitable for a variety of professionals and students intending to start their journey in data and statistics-driven roles such as Data Scientists, Data Analysts, Business Analysts, Statisticians, and Researchers. It does not require any computer science or statistics background. We strongly recommend taking the Python for Data Science course before starting this course to get familiar with the Python programming language, Jupyter notebooks, and libraries. An optional refresher on Python is also provided.\u003c/p\u003e33a:T6d8,\u003cp\u003eA strong foundation in mathematics is critical for success in all science and engineering disciplines. Whether you want to make a strong start to a master’s degree, prepare for more advanced courses, solidify your knowledge in a professional context or simply br"])</script><script>self.__next_f.push([1,"ush up on fundamentals, this course will get you up to speed.\u003c/p\u003e\n\u003cp\u003eIn many engineering master’s programs, statistics is used quite intensively. As soon as you are dealing with real-life data, you will need to get an idea of what these data tell you and how you can visualize this (descriptive statistics). But you will also want to perform some analysis (inferential statistics): you may want to build a model that mimics reality, estimate some quantities, or test some hypotheses.\u003c/p\u003e\n\u003cp\u003eThe statistics course in this series will help you refresh your knowledge on these topics. Along the way you will learn how to apply these concepts to datasets, using the statistical software R.\u003c/p\u003e\n\u003cp\u003eThis course offers enough depth to cover the statistics you need to succeed in your engineering master’s or profession in areas such as machine learning, data science and more.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis is a review course\u003c/strong\u003e\u003cbr /\u003e\nThis self-contained course is modular, so you do not need to follow the entire course if you wish to focus on a particular aspect. As a review course you are expected to have previously studied or be familiar with most of the material. Hence the pace will be higher than in an introductory course.\u003c/p\u003e\n\u003cp\u003eThis format is ideal for refreshing your bachelor level mathematics and letting you practice as much as you want. You will get many exercises, to be solved using Grasple or R, for which you will receive intelligent, personal and immediate feedback.\u003c/p\u003e33b:T49b,\u003cp\u003eThis course provides an introduction to basic probability concepts. Our emphasis is on applications in science and engineering, with the goal of enhancing modeling and analysis skills for a variety of real-world problems. \u003c/p\u003e\n\u003cp\u003eIn order to make the course completely self-contained (and to bring back long-lost memories), we’ll start off with Bootcamp lessons to review concepts from set theory and calculus. We’ll then discuss the probability axioms that serve as the basis for all of our subsequent work – what makes probability tick? That d"])</script><script>self.__next_f.push([1,"iscussion will give us the tools to study elementary probability counting rules, including permutations and combinations. We’ll use these rules to work on various cool applications, including poker probability calculations and baseball line-ups!\u003c/p\u003e\n\u003cp\u003eThe next venues on our tour are the concepts of independence and conditional probability, which allow us to see how the probabilities of different events are related to each other, and how new information can be used to update probabilities. The course culminates in a discussion of Bayes Rule and its various interesting consequences related to probability updates.\u003c/p\u003e33c:T4ea,\u003cp\u003eThis course discusses properties and applications of random variables. When you’re done, you’ll have enough firepower to undertake a wide variety of modeling and analysis problems; and you’ll be well-prepared for the upcoming Statistics courses. \u003c/p\u003e\n\u003cp\u003eWe’ll begin by introducing the concepts of discrete and continuous random variables. For instance, how many customers are likely to arrive in the next hour (discrete)? What’s the probability that a lightbulb will last more than a year (continuous)? \u003c/p\u003e\n\u003cp\u003eWe’ll learn about various properties of random variables such as the expected value, variance, and moment generating function. This will lead us to a discussion of functions of random variables. Such functions have many uses, including some wonderful applications in computer simulations. \u003c/p\u003e\n\u003cp\u003eIf you enjoy random variables, then you’ll really love joint (two-dimensional) random variables. We’ll provide methodology to extract marginal (one-dimensional) and conditional information from these big boys. This work will enable us to \u003cbr /\u003e\nstudy the important concepts of independence and correlation. \u003c/p\u003e\n\u003cp\u003eAlong the way, we’ll start working with the R statistical package to do some of our calculations and analysis.\u003c/p\u003e33d:T43b,\u003cp\u003eUpon completion of the course, participants should be able to:\u003c/p\u003e\n\u003cp\u003e• Explain the basic concepts, definitions, and accounting principles in "])</script><script>self.__next_f.push([1,"the integrated GFS framework.\u003c/p\u003e\n\u003cp\u003e• Classify basic government flows and stock positions according to GFSM 2014.\u003c/p\u003e\n\u003cp\u003e• Apply the general principles to classify an entity in the public sector and in relevant subsectors, such as the general government and public corporations.\u003c/p\u003e\n\u003cp\u003e• Record the fiscal flows and stocks associated with the activities of public sector entities, following the GFSM 2014 guidelines and classifications.\u003c/p\u003e\n\u003cp\u003e• Explain how the main GFS aggregates and analytical balances are calculated, and what they show about the government’s impact on the economy.\u003c/p\u003e\n\u003cp\u003e• Develop a migration plan to adopt the GFSM 2014 methodology, and compile and disseminate GFS following international guidelines.\u003c/p\u003e\n\u003cp\u003e• Recognize the value of comprehensive, consistent, and internationally comparable GFS, and the use of the key GFS indicators in the design, monitoring, and evaluation of fiscal policy.\u003c/p\u003e33e:T551,\u003cp\u003e\u003cspan lang=\"EN\"\u003eBasics of Bayesian Data Analysis Using R is part one of the Bayesian Data Analysis in R professional certificate. \u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eBayesian approach is becoming increasingly popular in all fields of data analysis, including but not limited to epidemiology, ecology, economics, and political sciences. It also plays an increasingly important role in data mining and deep learning. Let this course be your first step into Bayesian statistics.\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eHere, you will find a practical introduction to applied Bayesian data analysis with the emphasis on formulating and answering real life questions. You will learn how to combine the data generating mechanism, likelihood, with prior distribution using Bayes’ Theorem to produce the posterior distribution. You will investigate the underlying theory and fundamental concepts by way of simple and clear practical examples, including a case of linear regression.\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eYou will be introduced to the Gibbs sampler – the simplest version of the powerful Markov Chain Mont"])</script><script>self.__next_f.push([1,"e Carlo (MCMC) algorithm. And you will see how the popular R-software can be used in this context, and encounter some Bayesian R packages .\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eA facility in basic algebra and calculus as well as programming in R is recommended.\u003c/p\u003e33f:T527,\u003cp\u003e\u003cspan lang=\"EN\"\u003e•\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003cspan lang=\"EN\"\u003e Bayes’ Theorem. Differences between classical (frequentist) and Bayesian inference.\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e•\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003cspan lang=\"EN\"\u003e Posterior inference: summarizing posterior distributions, credible intervals, posterior probabilities, posterior predictive distributions and data visualisation.\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e•\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003cspan lang=\"EN\"\u003e Gamma-poisson, beta-binomial and normal conjugate models for data analysis.\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e•\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003cspan lang=\"EN\"\u003e Bayesian regression analysis and analysis of variance (ANOVA).\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e•\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003cspan lang=\"EN\"\u003e Use of simulations for posterior inference. Simple applications of Markov chain-Monte Carlo (MCMC)\u003c/span\u003e methods and their implementation in R.\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e•\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003cspan lang=\"EN\"\u003e Bayesian cluster analysis. \u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e•\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003cspan lang=\"EN\"\u003e Model diagnostics and comparison.\u003c/span\u003e\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e•\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003cspan lang=\"EN\"\u003e Ensuring you answer the actual research question rather than “apply methods to the data”\u003c/span\u003e\u003c/p\u003e340:T4e3,\u003cp\u003eThis course covers two important methodologies in statistics – \u003cem\u003econfidence intervals\u003c/em\u003e and \u003cem\u003ehypothesis testing\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eConfidence intervals are encountered in everyday life, and allow us to make probabilistic statements such as: “Based on the sample of observations we conducted, we are 95% sure that the unknown mean lies between A and B,” and “We are 95% sure that Candidate Smith’s popularity is 52% +/- 3%.” We begin the"])</script><script>self.__next_f.push([1," course by discussing what a confidence interval is and how it is used. We then formulate and interpret confidence intervals for a variety of probability distributions and their parameters.\u003c/p\u003e\n\u003cp\u003eHypothesis testing allows us to pose hypotheses and test their validity in a statistically rigorous way. For instance, “Does a new drug result in a higher cure rate than the old drug?” or “Is the mean tensile strength of item A greater than that of item B?” The second half the course begins by motivating hypothesis tests and how they are used. We then discuss the types of errors that can occur with hypothesis testing, and how to design tests to mitigate those errors. Finally, we formulate and interpret hypothesis tests for a variety of probability distributions and their parameters.\u003c/p\u003e341:T403,\u003cp\u003eUpon completion of the course, you should be able to:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDescribe the system of national accounts framework, understand the sequence of accounts and their interrelationships, and identify the key macroeconomic indicators\u003c/li\u003e\n\u003cli\u003eExplain key national accounts concepts, accounting rules, and methods.\u003c/li\u003e\n\u003cli\u003eDefine the components of gross domestic product (GDP) from the production approach. Apply the concepts, accounting rules, methods, and data sources necessary to estimate GDP from the production approach.\u003c/li\u003e\n\u003cli\u003eDefine the components of GDP from the expenditure approach. Apply the concepts, accounting rules, methods, and data sources necessary to estimate GDP from the expenditure approach.\u003c/li\u003e\n\u003cli\u003eDefine the components of GDP from the income approach. Apply the concepts, accounting rules, methods and data sources necessary to estimate GDP from the income approach.\u003c/li\u003e\n\u003cli\u003eDefine and explain how to compile volume estimates of GDP from both the production and expenditure perspective.\u003c/li\u003e\n\u003c/ul\u003e342:T4a4,\u003cp\u003eThis course, presented by the Statistics Department, provides participants with an introduction to the compilation of monetary statistics covering the central bank (CB), other depository corporations (O"])</script><script>self.__next_f.push([1,"DCs) and other financial corporations (OFCs) in accordance with international statistical standards. Course materials are based on the 2016 Monetary and Financial Statistics Manual and Compilation Guide (MFSMCG). The course discusses the principles of residency and sectoring of institutional units, the characteristics and types of financial instruments, valuation principles, and other accounting issues that are relevant to the compilation of monetary statistics. Participants will also become familiar with the defining characteristics of depository corporations (DCs), notably their role as money issuers, and with the main principles on which analysis of monetary and credit aggregates is based. They will also gain a deeper understanding of the OFCs sector and their relevance for compiling a broader and often more reliable measure of the liquidity available in an economy and financing extended to the nonfinancial sectors and nonresidents by the financial corporations.\u003c/p\u003e343:T50c,\u003cp\u003e\u003cspan lang=\"EN\"\u003eAdvanced\u003c/span\u003e Bayesian Data Analysis Using R is part two of the Bayesian Data Analysis in R professional certificate.\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis course is directed at people who are already familiar with the fundamentals of Bayesian inference. It explores further the concepts, methods, and algorithms introduced in the part one (Introductory Bayesian Data Analysis Using R).\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe course places mixed effects regression models useful for experiments with repeated measures or additional hierarchy often encountered in biostatistics, ecology and health sciences among others within the Bayesian context. It takes a closer look at the Markov Chain Monte Carlo (MCMC) algorithms, why they work and how to implement them in the R programming language. Convergence assessment and visualisation of the results are discussed in some detail. The course also explores Bayesian model averaging, often used in machine learning, all within the context of practical examples. \u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eFina"])</script><script>self.__next_f.push([1,"lly, we discuss different kinds of missing data, and the Bayesian methods of dealing with such situations.\u003cspan lang=\"EN\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003ePrior facility in basic algebra and calculus as well as programming in R is highly recommended.\u003c/p\u003e344:T594,\u003cp\u003eThis course, presented by IMF Statistics Department, covers the fundamentals needed to compile and disseminate comprehensive public sector debt statistics (PSDS) that are useful for policy- and decision-makers, as well as other users. \u003c/p\u003e\n\u003cp\u003eThe course introduces the conceptual statistical framework for PSDS—as presented in the Public Sector Debt Statistics: Guide for Compilers and Users—in the context of the government finance statistics (GFS) framework, which is harmonized with other macroeconomic statistical frameworks. Basic concepts, definitions, and classifications are covered, along with the principal accounting rules (including valuation and consolidation) that are relevant for PSDS compilation. \u003c/p\u003e\n\u003cp\u003eThe course discusses the recommended the instrument and institutional coverage for compiling comprehensive, internationally comparable PSDS, and how to record contingent liabilities such as government guarantees. It also deals with the impact on PSDS of some debt-related issues such as debt assumption, debt forgiveness, on-lending, financial leases, and financial bailouts. \u003c/p\u003e\n\u003cp\u003eImportant PSDS compilation considerations—including what PSDS to compile and disseminate—and the IMF’s guidelines and standards on disseminating PSDS are also covered. The course also presents possible uses of PSDS, including debt sustainability analyses (DSA), and fiscal risk and vulnerability analyses.\u003c/p\u003e345:T62d,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eTraining in hypothesis testing offers several benefits that can enhance your analytical and decision-making skills, whether you work in manufacturing, business, healthcare, research, or any field that involves data analysis. Here are some of the key benefits:\u003c/span\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan lang=\"EN-US\"\u003eContinuous Improvement: By testing and evalua"])</script><script>self.__next_f.push([1,"ting changes systematically, you \u003c/span\u003eoptimize processes over time.\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"EN-US\"\u003eCritical Thinking: By learning to design experiments, select \u003c/span\u003eappropriate statistical tests, and interpret results, critical thinking skills are enhanced. \u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"EN-US\"\u003eProblem Framing: Hypothesis testing \u003c/span\u003erequires the ability to frame problems accurately, a crucial aspect of finding effective solutions.\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"EN-US\"\u003eData Interpretation: Understand the significance of patterns, relationships, and variations in the data, which is crucial for drawing meaningful insights.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003eStatistical Literacy: Hypothesis testing training introduces you to statistical concepts and techniques, enhancing your ability to read and understand research papers, reports, and studies that involve statistical analyses.\u003c/li\u003e\n\u003cli\u003eEvidence-Based Decision Making: Hypothesis testing provides a structured approach to making decisions based on evidence rather than intuition or assumptions. \u003c/li\u003e\n\u003cli\u003eEffective Communication: Learning how to present and explain hypothesis testing results to both technical and non-technical audiences will improve your communication skills.\u003c/li\u003e\n\u003c/ul\u003e346:T42b,\u003cp\u003eStatistics is a versatile discipline that has revolutionized the fields of business, engineering, medicine and pure sciences. This course is Part 2 of a 4-part series on Business Statistics, and is ideal for learners who wish to enroll in business programs. The first two parts cover topics in Descriptive Statistics, whereas the next two focus on Inferential Statistics.\u003c/p\u003e\n\u003cp\u003eSpreadsheets containing real data from diverse areas such as economics, finance and HR drive much of our discussions. \u003c/p\u003e\n\u003cp\u003eIn Part 2, we use the language of probability to examine the underlying distributions of random variables. We model real-life phenomena using known variables such as Binomial, Poisson and Normal. We learn how to simulate data that are distributed according to these variables.\u003c/p\u003e\n\u003cp\u003eWe shall take up datasets that h"])</script><script>self.__next_f.push([1,"ave over a million rows, which makes it difficult to analyze using a spreadsheet. This is a natural setting for R, an advanced statistical programming platform. We incorporate helpful tutorials to get learners acquainted with the platform.\u003c/p\u003e347:T9ee,"])</script><script>self.__next_f.push([1,"\u003cp\u003eWhen you meet a new person, it is hard to know what to expect. You may not be able to read the person or understand what they mean. Even if you want to have a good relationship with them, this lack of understanding can make interactions tense, unpredictable and scary! The same is true for a lot of people as they encounter statistics and mathematical ways of working with data. Statistics can be confusing and opaque. Symbols, Greek letters, very large and very small numbers, and how to interpret all of this can leave to feeling cold and disengaged—even fearful and resentful.\u003c/p\u003e\n\u003cp\u003eBut in the modern information age, having a healthy relationship with statistics can make life a whole lot easier. We are constantly faced with an onslaught of data and claims about it—from news articles, to Facebook and blog posts, casual and professional conversations, reports at our workplace, advertising, and claims from politicians and public officials. How can we process that information, make sense of it, evaluate truth claims, and put ourselves in a position to act on the information? One of the most important ways is by befriending statistics and consistently using statistical ways of thinking. \u003c/p\u003e\n\u003cp\u003eThe purpose of this course, then is to help you develop a functional, satisfying, and useful life-long relationship with statistics. To achieve that goal, we will take a non-technical approach—you will learn how statistics work and why they are so helpful in evaluating the world of information that is around us. You will learn about the logic of statistical thinking and the concepts (rather than the mathematical details and probability theory) that guide statistical inferences and conclusions. \u003c/p\u003e\n\u003cp\u003eYou do not need to be a math whiz to take this course. If you can add, subtract, multiply, and divide (or just be able to use a calculator to do that!), you will be more than able to handle what will happen as this relationship develops. \u003c/p\u003e\n\u003cp\u003eBy the end of the course you will be able to:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eIdentify the most important features of a data set\u003c/li\u003e\n\u003cli\u003eSelect a statistical test based on the features of the data\u003c/li\u003e\n\u003cli\u003eThink like a statistical detective\u003c/li\u003e\n\u003cli\u003eUnderstand the relationship between two different characteristics or variables\u003c/li\u003e\n\u003cli\u003ePerform some simple statistical calculations and draw some conclusions from real data\u003c/li\u003e\n\u003cli\u003eHopefully, love stats! \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe’ll do all of this using entertaining examples related to real-life situations we all encounter in everyday life.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"348:T13b9,"])</script><script>self.__next_f.push([1,"\u003cp\u003eData scientists bring value to organizations across industries because they are able to solve complex challenges with data and drive important decision-making processes. Not only is there a huge demand, but there is a significant shortage of qualified data scientists with 54% of the most rigorous data science positions requiring a degree higher than a bachelor’s.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis MicroMasters® program in Statistics and Data Science (SDS) was developed by MITx and the \u003ca href=\"https://idss.mit.edu/academics/micromasters-program-in-statistics-and-data-science-sds/\"\u003eMIT Institute for Data, Systems, and Society (IDSS)\u003c/a\u003e. It is a multidisciplinary approach comprised of four separate tracks with four online courses each and a virtually proctored exam. Each track focuses on a combination of methods-centered courses and domain analysis courses to provide you with foundational knowledge and hands-on training. All learners complete the Probability and Machine Learning courses, two other courses determined by the chosen track, and the Capstone Exam.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eGeneral Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you to become an informed and effective practitioner of data science who adds value to your organization across industries.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-general-track\"\u003eExplore the General track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eMethods Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you with in-depth knowledge of data science and time series analysis and will enable you to conduct rigorous analysis, inform decision-making processes, and contribute to evidence-based practices across industries.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-methods-track\"\u003eYou are currently exploring the Methods track\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eSocial Sciences Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you to extract meaningful insights from social, cultural, economic, and policy-related data and equip you to tackle complex real-world problems and contribute to cutting-edge advancements in AI and data-driven solutions within all social sciences.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-social-sciences-track\"\u003eExplore the Social Sciences track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eTime Series and Social Sciences Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will equip you to analyze the impact of interventions on time series data, preparing you for roles in economics, public policy, and social sciences where understanding temporal dynamics is crucial for informed decision-making and policy formulation.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-time-series-and-social-sciences-track\"\u003eExplore the Time Series and Social Sciences track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eAll tracks are taught by MIT faculty and administered by IDSS at a similar pace and level of rigor as an on-campu\u003cp\u003eTo earn the MicroMasters program certificate in Statistics and Data Science, learners must complete and successfully earn a certificate in the four required courses and pass a virtually-proctored capstone exam.\u003c/p\u003e\r\n\r\n\u003cp\u003eMicroMasters programs are designed to offer learners a pathway to an advanced degree and can count as credit toward completing a Master’s degree program. Learners who successfully earn this MicroMasters program certificate may apply for admission to several Master’s programs, and if accepted, the MicroMasters program certificate will count towards the degree.\u003c/p\u003e\r\n\r\n\u003cp\u003eLearners who successfully complete this MicroMasters program certificate have the opportunity to apply to the MIT Doctoral Program in Social and Engineering Systems (SES) offered through the MIT Institute for Data, Systems, and Society (IDSS).\u003c/p\u003e \r\n\r\n\u003cp\u003eLearners can use their MicroMasters program certificate to demonstrate their preparation in Statistics and Data Science fundamentals to the SES Admissions Committee. Learners admitted to SES can expect that their MicroMasters coursework will be recognized with credit for corresponding SES core classes, and for satisfying the SES Information, Systems, and Decision Science requirements. More information on the MIT SES Doctoral Program can be found \u003ca href=\"https://idss.mit.edu/academics/ses_doc/\"\u003ehere\u003c/a\u003e.\u003c/p\u003e\r\n\r\n\u003cp\u003eIn addition, learners who successfully earn the MicroMasters program certificate in Statistics and Data Science are now eligible to earn credit at a number of universities across the globe to fast track their pursuit of a full Master’s degree. A list of pathways to graduate programs can be found \u003ca href=\"https://micromasters.mit.edu/ds/pathways-graduate-programs/\"\u003ehere\u003c/a\u003e.\u003c/p\u003es course at MIT. The program is designed for learners who want to acquire sophisticated and rigorous training in data science without leaving their day job but without compromising quality. There is no application process, but college-level calculus and comfort with mathematical reasoning and Python programming are highly recommended if you want to excel.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"349:Tafb,"])</script><script>self.__next_f.push([1,"\u003cp\u003eData scientists bring value to organizations across industries because they are able to solve complex challenges with data and drive important decision-making processes. Not only is there a huge demand, but there is a significant shortage of qualified data scientists with 54% of the most rigorous data science positions requiring a degree higher than a bachelor’s.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis MicroMasters® program in Statistics and Data Science (SDS) was developed by MITx and the \u003ca href=\"https://idss.mit.edu/academics/micromasters-program-in-statistics-and-data-science-sds/\"\u003eMIT Institute for Data, Systems, and Society (IDSS)\u003c/a\u003e. It is a multidisciplinary approach comprised of four separate tracks with four online courses each and a virtually proctored exam. Each track focuses on a combination of methods-centered courses and domain analysis courses to provide you with foundational knowledge and hands-on training. All learners complete the Probability and Machine Learning courses, two other courses determined by the chosen track, and the Capstone Exam.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eGeneral Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you to become an informed and effective practitioner of data science who adds value to your organization across industries.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-general-track\"\u003eExplore the General track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eMethods Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you with in-depth knowledge of data science and time series analysis and will enable you to conduct rigorous analysis, inform decision-making processes, and contribute to evidence-based practices across industries.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-methods-track\"\u003eExplore the Methods track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eSocial Sciences Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you to extract meaningful insights from social, cultural, economic, and policy-related data and equip you to tackle complex real-world problems and contribute to cutting-edge advancements in AI and data-driven solutions within all social sciences.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-social-sciences-track\"\u003eYou are currently exploring the Social Sciences track\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eTime Series and Social Sciences Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will equip you to analyze the impact of interventions on time series data, preparing you for roles in economics, public policy, and social sciences where understanding temporal dynamics is crucial for informed decision-making and policy formulation.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-time-series-and-social-sciences-track\"\u003eExplore the Time Series and Social Sciences track here\u003c/a\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"34a:T414,Master the foundations of data science, data analysis, time series with interventions, and machine learning.,Analyze big data and make data-driven predictions through probabilistic modeling and statistical inference; identify and deploy appropriate modeling and methodologies in order to extract meaningful information for decision making.,Develop and build machine learning algorithms to extract meaningful information from seemingly unstructured data; learn popular unsupervised learning methods, including clustering methodologies and supervised methods such as deep neural networks.,Understand the interplay between statistics and computation for the analysis of real data.,Learn the methods for harnessing and analyzing data to answer questions of cultural, social, economic, and policy interest, and then assess that knowledge.,Finishing this track of the MicroMasters program will prepare you for job titles such as: Data Scientist, Data Analyst, Business Intelligence Analyst, Systems Analyst, Data Engineer in Social Sciences contexts.34b:Tcf0,"])</script><script>self.__next_f.push([1,"\u003cp\u003eData scientists bring value to organizations across industries because they are able to solve complex challenges with data and drive important decision-making processes. Not only is there a huge demand, but there is a significant shortage of qualified data scientists with 54% of the most rigorous data science positions requiring a degree higher than a bachelor’s.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis MicroMasters® program in Statistics and Data Science (SDS) was developed by MITx and the \u003ca href=\"https://idss.mit.edu/academics/micromasters-program-in-statistics-and-data-science-sds/\"\u003eMIT Institute for Data, Systems, and Society (IDSS)\u003c/a\u003e. It is a multidisciplinary approach comprised of four separate tracks with four online courses each and a virtually proctored exam. Each track focuses on a combination of methods-centered courses and domain analysis courses to provide you with foundational knowledge and hands-on training. All learners complete the Probability and Machine Learning courses, two other courses determined by the chosen track, and the Capstone Exam.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eGeneral Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you to become an informed and effective practitioner of data science who adds value to your organization across industries.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-general-track\"\u003eYou are currently exploring the General track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eMethods Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you with in-depth knowledge of data science and time series analysis and will enable you to conduct rigorous analysis, inform decision-making processes, and contribute to evidence-based practices across industries.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-methods-track\"\u003eExplore the Methods track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eSocial Sciences Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will prepare you to extract meaningful insights from social, cultural, economic, and policy-related data and equip you to tackle complex real-world problems and contribute to cutting-edge advancements in AI and data-driven solutions within all social sciences.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-social-sciences-track\"\u003eExplore the Social Sciences track here\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eTime Series and Social Sciences Track\u003c/b\u003e\u003cbr\u003e\r\nThis track will equip you to analyze the impact of interventions on time series data, preparing you for roles in economics, public policy, and social sciences where understanding temporal dynamics is crucial for informed decision-making and policy formulation.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003ca href=\"https://www.edx.org/masters/micromasters/mitx-statistics-and-data-science-time-series-and-social-sciences-track\"\u003eYou are currently exploring the Time Series and Social Sciences track\u003c/a\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eAll tracks are taught by MIT faculty and administered by IDSS at a similar pace and level of rigor as an on-campus course at MIT. The program is designed for learners who want to acquire sophisticated and rigorous training in data science without leaving their day job but without compromising quality. There is no application process, but college-level calculus and comfort with mathematical reasoning and Python programming are highly recommended if you want to excel.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"34c:Tc05,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis comprehensive MicroBachelors program in Mathematics and Statistics Fundamentals introduces students to the essential mathematical and statistical concepts, methods and techniques which they can use to grow their skills in quantitative careers, or as a step towards further study at undergraduate level or in specialised subjects.\u003c/p\u003e\r\n\r\n\u2028\u003cp\u003eSpanning four individual courses, all of which are self-paced and asynchronous, this programme provides students with maximum flexibility to learn with a world-leading institution from anywhere in the world in a way that fits their schedule. Students will be introduced to foundational mathematical and statistical concepts, as well as gain essential skills in the methods of calculus and linear algebra required for economic-based subjects.\u003c/p\u003e\r\n\u2028\r\n\u003cp\u003eThese courses are based on service-level statistics courses offered as part of the University of London degree programmes in Economics, Management, Finance and the Social Sciences (EMFSS), with academic direction from the London School of Economics and Political Science (LSE). They equip students with the fundamental knowledge and tools to set them up for success in second and third-year courses in subjects such as economics, finance, data science, mathematics, statistics, business analytics and programming.\u003c/p\u003e\r\n\r\n\u003cp\u003eThose that complete this MicroBachelors program may wish to go on to apply to the University of London's academically rigorous EMFSS degree programmes that give learners the opportunity to earn a BSc from a top London university wherever they are in the world.\u003c/p\u003e\r\n\r\n\u003cp\u003eShould you wish, you may elect to just study some of the individual courses within the MicroBachelors program, perhaps to build or refresh quantitative skills for career advancement.\u003c/p\u003e\r\n\r\n\u003cp\u003eNo prior mathematics or statistics knowledge is required for this programme.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eMathematics 1a: Differential calculus\u003c/b\u003e \r\n\u003cul\u003e\r\n\u003cli\u003eFunctions and graphs\u003c/li\u003e\r\n\u003cli\u003eThe derivative\u003c/li\u003e\r\n\u003cli\u003eCurve sketching and optimisation\u003c/li\u003e \r\n\u003cli\u003eFunctions of two variables and partial derivatives\u003c/li\u003e \r\n\u003cli\u003eCritical points of two-variable functions\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eMathematics 1b: Integral calculus, algebra, and applications\u003c/b\u003e \r\n\u003cul\u003e\r\n\u003cli\u003eIntegration\u003c/li\u003e\r\n\u003cli\u003eProfit maximisation\u003c/li\u003e\r\n\u003cli\u003eConstrained optimisation\u003c/li\u003e \r\n\u003cli\u003eMatrices, vectors, and linear equations\u003c/li\u003e \r\n\u003cli\u003eSequences, series, and financial modelling\u003c/li\u003e\r\n\u003c/ul\u003e\r\n \r\n\u003cp\u003e\u003cb\u003eStatistics 1a: Introductory statistics, probability and estimation\u003c/b\u003e \r\n\u003cul\u003e\r\n\u003cli\u003eMathematical revision and the nature of statistics\u003c/li\u003e \r\n\u003cli\u003eData visualisation and descriptive statistics\u003c/li\u003e \r\n\u003cli\u003eProbability theory\u003c/li\u003e \r\n\u003cli\u003eThe normal distribution and ideas of sampling\u003c/li\u003e \r\n\u003cli\u003ePoint and interval estimation\u003c/li\u003e \r\n\u003c/ul\u003e\u003c/p\u003e\r\n \r\n\u003cp\u003e\u003cb\u003eStatistics 1b: Statistical methods\u003c/b\u003e \r\n\u003cul\u003e\r\n\u003cli\u003eHypothesis testing I\u003c/li\u003e \r\n\u003cli\u003eHypothesis testing II\u003c/li\u003e\r\n\u003cli\u003eContingency tables and the chi-squared test\u003c/li\u003e \r\n\u003cli\u003eSampling design and some ideas underlying causation\u003c/li\u003e \r\n\u003cli\u003eCorrelation and linear regression\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"34d:Tbe2,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis comprehensive MicroBachelors program in Statistics Fundamentals introduces students to the essential statistical concepts, methods and techniques which they can use to grow their skills in quantitative careers, or as a step towards further study at undergraduate level or in specialised subjects.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u2028Spanning four individual courses, all of which are self-paced and asynchronous, this programme provides students with maximum flexibility to learn with a world-leading institution from anywhere in the world in a way that fits their schedule. Students will first be introduced to core statistical concepts and gain essential skills to analyse, summarise, and present data, before progressing to probability, distribution theory and statistical inference.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u2028\u2028These courses are based on service-level statistics courses offered as part of the University of London degree programmes in Economics, Management, Finance and the Social Sciences (EMFSS), with academic direction from the London School of Economics and Political Science (LSE). They equip students with the fundamental statistical knowledge and tools to set them up for success in second and third-year courses in subjects such as economics, finance, data science, mathematics, statistics, business analytics and programming.\u003c/p\u003e \r\n\r\n\u003cp\u003e\u2028Those that complete this MicroBachelors program may wish to go on to apply to the University of London's academically rigorous EMFSS degree programmes that give learners the opportunity to earn a BSc from a top London university wherever they are in the world.\u003c/p\u003e \r\n\r\n\u003cp\u003e\u2028Should you wish, you may elect to just study some of the individual courses within the MicroBachelors program, perhaps to build or refresh quantitative skills for career advancement.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u2028No prior statistics knowledge is required for this programme.\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eStatistics 1a: Introductory statistics, probability and estimation\u003c/b\u003e\r\n\u003cul\u003e\r\n\u003cli\u003eMathematical revision and the nature of statistics\u003c/li\u003e\r\n\u003cli\u003eData visualisation and descriptive statistics\u003c/li\u003e \r\n\u003cli\u003eProbability theory\u003c/li\u003e \r\n\u003cli\u003eThe normal distribution and ideas of sampling\u003c/li\u003e \r\n\u003cli\u003ePoint and interval estimation\u003c/li\u003e \r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eStatistics 1b: Statistical methods\u003c/b\u003e\r\n\u003cul\u003e\r\n\u003cli\u003eHypothesis testing I\u003c/li\u003e \r\n\u003cli\u003eHypothesis testing II\u003c/li\u003e \r\n\u003cli\u003eContingency tables and the chi-squared test\u003c/li\u003e \r\n\u003cli\u003eSampling design and some ideas underlying causation\u003c/li\u003e \r\n\u003cli\u003eCorrelation and linear regression\u003c/li\u003e \r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eStatistics 2a: Probability and distribution theory \u003c/b\u003e\r\n\u003cul\u003e\r\n\u003cli\u003eProbability theory I\u003c/li\u003e \r\n\u003cli\u003eProbability theory II\u003c/li\u003e \r\n\u003cli\u003eRandom variables\u003c/li\u003e \r\n\u003cli\u003eCommon distributions of random variables\u003c/li\u003e \r\n\u003cli\u003eMultivariate random variables\u003c/li\u003e \r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eStatistics 2b: Statistical inference\u003c/b\u003e\r\n\u003cul\u003e\r\n\u003cli\u003eSampling distributions of statistics\u003c/li\u003e \r\n\u003cli\u003ePoint estimation I\u003c/li\u003e \r\n\u003cli\u003ePoint estimation II and interval estimation\u003c/li\u003e \r\n\u003cli\u003eHypothesis testing\u003c/li\u003e \r\n\u003cli\u003eAnalysis of variance (ANOVA)\u003c/li\u003e \r\n\u003c/ul\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"34e:T925,"])</script><script>self.__next_f.push([1,"\u003cp\u003eWhether you want to make a strong start to a master’s degree, solidify your knowledge in a professional context or simply brush up on fundamentals in probability and statistics, this program will get you up to speed.\u003c/p\u003e\r\n\r\n\u003cp\u003eStatistics is used quite intensively in many engineering contexts and master’s programs. As soon as you are dealing with real-life data, you will need to get an idea of what these data tell you and how you can visualize this (descriptive statistics). You will also want to perform some analysis (inferential statistics), build a model that mimics reality, estimate some quantities, or test some hypotheses. Along the way you will learn how to apply these concepts to datasets, using the statistical software R.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program also provides an introduction to probability theory. You will encounter discrete and continuous random variables and learn in which situations they appear, what their properties are and how they interact. Probability theory can be applied to learn more about real-life problems, and it is useful for building models. Moreover, it provides the basis for statistics and applications in data analysis. Therefore, it is a useful subject for any aspiring engineer.\u003c/p\u003e\r\n\r\n\u003cp\u003eThese courses are self-paced, self-contained and modular, to make it easier to review specific topics and practice as often as you want without having to follow the entire courses.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program is ideal for:\r\n\u003cul\u003e\r\n\u003cli\u003eProspective engineering students who want to meet the prerequisites for a MSc program, be better prepared or refresh their mathematics knowledge before starting a master’s degree.\u003c/li\u003e\r\n\u003cli\u003eEngineering or bachelor students who realize that they have a gap in their math knowledge or would like an additional challenge in mathematics not offered by their studies.\u003c/li\u003e\r\n\u003cli\u003eWorking professionals who would like to improve their math knowledge.\u003c/li\u003e\r\n\u003cli\u003eAnyone interested in university level mathematics.\u003c/li\u003e\r\n\u003c/ul\u003e\r\n\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program will refresh your knowledge and review the relevant topics. As review courses, you are expected to have previously studied or be familiar with most of the material.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program is part of our series ‘Mastering Mathematics for Engineers’, together with ‘Mastering Calculus’ and ‘Mastering Linear Algebra’.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"34f:Ta63,"])</script><script>self.__next_f.push([1,"\u003cp\u003eStatistics is everywhere! This program will prepare learners for subsequent advanced courses and eventually careers in consultancy, research, and industry – any professions involving data analysis and optimization of real-world systems. The course is brought to you by faculty from Georgia Tech’s top-ranked School of Industrial and Systems Engineering.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis first half of this program (Course 1) provides an introduction to basic statistical concepts. We begin by walking through a library of probability distributions, where we motivate their uses and go over their fundamental properties. These distributions include such important folks as the Bernoulli, binomial, geometric, Poisson, uniform, exponential, and normal distributions, just to name a few. Particular attention is paid to the normal distribution, because it leads to the Central Limit Theorem (the most-important mathematical result in the universe, actually), which enables us to make probability calculations for arbitrary averages and sums of random variables.\u003c/p\u003e \r\n\r\n\u003cp\u003eWe then discuss elementary descriptive statistics and estimation methods, including unbiased estimation, maximum likelihood estimation, and the method of moments – you gotta love your MoM! Finally, we describe the t, χ2, and F sampling distributions, which will prove to be useful in upcoming statistical applications.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe second half of the program (Course 2) covers two important methodologies in statistics – confidence intervals and hypothesis testing. Confidence intervals are encountered in everyday life, and allow us to make probabilistic statements such as: “Based on the sample of observations we conducted, we are 95% sure that the unknown mean lies between A and B,” and “We are 95% sure that Candidate Smith’s popularity is 52% +/- 3%.” We begin the course by discussing what a confidence interval is and how it is used. We then formulate and interpret confidence intervals for a variety of probability distributions and their parameters.\u003c/p\u003e\r\n\r\n\u003cp\u003eHypothesis testing allows us to pose hypotheses and test their validity in a statistically rigorous way. For instance, “Does a new drug result in a higher cure rate than the old drug” or “Is the mean tensile strength of item A greater than that of item B?” The second half the course begins by motivating hypothesis tests and how they are used. We then discuss with the types of errors that can occur with hypothesis testing, and how to design tests to mitigate those errors. Finally, we formulate and interpret hypothesis tests for a variety of probability distributions and their parameters.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"350:T494,Bayes’ Theorem. Differences between classical (frequentist) and Bayesian inference.,Posterior inference: summarizing posterior distributions, credible intervals, posterior probabilities, posterior predictive distributions and data visualization.,Gamma-poisson, beta-binomial and normal conjugate models for data analysis.,Bayesian regression analysis and analysis of variance (ANOVA).,Use of simulations for posterior inference. Simple applications of Markov chain-Monte Carlo (MCMC) methods and their implementation in R.,Bayesian cluster analysis.,Model diagnostics and comparison.,Make sure to answer the actual research question rather than “apply methods to the data”,Using latent (unobserved) variables and dealing with missing data.,Multivariate analysis within the context of mixed effects linear regression models. Structure, assumptions, diagnostics and interpretation. Posterior inference and model selection.,Why Monte Carlo integration works and how to implement your own MCMC Metropolis-Hastings algorithm in R.,Bayesian model averaging in the context of change-point problem. Pinpointing the time of change and obtaining uncertainty estimates for it.351:T735,\u003cp\u003eBusiness analytics is the ability to collate and combine multiple streams of data to better understand business processes, customer demands, and relationships between multiple agents.\u003c/p\u003e\r\n\r\n\u003cp\u003eWe live and work in an uncertain world. Every day, business managers, economists, line managers, supervisors and front-line workers must make decisions and predictions based on limited information. This program will help you to make better informed decisions for the future to answer questions like:\r\n\u003cul\u003e\r\n\u003cli\u003eWhat is the probability that a staff member will need more than 20 days of sick leave in a year?\u003c/li\u003e\r\n\u003cli\u003eDoes the new system for preparing food for customers offer a significant improvement over the old system, or is it the same?\u003c/li\u003e\r\n\u003cli\u003eHow are sales likely to fluctuate over the next 12 months, based on trends in historical data?\u003c/li\u003e\r\n\u003c/ul\u003e\r\n"])</script><script>self.__next_f.push([1,"\u003c/p\u003e\r\n\u003cp\u003eTo inform strategic decisions and remain competitive, businesses must leverage the insights contained in the large volumes of data produced both within the business and in the broader business environment. Providing you with skills that are highly sought after in the global workplace, this program will equip you with the analytical know-how needed to extract meaning from complex data sets and translate this meaning into actionable insights. \u003c/p\u003e\r\n\r\n\u003cp\u003eThis program covers a variety of techniques applicable to the collection, presentation, interpretation, and use of numerical data. It provides a foundation for understanding statistical procedures that will help you undertake solid statistical analysis in business and economic situations. This includes statistical inference, probability \u0026 sampling distributions, estimation, hypothesis tests, correlation \u0026 regression, experimental design, sample survey design, quality sampling, and modern business decision theory.\u003c/p\u003e352:T5c2,\u003cp\u003eThis is the second of three courses in the Machine Learning Operations Program using Azure Machine Learning.\u003c/p\u003e\n\u003cp\u003eData Science, AI, and Machine Learning projects can deliver an amazing return on investment. But, in practice, most projects that look great in the lab (and would work if implemented!) never see the light of day. They could save or make the organization millions of dollars but never make it all the way into production. What’s going on? It turns out that making decisions in a whole new way is a big challenge to implement--for many technical, business andhuman-naturereasons. After decades of experience though, our team has learned how to turn this around and actually get working models into production the great majority of the time. A key part of deployment is excellence in data engineering, and is why we developed this course: \u003cstrong\u003eMLOps1 (Azure): Deploying AI \u0026amp; ML Models in Production using Microsoft Azure Machine Learning.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYou will get hands on experience with topics like data pipelines, data a"])</script><script>self.__next_f.push([1,"nd model “versioning”, model storage, data artifacts, and more.\u003c/p\u003e\n\u003cp\u003eMost importantly, by the end of this course, you will know...\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhat data engineers need to know to work effectively with data scientists\u003c/li\u003e\n\u003cli\u003eHow to embed a predictive model in a pipeline that takes in data and outputs predictions automatically\u003c/li\u003e\n\u003cli\u003eHow to moniter the model’s performance and follow best practices\u003c/li\u003e\n\u003c/ul\u003e353:T526,\u003cp\u003eWhat is Predictive Analytics? These methods lie behind the most transformative technologies of the last decade, that go under the more general name Artificial Intelligence or AI. In this course, the focus is on the skills that will allow you to fit a model to data, and measure how well it performs. \u003c/p\u003e\n\u003cp\u003eThese skills also go under the names \"machine learning\" and \"data science,\" the latter being a broader term than machine learning or predictive analytics but narrower than AI. This course is part of the Machine Learning Operations (MLOps) Program. We will be doing enough data science so that you get hands-on familiarity with understanding a dataset, fitting a model to it, and generating predictions. As you get further into the program, you will learn how to fit that model into a machine learning pipeline.\u003c/p\u003e\n\u003cp\u003eYou will get hands-on experience with the top techniques in supervised learning: linear and logistic regression modeling, decision trees, neural networks, ensembles, and much more.\u003c/p\u003e\n\u003cp\u003eBut most importantly, by the end of this course, you will know\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhat a predictive model can (and cannot) do, and how its data is structured\u003c/li\u003e\n\u003cli\u003eHow to predict a numerical output, or a class (category)\u003c/li\u003e\n\u003cli\u003eHow to measure the out-of-sample (future)performance of a model\u003c/li\u003e\n\u003c/ul\u003e354:T5a7,\u003cp\u003eThis is the second of three courses in the Machine Learning Operations Program using Amazon Web Services (AWS).\u003c/p\u003e\n\u003cp\u003eData Science, AI, and Machine Learning projects can deliver an amazing return on investment. But, in practice, most projects that look great in the lab (and would work "])</script><script>self.__next_f.push([1,"if implemented!) never see the light of day. They could save or make the organization millions of dollars but never make it all the way into production. What’s going on? It turns out that making decisions in a whole new way is a big challenge to implement--for many technical, business and human-nature reasons. After decades of experience though, our team has learned how to turn this around and actually get working models into production the great majority of the time. A key part of deployment is excellence in data engineering, and is why we developed this course: MLOps1(AWS): Deploying AI \u0026amp; ML Models in Production.\u003c/p\u003e\n\u003cp\u003eYou will get hands-on experience with topics like data pipelines, data and model “versioning”, model storage, data artifacts, and more.\u003c/p\u003e\n\u003cp\u003eMost importantly, by the end of this course, you will know...\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eWhat data engineers need to know to work effectively with data scientists\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHow to embed a predictive model in a pipeline that takes in data and outputs predictions automatically\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eHow to monitor the model’s performance and follow best practices\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e355:T5a0,\u003cp\u003eThis is the second of three courses in the Machine Learning Operations Program using Google Cloud Platform (GCP).\u003c/p\u003e\n\u003cp\u003eData Science, AI, and Machine Learning projects can deliver an amazing return on investment. But, in practice, most projects that look great in the lab (and would work if implemented!) never see the light of day. They could save or make the organization millions of dollars but never make it all the way into production. What’s going on? It turns out that making decisions in a whole new way is a big challenge to implement--for many technical, business and human-nature reasons. After decades of experience though, our team has learned how to turn this around and actually get working models into production the great majority of the time. A key part of deployment is excellence in data engineering, and is why we developed this course: \u003cstrong\u003eMLOps1 (GC"])</script><script>self.__next_f.push([1,"P): Deploying AI \u0026amp; ML Models in Production\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eYou will get hands-on experience with topics like data pipelines, data and model “versioning”, model storage, data artifacts, and more.\u003c/p\u003e\n\u003cp\u003eMost importantly, by the end of this course, you will know...\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhat data engineers need to know to work effectively with data scientists\u003c/li\u003e\n\u003cli\u003eHow to embed a predictive model in a pipeline that takes in data and outputs predictions automatically\u003c/li\u003e\n\u003cli\u003eHow to monitor the model’s performance and follow best practices\u003c/li\u003e\n\u003c/ul\u003e356:T560,\u003cp\u003eConcern about the harmful effects of machine learning algorithms and big data AI models (bias and more) has resulted in greater attention to the fundamentals of data ethics. News stories appear regularly about credit algorithms that discriminate against women, medical algorithms that discriminate against African Americans, hiring algorithms that base decisions on gender, and more. In most cases, the data scientists who developed and deployed these decision making algorithms and data processes had no such intentions, and were unaware of the harmful impact of their work.\u003c/p\u003e\n\u003cp\u003eThis data science ethics course, the second in the data science ethics program for both practitioners and managers, provides guidance and practical tools to build better models, do better data analysis and avoid these problems. You’ll learn about ****\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eTools for model interpretability\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGlobal versus local model interpretability methods\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eMetrics for model fairness\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eAuditing your model for bias and fairness\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eRemedies for biased models\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe course offers real world problems and datasets, a framework data scientists can use to develop their projects, and an audit process to follow in reviewing them. Case studies with ethical considerations, along with Python code, are provided.\u003c/p\u003e357:T839,"])</script><script>self.__next_f.push([1,"\u003cp\u003eMachine Learning Operations (MLOps) lies at the core of the AI Engineering function. In Statistics.com’s MLOps with Azure program you will learn to combine data engineering and data science skills to deploy machine learning models.\u003c/p\u003e\r\n\r\n\u003cp\u003eMost of the work in deploying AI models does not lie in developing models. Rather, it lies in developing, monitoring and maintaining an automated, self-monitoring data pipeline through a model and into actions. The common practice of tossing a project back and forth between pure data scientists and pure data engineers leads to delay and errors. This has created a need for AI engineers who have knowledge of each function. Mastering machine learning deployment skills on the Microsoft Azure platform is a sure path to career success.\u003c/p\u003e\r\n \r\n\u003cp\u003eIn this course, you will learn how to work with data scientists to deploy machine learning models that can learn from data, and generate predictions, recommendations or decisions. This process usually is automated and that is where MLOps and AI engineering skills are needed.\u003c/p\u003e\r\n\r\n\u003cp\u003eYou will focus on developing the skills needed to create a Microsoft Azure pipeline that:\r\n\u003cul\u003e\r\n\u003cli\u003eIngests data to train a predictive model\u003c/li\u003e\r\n\u003cli\u003eFeeds the model as it operates\u003c/li\u003e\r\n\u003cli\u003eScores the data on an ongoing basis\u003c/li\u003e\r\n\u003cli\u003eOutputs an action\u003c/li\u003e\r\n\u003cli\u003eIntegrates into business applications\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eAdditionally, you will learn to develop the pipeline so that it will continuously monitor several points of operation, including the incoming data (for data drift) and the decision outputs (for anomalies).\r\nStatistics.com is the training platform of Elder Research (elderresearch.com), an internationally recognized data analytics consulting firm that, since 1995, has consulted for hundreds of leading businesses in data strategy, data science, and data engineering. Elder Research leverages the wisdom gained by solving a wide variety of real-world problems to infuse their education programs on the Statistics.com platform with the most cutting-edge training that can be applied day one.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"358:T831,"])</script><script>self.__next_f.push([1,"\u003cp\u003eMachine Learning Operations (MLOps) lies at the core of the AI Engineering function. In Statistics.com’s MLOps with AWS program you will learn to combine data engineering and data science skills to deploy machine learning models.\u003c/p\u003e\r\n\r\n\u003cp\u003eMost of the work in deploying AI models does not lie in developing models. Rather, it lies in developing, monitoring and maintaining an automated, self-monitoring data pipeline through a model and into actions. The common practice of tossing a project back and forth between pure data scientists and pure data engineers leads to delay and errors. This has created a need for AI engineers who have knowledge of each function. Mastering machine learning deployment skills on the Amazon Web Services platform is a sure path to career success.\u003c/p\u003e\r\n \r\n\u003cp\u003eIn this course, you will learn how to work with data scientists to deploy machine learning models that can learn from data, and generate predictions, recommendations or decisions. This process usually is automated and that is where MLOps and AI engineering skills are needed.\u003c/p\u003e\r\n\r\n\u003cp\u003eYou will focus on developing the skills needed to create an AWS pipeline that:\r\n\u003cul\u003e\r\n\u003cli\u003eIngests data to train a predictive model\u003c/li\u003e\r\n\u003cli\u003eFeeds the model as it operates\u003c/li\u003e\r\n\u003cli\u003eScores the data on an ongoing basis\u003c/li\u003e\r\n\u003cli\u003eOutputs an action\u003c/li\u003e\r\n\u003cli\u003eIntegrates into business applications\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eAdditionally, you will learn to develop the pipeline so that it will continuously monitor several points of operation, including the incoming data (for data drift) and the decision outputs (for anomalies).\r\nStatistics.com is the training platform of Elder Research (elderresearch.com), an internationally recognized data analytics consulting firm that, since 1995, has consulted for hundreds of leading businesses in data strategy, data science, and data engineering. Elder Research leverages the wisdom gained by solving a wide variety of real-world problems to infuse their education programs on the Statistics.com platform with the most cutting-edge training that can be applied day one.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"359:T828,"])</script><script>self.__next_f.push([1,"\u003cp\u003eMachine Learning Operations (MLOps) lies at the core of the AI Engineering function. In Statistics.com’s MLOps with GCP program you will learn to combine data engineering and data science skills to deploy machine learning models.\u003c/p\u003e\r\n\r\n\u003cp\u003eMost of the work in deploying AI models does not lie in developing models. Rather, it lies in developing, monitoring and maintaining an automated, self-monitoring data pipeline through a model and into actions. The common practice of tossing a project back and forth between pure data scientists and pure data engineers leads to delay and errors. This has created a need for AI engineers who have knowledge of each function. Mastering machine learning deployment skills on the Google Cloud platform is a sure path to career success.\u003c/p\u003e\r\n \r\n\u003cp\u003eIn this course, you will learn how to work with data scientists to deploy machine learning models that can learn from data, and generate predictions, recommendations or decisions. This process usually is automated and that is where MLOps and AI engineering skills are needed.\u003c/p\u003e\r\n \r\n\u003cp\u003eYou will focus on developing the skills needed to create a GCP pipeline that:\r\n\u003cul\u003e\r\n\u003cli\u003eIngests data to train a predictive model\u003c/li\u003e\r\n\u003cli\u003eFeeds the model as it operates\u003c/li\u003e\r\n\u003cli\u003eScores the data on an ongoing basis\u003c/li\u003e\r\n\u003cli\u003eOutputs an action\u003c/li\u003e\r\n\u003cli\u003eIntegrates into business applications\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eAdditionally, you will learn to develop the pipeline so that it will continuously monitor several points of operation, including the incoming data (for data drift) and the decision outputs (for anomalies).\r\nStatistics.com is the training platform of Elder Research (elderresearch.com), an internationally recognized data analytics consulting firm that, since 1995, has consulted for hundreds of leading businesses in data strategy, data science, and data engineering. Elder Research leverages the wisdom gained by solving a wide variety of real-world problems to infuse their education programs on the Statistics.com platform with the most cutting-edge training that can be applied day one.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"35a:T7e7,\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIf you have specific questions about this course, please contact us at\u003ca href=\"mailto:sds-mm@mit.edu\"\u003e sds-mm@mit.edu\u003c/a\u003e.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMachine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), advertisers, and financial institutions employ machine learning algorithms for content recommendation, predicting customer behavior, compliance, or risk.\u003c/p\u003e\n\u003cp\u003eAs a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control.\u003c/p\u003e\n\u003cp\u003eIn this course, students will learn about principles and algorithms for turning training data into effective automated predictions. We will cover:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRepresentation, over-fitting, regularization, generalization, VC dimension;\u003c/li\u003e\n\u003cli\u003eClustering, classification, recommender problems, probabilistic modeling, reinforcement learning;\u003c/li\u003e\n\u003cli\u003eOn-line algorithms, support vector machines, and neural networks/deep learning.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eStudents will implement and experiment with the algorithms in several Python projects designed for different practical applications.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis course is part of the\u003ca href=\"https://www.edx.org/micromasters/mitx-statistics-and-data-science\"\u003e MITx MicroMasters Program in Statistics and Data Science\u003c/a\u003e. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit \u003ca href=\"https://micromasters.mit.edu/ds/\"\u003ehttps://micromasters.mit.edu/ds/\u003c/a\u003e.\u003c/strong\u003e\u003c/p\u003e35b:T571,\u003cp\u003eSupp"])</script><script>self.__next_f.push([1,"ly chains are complex systems involving multiple businesses and organizations with different goals and objectives. Many different analytical methods and techniques are used by researchers and practitioners alike to better design and manage their supply chains. This business and management course introduces the primary methods and tools that you will encounter in your study and practice of supply chains. We focus on the application of these methods, not necessarily the theoretical underpinnings.\u003c/p\u003e\n\u003cp\u003eWe will begin with an overview of introductory probability and decision analysis to ensure that students understand how uncertainty can be modeled. Next, we will move into basic statistics and regression. Finally, we will introduce optimization modeling from unconstrained to linear, non-linear, and mixed integer linear programming.\u003c/p\u003e\n\u003cp\u003eThis is a hands-on course. Students will use spreadsheets extensively to apply these techniques and approaches in case studies drawn from actual supply chains.\u003c/p\u003e\n\u003cp\u003eSC0x is different from our other courses as it is self-paced and has a scheduled final exam. All material is made available during the second week, allowing learners to begin with any topic at their own convenience.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis is an open enrollment course\u003c/strong\u003e , making it accessible for almost anyone, anywhere in the world to enroll and learn for free.\u003c/p\u003e35c:T4ff,\u003cul\u003e\n\u003cli\u003eProbability distributions in finance\u003c/li\u003e\n\u003cli\u003eTime-series models: random walks, ARMA, and GARCH\u003c/li\u003e\n\u003cli\u003eContinuous-time stochastic processes\u003c/li\u003e\n\u003cli\u003eOptimization\u003c/li\u003e\n\u003cli\u003eLinear algebra of asset pricing\u003c/li\u003e\n\u003cli\u003eStatistical and econometric analysis\u003c/li\u003e\n\u003cli\u003eMonte Carlo simulation\u003c/li\u003e\n\u003cli\u003eApplied computational techniques\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr /\u003e\n\u003c/strong\u003e\u003cstrong\u003eHow to Prepare\u003c/strong\u003e \u003cstrong\u003e\u003cbr /\u003e\n\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are a number of prerequisites for this course: Calculus (multivariable), probability and statistics, linear algebra, and basic programming skills. Learners are urged to thoroughly review the \u003ca href=\"https://lea"])</script><script>self.__next_f.push([1,"rning.edx.org/course/course-v1:MITx+FIN.Px+2T2022\"\u003e15.455x Prerequisites and Resources site\u003c/a\u003e* which details these prerequisites and provides a robust suite of resources to prepare you for this advanced math course, including a readiness assessment to help you confirm that you have a solid understanding of the 15.455x prerequisite material, and to indicate directions of study in case you need to build on your current foundations prior to starting the course.\u003c/p\u003e\n\u003cp\u003e*Please note that you will need to enroll in order to access the Prerequisite and Resources site. To do so, click the link above, then click \"Enroll.\"\u003c/p\u003e35d:T49b,\u003cp\u003eCreated specifically for those who are new to the study of probability, or for those who are seeking an approachable review of core concepts prior to enrolling in a college-level statistics course, Fat Chance prioritizes the development of a mathematical mode of thought over rote memorization of terms and formulae. Through highly visual lessons and guided practice, this course explores the quantitative reasoning behind probability and the cumulative nature of mathematics by tracing probability and statistics back to a foundation in the principles of counting. \u003c/p\u003e\n\u003cp\u003eIn Modules 1 and 2, you will be introduced to basic counting skills that you will build upon throughout the course. In Module 3, you will apply those skills to simple problems in probability. In Modules 4 through 6, you will explore how those ideas and techniques can be adapted to answer a greater range of probability problems. Lastly, in Module 7, you will be introduced to statistics through the notion of expected value, variance, and the normal distribution. You will see how to use these ideas to approximate probabilities in situations where it is difficult to calculate their exact values.\u003c/p\u003e35e:T555,\u003cp\u003eIn this first course of the Six Sigma Program, you will understand the background and meaning of Six Sigma and the five steps of the DMAIC process improvement flow: Define, Measure, Analyze, Improve, and Control. Discuss w"])</script><script>self.__next_f.push([1,"hat \"Quality\" means and how to identify the Voice of the Customer.\u003c/p\u003e\n\u003cp\u003eYou will learn how to set an improvement project goal, calculate process yield, and identify Critical-to-Quality parameters.\u003c/p\u003e\n\u003cp\u003eYou will learn how to map a process and to use the necessary statistical techniques to establish the baseline performance of a process and to calculate the process capability.\u003c/p\u003e\n\u003cp\u003eTo complement the lectures, we provide interactive exercises, which allow learners to see the statistics \"in action.\" Learners then master the statistical concepts by completing practice problems. These are then reinforced using interactive case studies, which illustrate the application of the statistics in quality improvement situations.\u003c/p\u003e\n\u003cp\u003eUpon successful completion of this program, learners will earn the TUM Lean and Six Sigma Yellow Belt certification, confirming mastery of Lean Six Sigma fundamentals to a Green Belt level. The material is based on the American Society for Quality (www.asq.org) Body of Knowledge up to a Green Belt Level. The Professional Certificate is designed as preparation for a Lean Six Sigma Green Belt exam.\u003c/p\u003e35f:T508,\u003cp\u003eCan you think of an area of your life that is influenced by statistics? Many times when we think about statistics in our daily lives, we think about numerical expressions of statistics, such as the number of daily COVID cases in our county, the percentage of students admitted each year to our university, or the number of people that voted in the last election. From each of these examples, we could go on to make inferences or look to answer questions based on this data, such as whether to open restaurants, how many new students are psychology majors, or if a specific issue drove voters to the polls in a specific state.\u003c/p\u003e\n\u003cp\u003eThis course will begin by introducing the basic concepts of how to describe and visualize data, the fundamentals of using statistics to make inferences, and the logic of null hypothesis testing. Various types of hypothesis tests will be introduced, along with cri"])</script><script>self.__next_f.push([1,"teria for selecting which is appropriate for different study conditions. As an extension of null hypothesis significance tests, you will learn about how to interpret effect sizes and confidence intervals, along with statistical power, before being introduced to alternatives to null hypothesis significance testing. All this is fleshed out in Data Analysis for the Behavioral Sciences.\u003c/p\u003e360:T543,\u003cp\u003eIn this course, you will develop a solid foundational understanding of the most common statistical methods used in health care data analysis.\u003c/p\u003e\n\u003cp\u003eThis course covers some of the most common univariate and multivariate statistical methods used in healthcare data analysis. Students will also learn how to apply these methods using a statistical software package. The course covers basic data wrangling that is necessary for data analysis. It uses examples from the healthcare industry. This course focuses on the use of statistical methods although there may be some discussion of the mathematical underpinnings and relevant formulae and assumptions necessary for understanding the application of statistical methods.\u003c/p\u003e\n\u003cp\u003eThis self-paced course is comprised of written content, video content, step-by-step follow-along activities, and assessments to reinforce your learning (Assessments available to Verified Track learners only).\u003c/p\u003e\n\u003cp\u003eThe course is comprised of 5 modules that you should complete in order, as each subsequent module builds on the previous one.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eModule 1: Descriptive Statistics and Data Distributions\u003c/li\u003e\n\u003cli\u003eModule 2: Sampling Distribution and Hypothesis Tests\u003c/li\u003e\n\u003cli\u003eModule 3: Visualize and Summarize Data in R\u003c/li\u003e\n\u003cli\u003eModule 4: Independent and Paired Sample t-tests\u003c/li\u003e\n\u003cli\u003eModule 5: ANOVA\u003c/li\u003e\n\u003c/ul\u003e361:T513,\u003cp\u003eThe 'Statistical and Probabilistic Foundations of AI' course provides an accessible overview of the mathematics and statistics behind fundamental concepts of machine learning, data science, and artificial intelligence. \u003c/p\u003e\n\u003cp\u003eIt covers descriptive and exploratory data analysis and a brief "])</script><script>self.__next_f.push([1,"introduction to inferential statistics. Starting with summary statistics, it focuses on visualising data and the resulting key characteristics. This includes box plots, histograms, kernel density estimates, and regression. In addition, the course provides the principles of probability necessary to understand the methods used in inferential statistics and machine learning at an introductory level. Starting with the basic concepts of probability and elementary stochastic models, the course also covers more advanced topics of probability theory. These include multivariate distributions, generating functions, limit theorems, and a brief introduction to stochastic simulation. \u003c/p\u003e\n\u003cp\u003eFinally, a brief introduction to inferential statistics is given. Parametric and non-parametric inferential approaches are discussed. Point and interval estimation and hypothesis testing are also covered.\u003c/p\u003e\n\u003cp\u003eThe presentation is rounded off with many examples and data that are analysed and visualised using R.\u003c/p\u003e362:T526,\u003cp\u003eThose enrolled in Probability for Actuaries: Introduction to Discrete Distributions will learn to:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDescribe basic data types\u003c/li\u003e\n\u003cli\u003eDescribe the central tendency measures of datasets: mean, median and mode\u003c/li\u003e\n\u003cli\u003eDescribe the dispersion measures of datasets: range, percentiles and variance\u003c/li\u003e\n\u003cli\u003eDescribe basic probability concepts including sample space, events and set operations\u003c/li\u003e\n\u003cli\u003eCalculate probabilities for simple discrete events\u003c/li\u003e\n\u003cli\u003eDifferentiate between a discrete and continuous random variable\u003c/li\u003e\n\u003cli\u003eDescribe Bayes Theorem, conditional probability, law of total probability and statistical independence\u003c/li\u003e\n\u003cli\u003eDescribe and use a probability mass function, probability density function and cumulative distribution function\u003c/li\u003e\n\u003cli\u003eDescribe and calculate the mathematical expectation of a random variable\u003c/li\u003e\n\u003cli\u003eDescribe the features and application of the following discrete distributions: Uniform, Binomial, Poisson\u003c/li\u003e\n\u003cli\u003eCalculate probabilities for random variables governed by"])</script><script>self.__next_f.push([1," a Uniform, Binomial or Poisson distribution\u003c/li\u003e\n\u003cli\u003eDescribe the features and application of the following discrete distributions: Geometric, Negative Binomial\u003c/li\u003e\n\u003cli\u003eCalculate probabilities for random variables governed by a Geometric or Negative Binomial distribution\u003c/li\u003e\n\u003c/ul\u003e363:T450,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eData science tools have revolutionized the way farmers and agricultural professionals approach their work. Python data analysis tools, such as pandas and seaborn, enable farmers to make data-driven decisions using soil, water, and economic data accounts. Pandas is a Python library used to simplify handling large sets of data. Seaborn is a data visualization library used to quickly create graphs.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis hands-on guided project will prepare you to handle agricultural datasets using these Python tools. Y\u003cspan lang=\"EN-US\"\u003eou will develop job-ready skills, like how to download, prepare, analyze, and visualize data using Python libraries, including pandas and seaborn. You will learn how to build a trend line in order to forecast future trends, and finally, you will learn how to create interactive maps which show data change over time.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eYou will be provided with access to a Cloud-based IDE, which has all of the required software, including Python, pre-installed. All you need is a recent version of a modern web browser to complete this project.\u003c/p\u003e364:T820,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThe world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic modeling and the related field of statistical inference are the keys to analyzing data and making scientifically sound predictions.\u003c/p\u003e\n\u003cp\u003eProbabilistic models use the language of mathematics. But instead of relying on the traditional \"theorem-proof\" format, we develop the material in an intuitive -- but still rigorous and mathematically-precise -- manner. Furthermore, while the applications are multiple and evident, we emphasize the basic concepts and methodologies that are universally applicable.\u003c/p\u003e\n\u003cp\u003eThe course covers all of the basic probability concepts, including:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003emultiple discrete or continuous random variables, expectations, and conditional distributions\u003c/li\u003e\n\u003cli\u003elaws of large numbers\u003c/li\u003e\n\u003cli\u003ethe main tools of Bayesian inference methods\u003c/li\u003e\n\u003cli\u003ean introduction to random processes (Poisson processes and Markov chains)\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe contents of this courseare heavily based upon the corresponding MIT class -- \u003cem\u003eIntroduction to Probability\u003c/em\u003e -- a course that has been offered and continuously refined over more than 50 years. It is a challenging class but will enable you to apply the tools of probability theory to real-world applications or to your research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis course is part of the\u003ca href=\"https://micromasters.mit.edu/ds/\"\u003eMITx MicroMasters Program in Statistics and Data Science\u003c/a\u003e. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit \u003ca href=\"https://micromasters.mit.edu/ds/\"\u003ehttps://micromasters.mit.edu/ds/\u003c/a\u003e.\u003c/strong\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"365:T743,\u003cp\u003e\u003cem\u003eIf you have specific questions about this course, please contact us at\u003c/em\u003e \u003cem\u003e\u003ca href=\"mailto:sds-mm@mit.edu\"\u003esds-mm@mit.edu\u003c/a\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData science requires multi-disciplinary skills ranging from mathematics, statistics, machine learning, problem solving to programming, visualization, and communication skills. In this course, learners will combine these foundational and practical skills with domain knowledge to ask and answer questions using real data.\u003c/p\u003e\n\u003cp\u003eThis course will start with a review of common statistical and computational tools such as hypothesis testing, regression, and gradient descent methods. Then, learners will study common models and methods to analyze specific types of data in four different domain areas:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEpigenetic Codes and Data Visualization\u003c/li\u003e\n\u003cli\u003eCriminal Networks and Network Analysis\u003c/li\u003e\n\u003cli\u003ePrices, Economics and Time Series\u003c/li\u003e\n\u003cli\u003eEnvironmental Data and Spatial Statistics\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLearners will be guided to analyze a real data set from each of these areas of focus, and present their findings in written reports. They will also discuss relevant and practical issues with peers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis course is part of the MITx MicroMasters Program in Statistics and Data Science. It is at a similar pace and level of rigor as an on-campus course at MIT. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit\u003c/strong\u003e \u003cstrong\u003e\u003ca href=\"https://micromasters.mit.edu/ds/\"\u003ehttps://micromasters.mit.edu/ds/\u003c/a\u003e.\u003c/strong\u003e\u003c/p\u003e366:Tb12,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cem\u003eIf you have specific questions about this course, please contact us at\u003ca href=\"mailto:sds-mm@mit.edu\" rel=\"noopener\" target=\"_blank\"\u003esds-mm@mit.edu\u003c/a\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA time series is a time-stamped set of noisy observations from an underlying process that evolves over time. These observations are dependent on each other in a particular, unknown, fashion. Examples of such series include stock values, value of a currency with respect to the dollar, mean housing prices, the number of Covid-19 infections, or the pitch angle of an airplane during flights. Modeling such processes for the purpose of prediction or intervention is a fundamental problem in statistical learning.\u003c/p\u003e\n\u003cp\u003eThis graduate-level course that will address three lines of development:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLearning Structured Models\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e In this module, we focus on learning the underlying stochastic dynamic model that generates the data. We discuss how algorithms depend on the underlying class of models adopted for this learning. We address the accuracy and reliability of our learned models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrediction:\u003c/strong\u003e In this module, we make no assumptions on how the data is generated and focus on predicting the next outcome of the process based on past observations. In this context, we analyze \u003cem\u003eMatrix and Tensor Completion Methods\u003c/em\u003e in providing such predictions and we analyze the accuracy of these prediction in the presence of noise, missing data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOptimal Intervention and Reinforcement Learning (RL):\u003c/strong\u003e A key ingredient of RL is a simulator that can estimate the value of a reward for a given intervention. In this module course, we build on techniques from RL as well as the first two parts to show how new intervention/control can be derived with better outcomes.\u003c/p\u003e\n\u003cp\u003eThis course will consist of three hands-on projects, in which learners will apply knowledge gained in lectures, build models and implement algorithms to solve problems posed on real time series data sets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis course is part of the\u003ca href=\"https://micromasters.mit.edu/ds/\" rel=\"noopener\" target=\"_blank\"\u003eMITx MicroMasters Program in Statistics and Data Science\u003c/a\u003e. \u003c/strong\u003e\u003cstrong\u003eMaster the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit\u003ca href=\"https://micromasters.mit.edu/ds/\" rel=\"noopener\" target=\"_blank\"\u003ehttps://micromasters.mit.edu/ds/\u003c/a\u003e.\u003cbr /\u003e\n\u003c/strong\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"367:Ta05,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis course concentrates on recognizing and solving convex optimization problems that arise in applications. The syllabus includes: convex sets, functions, and optimization problems; basics of convex analysis; least-squares, linear and quadratic programs, semidefinite programming, minimax, extremal volume, and other problems; optimality conditions, duality theory, theorems of alternative, and applications; interior-point methods; applications to signal processing, statistics and machine learning, control and mechanical engineering, digital and analog circuit design, and finance.\u003c/p\u003e\n\u003cp\u003eThis course should benefit anyone who uses or will use scientific computing or optimization in engineering or related work (e.g., machine learning, finance). More specifically, people from the following fields: Electrical Engineering (especially areas like signal and image processing, communications, control, EDA \u0026amp; CAD); Aero \u0026amp; Astro (control, navigation, design), Mechanical \u0026amp; Civil Engineering (especially robotics, control, structural analysis, optimization, design); Computer Science (especially machine learning, robotics, computer graphics, algorithms \u0026amp; complexity, computational geometry); Operations Research; Scientific Computing and Computational Mathematics. The course may be useful to students and researchers in several other fields as well: Mathematics, Statistics, Finance, Economics.\u003c/p\u003e\n\u003cp\u003eAdditional Instructors / Contributors\u003c/p\u003e\n\u003cp\u003eNeal Parikh\u003c/p\u003e\n\u003cp\u003eNeal Parikh is a 5th year Ph.D. Candidate in Computer Science at Stanford University. He has previously taught Convex Optimization (EE 364A) at Stanford University and holds a B.A.S., summa cum laude, in Mathematics and Computer Science from the University of Pennsylvania and an M.S. in Computer Science from Stanford University.\u003c/p\u003e\n\u003cp\u003eErnest Ryu\u003c/p\u003e\n\u003cp\u003eErnest Ryu is a PhD candidate in Computational and Mathematical Engineering at Stanford University. He has served as a TA for EE364a at Stanford. His research interested include stochastic optimization, convex analysis, and scientific computing.\u003c/p\u003e\n\u003cp\u003eMadeleine Udell\u003c/p\u003e\n\u003cp\u003eMadeleine Udell is a PhD candidate in Computational and Mathematical Engineering at Stanford University. She has served as a TA and as an instructor for EE364a at Stanford. Her research applies convex optimization techniques to a variety of non-convex applications, including sigmoidal programming, biconvex optimization, and structured reinforcement learning problems, with applications to political science, biology, and operations research.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"368:T556,\u003cp\u003eDecision makers often struggle with questions such as: What should be the right price for a product? Which customer is likely to default in his/her loan repayment? Which products should be recommended to an existing customer? Finding right answers to these questions can be challenging yet rewarding.\u003c/p\u003e\r\n\u003cp\u003ePredictive analytics is emerging as a competitive strategy across many business sectors and can set apart high performing companies. It aims to predict the probability of the occurrence of a future event such as customer churn, loan defaults, and stock market fluctuations – leading to effective business management.\u003c/p\u003e\r\n\u003cp\u003eModels such as multiple linear regression, logistic regression, auto-regressive integrated moving average (ARIMA), decision trees, and neural networks are frequently used in solving predictive analytics problems. Regression models help us understand the relationships among these variables and how their relationships can be exploited to make decisions.\u003c/p\u003e\r\n\u003cp\u003eThis course is suitable for students/practitioners interested in improving their knowledge in the field of predictive analytics. The course will also prepare the learner for a career in the field of data analytics. If you are in the quest for the right competitive strategy to make companies successful, then join us to master the tools of predictive analytics.\u003c/p\u003e369:T750,\u003cp\u003eData Science along with artificial intelligence (AI) and its various components such as statistical learning (SL), machine learning (ML) and deep learning algorithms (DL) are recognized as main drivers of organizational value creation. According to Dr Jim Gray, Data Science is the fourth paradigm which drives innovative solutions to organizational problems.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIn this course we will start with basic concepts in probability such as joint and conditional probabilities. We will discuss the implementation of these concepts in ML algorithms for Market Basket Analysis and Recommender Systems. After covering basic probability conc"])</script><script>self.__next_f.push([1,"epts, we move on to random variables, discrete and continuous probability distributions, sampling, estimation and central limit theorem.\u003c/p\u003e\n\u003cp\u003eAn important step in ML model building is feature selection to avoid overfitting and underfitting. ML models such as regression and logistic regression use hypothesis testing to select features. We will discuss various hypothesis tests and how they are used in feature selection. \u003c/p\u003e\n\u003cp\u003eEvery ML model has an optimization stage, either to fine-tune the feature weights, or to find an optimal set of features. We will discuss important optimization techniques, and algorithms such as Gradient Descent, that play an important role in AI and ML model development.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eData must be represented in a matrix for AI and ML model development. Matrix operations such as matrix inverse and multiplication are elementary steps in model development. These fundamental concepts in linear algebra will be discussed.\u003c/p\u003e\n\u003cp\u003eThis course is suitable for students/practitioners interested in improving their knowledge in the fundamental concepts of Data Science. The course will also prepare the learner for a career in the field of Data Analytics.\u003c/p\u003e36a:T49f,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eData science tools have revolutionized the way farmers and agricultural professionals approach their work. Python data analysis tools, such as pandas and seaborn, enable farmers to make data-driven decisions using soil, water, and economic data accounts. Pandas is a Python library used to simplify handling large sets of data. Seaborn is a data visualization library used to quickly create graphs. \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis hands-on guided project will prepare you to handle agricultural datasets using these Python tools. Y\u003cspan lang=\"EN-US\"\u003eou will develop job-ready skills, like how to download, prepare, analyze, and visualize data using Python libraries, including pandas and seaborn. You will learn how to build a trend line in order to forecast future tre"])</script><script>self.__next_f.push([1,"nds, and finally, you will learn how to create interactive maps which show data change over time.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eYou will be provided with access to a Cloud-based IDE, which has all of the required software, including Python, pre-installed. All you need is a recent version of a modern web browser to complete this project.\u003c/p\u003e36b:T12a1,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cstrong\u003eTo learn more about this MicroMasters program, please visit\u003ca href=\"https://micromasters.mit.edu/ds/\"\u003ehttps://micromasters.mit.edu/ds/\u003c/a\u003e.\u003cbr /\u003e\n\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis course is an assessment that tests your knowledge on the course content from \u003ca href=\"https://mitx-micromasters.zendesk.com/hc/en-us/articles/360035543812-What-is-14-310x-Data-Analysis-for-Social-Scientists-Is-it-a-prerequisite-for-14-310Fx-Data-Analysis-in-Social-Science-Do-I-have-to-pay-for-it-\"\u003e14.310x - Data Analysis for Social Scientists\u003c/a\u003e. Learners are eligible to take this assessment only if they have passed \u003ca href=\"https://mitx-micromasters.zendesk.com/hc/en-us/articles/360035543812-What-is-14-310x-Data-Analysis-for-Social-Scientists-Is-it-a-prerequisite-for-14-310Fx-Data-Analysis-in-Social-Science-Do-I-have-to-pay-for-it-\"\u003e14.310x - Data Analysis for Social Scientists\u003c/a\u003e. \u003cbr /\u003e\nAll learners who have become eligible within the past year have already been automatically enrolled for this assessment. If you are eligible but have not been enrolled or have not received an email notification, please contact us asap at \u003ca href=\"mailto:sds-mm@mit.edu\"\u003esds-mm@mit.edu\u003c/a\u003e with your edX username, along with a proof (learners record or screenshot of your progress page) that you have obtained 50% or above in \u003ca href=\"https://mitx-micromasters.zendesk.com/hc/en-us/articles/360035543812-What-is-14-310x-Data-Analysis-for-Social-Scientists-Is-it-a-prerequisite-for-14-310Fx-Data-Analysis-in-Social-Science-Do-I-have-to-pay-for-it-\"\u003e14.310x - Data Analysis for Social Scientists\u003c/a\u003e. You must upgrade to become a verified learner to take this assessment.\u003ca href=\"https://mitx-micromasters.zendesk.com/hc/en-us/articles/360035543812-What-is-14-310x-Data-Analysis-for-Social-Scientists-Is-it-a-prerequisite-for-14-310Fx-Data-Analysis-in-Social-Science-Do-I-have-to-pay-for-it-\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e14.310x - Data Analysis for Social Scientists is a statistics and data analysis course that will introduce you to the essential notions of probability and statistics. It will cover techniques in modern data analysis: estimation, regression and econometrics, prediction, experimental design, randomized control trials (and A/B testing), machine learning, and data visualization. It will illustrate these concepts with applications drawn from real world examples and frontier research. Finally, it will provide instruction for how to use the statistical package R and opportunities for students to perform self-directed empirical analyses.\u003c/p\u003e\n\u003cp\u003eThis assessment course should only be taken by learners who have completed and passed \u003cem\u003e\u003ca href=\"https://mitx-micromasters.zendesk.com/hc/en-us/articles/360035543812-What-is-14-310x-Data-Analysis-for-Social-Scientists-Is-it-a-prerequisite-for-14-310Fx-Data-Analysis-in-Social-Science-Do-I-have-to-pay-for-it-\"\u003e14.310x - Data Analysis for Social Scientists\u003c/a\u003e\u003c/em\u003e and intend to pursue the MicroMasters credential in Statistics and Data Science. To get credit in this MicroMasters program:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eEnroll in both this assessment course and the content course \u003cem\u003e\u003ca href=\"https://mitx-micromasters.zendesk.com/hc/en-us/articles/360035543812-What-is-14-310x-Data-Analysis-for-Social-Scientists-Is-it-a-prerequisite-for-14-310Fx-Data-Analysis-in-Social-Science-Do-I-have-to-pay-for-it-\"\u003e14.310x - Data Analysis for Social Scientists\u003c/a\u003e\u003c/em\u003e (Note: There is no additional fee to enroll in the content course),\u003c/li\u003e\n\u003cli\u003eComplete the content course 14.310x with a passing grade,\u003c/li\u003e\n\u003cli\u003eCome back to this course and take the exam to earn your verified certificate that will count toward the MicroMasters credential in Statistics and Data Science.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eThis assessment course, along with the content course \u003cem\u003e\u003ca href=\"https://mitx-micromasters.zendesk.com/hc/en-us/articles/360035543812-What-is-14-310x-Data-Analysis-for-Social-Scientists-Is-it-a-prerequisite-for-14-310Fx-Data-Analysis-in-Social-Science-Do-I-have-to-pay-for-it-\"\u003e14.310x - Data Analysis for Social Scientists\u003c/a\u003e\u003c/em\u003e , is part of the \u003ca href=\"https://www.edx.org/micromasters/mitx-statistics-and-data-science\"\u003eMITx MicroMasters Program in Statistics and Data Science\u003c/a\u003e. Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities. To learn more about this program, please visit \u003ca href=\"https://micromasters.mit.edu/ds/\"\u003ehttps://micromasters.mit.edu/ds/\u003c/a\u003e.\u003c/strong\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"36c: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\u003e36d:Ta33,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIn today's work environment, it is vital for professionals to interpret the large amount of quantitative information to which they have access, more and more we have to interpret large volumes of data (big data), either by analyzing financial reports, case studies or presenting data effectively. This Professional Certification program, offered by EGADE Business School, the number one school in Mexico and Latin America in Finance and Administration, will give you the opportunity to successfully develop these skills that are highly required by companies in today's job market.\u003c/p\u003e\r\n \r\n\u003cp\u003eWith the help of experts, you will use interactive exercises to learn the fundamentals of mathematics, statististical analysiscs, data science and finance, all applied to business.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe topics covered in this Professional Certification program will allow you to obtain theoretical and practical training at the master's or postgraduate level to better understand the functions of business management; additionally, it will prepare you to continue your studies successfully by deciding to pursue a Master's Degree in Finance or an MBA at EGADE.\u003c/p\u003e\r\n\r\n\u003cp\u003eYou will need prior experience using Microsoft Excel.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program will provide you with the key concepts you need to make successful managerial decision makings in your company, both in the short term and in the long term. For example, to make financial decisions, monetary decisions or investment decisions, it is necessary to know the interest rate, opportunity costs, master financial models, know the capital budget and working capital, the value of money, risk management.analyze risk. In general, this program will give you the quantitative skills and problem solving skills you need professionally to be successful in real world making business decisions.\u003c/p\u003e\r\n\r\n\u003cp\u003eQuantitative skills like financial modeling give you a framework for creating new tools to compare investment options and determine which will yield the best return based on a set of inputs and assumptions.Corporate finance can help the company in the creation of value, to analyze its financial resources / equity, to know the real assets that the company has, or to select the best investment projects based on the current value of money, and to know the analysis used in the processes.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe program will also cover data science, which helps in making successful operational decisions and managerial decisions based on data analysis, for example, taking into account the information provided by the data you can determine the efficient use of resources.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"36e:T4ee,\u003cp\u003eTechnological advances have transformed fields that rely on data by providing a wealth of information ready to be analyzed. From working with single genes to comparing entire genomes, biomedical research groups around the world are producing more data than they can handle and the ability to interpret this information is a key skill for any practitioner. The skills necessary to work with these massive datasets are in high demand, and this series will help you learn those skills.\u003c/p\u003e\r\n\r\n\u003cp\u003eUsing the open-source R programming language, you’ll gain a nuanced understanding of the tools required to work with complex life sciences and genomics data. You’ll learn the mathematical concepts — and the data analytics techniques — that you need to drive data-driven research. From a strong foundation in statistics to specialized R programming skills, this series will lead you through the data analytics landscape step-by-step.\u003c/p\u003e\r\n\r\n\u003cp\u003eTaught by Rafael Irizarry from the Harvard T.H. Chan School of Public Health, these courses will enable new discoveries and will help you improve individual and population health. If you’re working in the life sciences and want to learn how to analyze data, enroll now to take your research to the next level.\u003c/p\u003e36f: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, Matplo"])</script><script>self.__next_f.push([1,"tlib, 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\u003e370:T694,\u003cp\u003eEvery single minute, computers across the world collect millions of gigabytes of data. What can you do to make sense of this mountain of data? How do data scientists use this data for the applications that power our modern world?\u003c/p\u003e\n\u003cp\u003eData science is an ever-evolving field, using algorithms and scientific methods to parse complex data sets. Data scientists use a range of programming languages, such as Python and R, to harness and analyze data. This course focuses on using Python in data science. By the end of the course, you’ll have a fundamental understanding of machine learning models and basic concepts around Machine Learning (ML) and Artificial Intelligence (AI).\u003c/p\u003e\n\u003cp\u003eUsing Python, learners will study regression models (Linear, Multilinear, and Polynomial) and classification models (kNN, Logistic), utilizing popular libraries such as sklearn, Pandas, matplotlib, and numPy. The course will cover key concepts of machine learning such as: picking the right complexity, preventing overfitting, regularization, assessing uncertainty, weighing trade-offs, and model evaluation. Participation in this course will build your confidence in using Python, preparing you for more advanced study in Machine Learning (ML) and Artificial Intelligence (AI), and advancement in your career.\u003c/p\u003e\n\u003cp\u003eLearners must have a minimum baseline of programming knowledge (preferably in Python) and statistics in order to be successful in this course. Python prerequisites can be met with an introductory Python course offered through CS50’s Introduction to Programming with Python, and statistics prerequis"])</script><script>self.__next_f.push([1,"ites can be met via Fat Chance or with Stat110 offered through HarvardX.\u003c/p\u003e371:T42b,\u003cp\u003eStatistical inference and modeling are indispensable for analyzing data affected by chance, and thus essential for data scientists. In this course, you will learn these key concepts through a motivating case study on election forecasting. \u003c/p\u003e\n\u003cp\u003eThis course will show you how inference and modeling can be applied to develop the statistical approaches that make polls an effective tool and we'll show you how to do this using R. You will learn concepts necessary to define estimates and margins of errors and learn how you can use these to make predictions relatively well and also provide an estimate of the precision of your forecast. \u003c/p\u003e\n\u003cp\u003eOnce you learn this you will be able to understand two concepts that are ubiquitous in data science: confidence intervals, and p-values. Then, to understand statements about the probability of a candidate winning, you will learn about Bayesian modeling. Finally, at the end of the course, we will put it all together to recreate a simplified version of an election forecast model and apply it to the 2016 election.\u003c/p\u003e372:T9cc,"])</script><script>self.__next_f.push([1,"\u003cp\u003eNote: Learners who successfully complete this MathWorks course can earn a Digital Credential — a visual representation of a verified achievement that can be issued, accessed, and displayed online. Enroll to learn more, complete the course, and claim your badge!\u003c/p\u003e\n\u003cp\u003eExpand your data analysis and modeling skills in MATLAB, a programming and numeric computing platform used to analyze data, develop algorithms, and create models. Millions of engineers and scientists worldwide use MATLAB to study and build advanced applications in machine learning, deep learning, signal processing, communications, image processing, and control systems. They are shaping the future by modeling rockets that may someday take you into space, developing autonomous vehicles to travel safely and efficiently, and designing wave farms that harness the power of ocean waves to generate clean energy.\u003c/p\u003e\n\u003cp\u003eIn this course, you'll use MATLAB to examine real-world problems and answer questions like:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eHow far does a blue whale swim each day?\u003c/li\u003e\n\u003cli\u003eWhat is the favorite topping in a pizza shop?\u003c/li\u003e\n\u003cli\u003eWhat is the ride quality of a car suspension?\u003c/li\u003e\n\u003cli\u003eHow does the magnitude of an earthquake impact the strength of a tsunami?\u003c/li\u003e\n\u003cli\u003eWhat is the most expensive failure in a factory?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eMATLAB makes it easy to see results quickly, so there are no pre-requisites for the course. Whether you're auditing or a verified learner, you will have free access to MATLAB for the duration of the course. You will learn how to process, analyze, and visualize data collected nearly everywhere in today's digital workplace. You'll use powerful templates and auto-generated code to start experimenting immediately and quickly process similar data sets. And you'll gain the essential programming skills needed to perform these exciting tasks.\u003c/p\u003e\n\u003cp\u003eThroughout the course, you'll have ample opportunities to practice your newly acquired skills – through auto-graded assignments, practice quizzes, interactive readings, and projects. By the end of the course, you'll be ready to analyze your own data sets and impress colleagues with word clouds, geographic plots, animations, and more.\u003c/p\u003e\n\u003cp\u003eAdditionally, this course will give you the skills you need to prepare for the \u003ca href=\"https://www.mathworks.com/certification\"\u003eMathWorks Certified MATLAB Associate exam\u003c/a\u003e. 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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 d"])</script><script>self.__next_f.push([1,"atabase 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\u003e387: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, including 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 "])</script><script>self.__next_f.push([1,"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\u003e388: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,"389: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,"38a: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,"38b:T998,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThe employment outlook for software engineers and full stack developers is projected to grow by 25% per year to 2032 (U.S. Bureau of Labor Statistics). This comprehensive program is designed to give you the skills you need to launch your full stack software development career in just 6-8 months. No prior programming experience or degree is required.\u003c/p\u003e \r\n\r\n\u003cp\u003eWhat do full stack developers do? Full stack developers contribute significantly to business success. Their valuable mix of frontend and backend programming expertize ensures that an engaging user interface integrates effectively with dynamic server-side functionalities to produce a winning web solution. If you’re a versatile professional looking to reskill in tech, or a creative student/graduate keen to kick start an IT career, this IBM Full Stack Developer Professional Certificate is your one-stop route to getting started in this field.\u003c/p\u003e\r\n \r\n\u003cp\u003eWith the support of IBM experts, you’ll learn the tools and technologies that successful software and web developers use to build, deploy, test, run and manage full stack cloud native applications. And you’ll dive into building cloud-based applications, completing hands-on labs, and completing projects that build job-ready skills employers need.\u003c/p\u003e\r\n \r\n\u003cp\u003eFrom the start, you’ll be immersed in technical topics such as cloud foundations, HTML, CSS, JavaScript, cloud native practices, CI/CD, containers, Docker, Kubernetes, OpenShift, Istio, Python programming, databases, SQL, Django, Bootstrap, application security, microservices, serverless computing and more. Plus, you'll explore key topics like GitHub, Node.js, React, and DevOps, building skills and knowledge that will set you up for success in software engineering.\u003c/p\u003e\r\n\r\n\u003cp\u003eAs you learn, you'll get valuable practical experience through hands-on labs and projects. And you’ll complete a final project where you will create a cloud environment using IBM Cloud to build and deploy an application consisting of multiple microservices using CI/CD. This will give you plenty to talk about in interviews to demonstrate your practical proficiency in applying various cloud native tools and technologies.\u003c/p\u003e\r\n\r\n\u003cp\u003eIf you’re looking to build a rewarding career in full stack development, this IBM Full Stack Developer Professional Certificate will get you job-ready and give you the skills you need for a resume that will open up rewarding career opportunities.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"38c:T574,\u003cp\u003eThe demand for data scientists is projected to grow 10x faster than other occupations (Source: US Bureau of Labor Statistics). This IBM Data Science Professional Certificate gives you the job-ready skills and practical experience you need to start your career in data science and machine learning. No prior computer science or programming experience is required.\u003c/p\u003e\r\n\r\n\u003cp\u003eData scientists analyze and interpret complex, large datasets using data mining, machine learning, and predictive modeling techniques. They then seek to uncover patterns, trends, and insights that help businesses make informed decisions.\u003c/P\u003e\r\n\r\n\u003cp\u003eDuring this program, you’ll learn Python programming, SQL for database querying, data manipulation with Pandas and Numpy, data visualization with Matplotlib and Seaborn, and machine learning with Scikit-learn. You’ll work hands-on with data science tools like Jupyter Notebooks, RStudio, and IBM watsonx. You'll use GitHub for version control and access data sources with APIs. Plus, you’ll gain valuable practical skills through hands-on labs, course projects, and a capstone project you can put on your resume and talk about in interviews.\u003c/p\u003e\r\n\r\n\u003cp\u003eIf you’re looking to get started in data science, this program gives you the job-ready skills you need to catch the eye of an employer. Enroll today and look forward to kickstarting a highly rewarding career.\u003c/p\u003e38d:T8c8,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis Professional Certificate program is intended for anyone who is seeking to develop the job-ready skills, tools, and portfolio for an entry-level data analyst or data scientist position. No prior knowledge of R, or programming is required to get you started!\u003c/p\u003e\r\n\r\n\u003cp\u003eIn this Data Analytics and Visualization with Excel and R Professional Certificate Program, you will dive into the role of a data analyst or data scientist and develop the essential skills you need work with a range of data sources and apply powerful tools, including Excel, Cognos Analytics, and the R programming language (including: ggplot2, Leaflet and R Shiny), towards becoming a data driven practitioner, and gaining a competitive edge in the job market.\u003c/p\u003e\r\n \r\n\u003cp\u003eBy the end of this program, you will be able to explain the data analyst and data scientist roles. Skills you will developer and tools you will be exposed to in this program include:\r\n\u003cul\u003e\r\n\u003cli\u003eExcel spreadsheets to create charts and plots.\u003c/li\u003e\r\n\u003cli\u003eCognos Analytics to create interactive dashboards.\u003c/li\u003e\r\n\u003cli\u003eRelational databases and query data using SQL statements.\u003c/li\u003e \r\n\u003cli\u003eR programming language to complete the entire data analysis process - including data preparation, statistical analysis, data visualization, predictive modeling, and creating interactive data applications.\u003c/li\u003e \r\n\u003cli\u003eVarious methods to communicate your data findings and learn to prepare a report for stakeholders.\r\nThis program is suitable for anyone with a passion for learning and does not require any prior data analysis, statistics, or programming experience.\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003e\u003cb\u003eApplied Learning Project\u003c/b\u003e\u003cbr\u003e\r\nThroughout this Professional Certificate, you will also complete hands-on labs and projects to help you gain practical experience with Excel, Cognos Analytics, SQL, and the R programing language and related libraries for data science, including Tidyverse, Tidymodels, R Shiny, ggplot2, Leaflet, and rvest.\u003c/p\u003e\r\n\r\n\u003cp\u003eIn the final course in this Professional Certificate, you will complete a capstone project that applies what you have learned to a challenge that requires data collection, analysis, basic hypothesis testing, visualization, and modelling to be performed on real-world datasets.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"38e:Ta6d,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThe US Bureau of Labor Statistics projects a 23% increase in cybersecurity jobs to 2033. The demand for cybersecurity analysts is growing ever stronger!\r\nWhat do cybersecurity analysts do? Cybersecurity analysts protect an organization's digital assets by identifying, analyzing, and mitigating security threats, ensuring data and systems remain secure against cyberattacks. \r\n \r\n\u003cp\u003eThis IBM Cybersecurity Professional Certificate has been designed by industry experts to provide you with the job-ready cybersecurity and digital forensic skills you need to jumpstart your cybersecurity career. No prior cybersecurity experience is required. \r\n\r\n\u003cp\u003eTo begin, you’ll build your foundational knowledge of cybersecurity essentials, tools, and technologies, operating systems, networking fundamentals, and the basics of cyberattacks. You’ll explore database fundamentals and vulnerabilities and learn about the steps a cybersecurity analyst would take to prevent, manage, and mitigate cybersecurity attacks. Plus, you’ll learn about cybersecurity architecture, compliance frameworks, standards, and regulations. \r\n\r\n\u003cp\u003eYou’ll also explore gen AI for cybersecurity and build valuable skills and hands-on experience in penetration testing, incident response, forensics, and threat intelligence. You’ll work on real-world projects developing cybersecurity plans and compliance frameworks, giving you great practical work to talk about in interviews. Plus, you’ll use real-world case studies that demonstrate that you can: \r\n\u003cul\u003e\r\n\u003cli\u003eApply incident response methodologies and forensics practices.\u003c/li\u003e \r\n\u003cli\u003eUse industry-specific security tools.\u003c/li\u003e \r\n\u003cli\u003eApply cybersecurity industry standards and best practices that mitigate risks, enhance security, and ensure compliance through audit processes.\u003c/li\u003e \r\n\u003cli\u003eUse gen AI tools to boost cybersecurity effectiveness and productivity.\u003c/li\u003e \r\n\u003cli\u003eInvestigate a real-world security breach by identifying the attack, vulnerabilities, costs, and prevention.\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e \r\n\r\n\u003cp\u003eTake advantage of the insights provided by seasoned industry experts to guide your job search, resume creation, and Interviewing to help you land your first cybersecurity role. This program will also prepare you to take and earn a 30% discount on the CompTIA Security+ and CySA+ certification exams. After successfully completing this program, you’ll have an industry-recognized IBM Professional Certificate that you can add to your resume and portfolio.\u003c/p\u003e\r\n\r\n\u003cp\u003eIf you’re looking to kickstart your cybersecurity career as a sought-after analyst, ENROLL TODAY, and look forward to being job-ready in less than 6 months!\u003c/p\u003e"])</script><script>self.__next_f.push([1,"38f:T687,\u003cp\u003eNow is a great time to launch a rewarding career in Information Technology (IT) support - no experience or degree is required to get started. The US Bureau of Labor Statistics forecasts a 9% growth in jobs through 2030, averaging 70,000 openings each year, and a medium salary of $58,000 annually for an entry-level Computer Support Specialist. With over 400,000 US job openings, Computer Tech Support Specialists are in high demand.\u003c/p\u003e \r\n \r\n\u003cp\u003eIn this self-paced, certificate program for beginners, you will learn IT support and build competency in IT fundamentals topics, including hardware, operating systems, software, system administration, programming, databases, networking, cybersecurity, and cloud computing, as well as critical skills covered such as customer service and troubleshooting. Mastery of these skills is essential for IT Helpdesk Support and also provides multiple options to grow your career as they are required skills for many technology jobs, including Software Engineer, Data Analyst, and Data Scientist.\u003c/p\u003e\r\n \r\n\u003cp\u003eThis Professional Certificate program from IBM is built by experts to prepare you for an entry-level job in Technical Support. If you can dedicate a few hours per week, you can complete the program in 3 to 6 months. By the end of the program, you will be equipped with job-ready skills employers look for, whether you are just starting out your IT career or changing jobs.\u003c/p\u003e\r\n\r\n\u003cp\u003eWhen you successfully complete the program you’ll receive dual credentials, IBM Digital Badges for each course to help your profile stand out, as well as a Professional Certificate to showcase your job readiness to potential employers.\u003c/p\u003e390:Tb86,"])</script><script>self.__next_f.push([1,"\u003cp\u003eLearn how to drive quality and productivity projects by mastering the methods, tools and principles of Lean Six Sigma.\u003c/p\u003e\r\n \r\n\u003cp\u003eLean Six Sigma enable organizations to identify weaknesses of their business processes, to measure, analyze and improve their performance, and thus to sustainably increase customer satisfaction and decrease costs.\u003c/p\u003e\r\n \r\n\u003cp\u003eIn this program, you will first earn the TUM Lean Six Sigma Yellow Belt Certification by mastering the fundamentals of the Lean Six Sigma methodology, plus the Green Belt Certification by applying your knowledge in practice with a predefined project.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe Body of Knowledge of the first three courses in the series (QPLS1-3x) reflects the standard of the American Society for Quality for their Certified Six Sigma Green Belt. In these courses, you will learn the DMAIC (Define, Measure, Analyze, Improve, Control) process improvement cycle and examine how the principles of Lean Production improve quality and productivity and enable organizational transformation. Both descriptive and inferential statistics will be applied, and all concepts are exercised using online problems and interactive exercises and case studies.\u003c/p\u003e\r\n\r\n\u003cp\u003eAfter receiving our Yellow Belt Certification, learners apply their knowledge in practice to earn the TUM Lean Six Sigma Green Belt Certification (QPLS4x), according the recommendation of the International Society of Six Sigma Professionals, by implementing our predefined project on environmental littering: Improve the cleanliness of areas around selected places in your hometown and control the sustainability of your measures.\u003c/p\u003e\r\n \r\n\u003cp\u003eWe will guide you using software with all relevant tools, specific tasks, a project storybook template to document your results, graded reviews of your documentation after each DMAIC phase, and weekly live online sessions for your questions. The Green Belt Certification will be awarded after completion of the course and final review of your completed project storybook and your collected data.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis TUM Lean Six Sigma Yellow and Green Belt certifications allow you to demonstrate your personal operational excellence with your project story book to actively reduce costs and increase customer satisfaction in any organizational environment. Your new skills can facilitate your career entry or advance your career.\u003c/p\u003e\r\n\r\n\u003cp\u003eNOTE: In order to achieve the TUM Lean Six Sigma Yellow Belt Certification, it is MANDATORY to complete the 3 courses: QPLS1x, QPLS2x and QPLS3x pursuing all 3 Verified Certificates, for which you will earn the Yellow Belt Certificate. Only then you may start your Green Belt Certification project with QPLS4x. There is no deadline for completing the courses and project.\u003c/p\u003e\r\n\r\n\r\n\u003chtml\u003e\r\n\u003cbody\u003e\r\n\r\n\u003cimg src=\"https://images.ctfassets.net/ii9ehdcj88bc/3kcIO09KMO9V3pLcmSgu5Z/89b3cdc9508a500d358329034d57268d/edxprize2022.png?h=250\" alt=\"edX Prize 2022\" \r\n\r\n\u003c/body\u003e\r\n\u003c/html\u003e"])</script><script>self.__next_f.push([1,"391:Td2a,"])</script><script>self.__next_f.push([1,"\u003cp\u003eLearn how to drive quality and productivity programs by mastering the powerful analytical and management tools of the Six Sigma methodology and the revolutionary concepts of Lean Management.\u003c/p\u003e\r\n\r\n\u003cp\u003eSix Sigma and Lean enable organizations to measure and analyze production processes, to eliminate waste and to evolve their management structures in order to motivate employees and improve quality and productivity.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe series of courses is provided in collaboration with the TUM School of Management Executive Education Center and the Chair of Production and Supply Chain Management.\u003c/p\u003e\r\n\r\n\u003cp\u003eIn this Professional Certificate program, you will learn the fundamentals of the Six Sigma methodology and Lean Manufacturing. You will learn the DMAIC (Define, Measure, Analyze, Improve, Control) process improvement cycle and examine how the principles of Lean production improve quality and productivity and enable organizational transformation.\u003c/p\u003e\r\n\r\n\u003cp\u003eYou will be challenged to learn both the quantitative and qualitative methods associated with Six Sigma and Lean, including:\u003c/p\u003e\r\n\r\n\u003cp\u003e\r\n\u003cul\u003e\u003cli\u003eproblem definition;\u003c/li\u003e\r\n\u003cli\u003ebaseline performance measurement and process capability;\u003c/li\u003e\r\n\u003cli\u003emeasurement system analysis;\u003c/li\u003e\r\n\u003cli\u003eroot cause analysis;\u003c/li\u003e\r\n\u003cli\u003eregression and correlation;\u003c/li\u003e\r\n\u003cli\u003edesign of experiments;\u003c/li\u003e\r\n\u003cli\u003econtrol charts implementation.\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eBoth descriptive and inferential statistics will be applied and all concepts are exercised using online problems and interactive exercises and case studies.\u003c/p\u003e\r\n\r\n\u003cp\u003eA TUM Lean Six Sigma Yellow Belt Certificate will help you advance your career, increase your salary earnings and improve your organization through your mastery of quality skills.\u003c/p\u003e\r\n\r\n\u003cp\u003eUpon successful completion of this program, learners will earn the \u003cb\u003eTUM Lean Six Sigma Yellow Belt certificate, confirming mastery of Lean Six Sigma theory to a Green Belt level\u003c/b\u003e. The Six Sigma material is based on the \u003ca href=\"https://web.archive.org/web/20201108085453/https:/asq.org/\"\u003eAmerican Society for Quality\u003c/a\u003e Six Sigma Body of Knowledge up to a Green Belt level. The Lean Production course material is based on the TUM School of Management’s course in Lean Operations\u003c/p\u003e\r\n\r\n\u003cp\u003eThe Professional Certificate program is designed as preparation for a Lean Six Sigma Green Belt Certification project. If you are interested in obtaining both the TUM Lean Six Sigma Yellow Belt and Green Belt certificates, please go to our \u003ca href=\"https://web.archive.org/web/20201108085453/https:/www.edx.org/professional-certificate/tumx-lean-six-sigma-green-belt-certification\"\u003eSix Sigma: Green Belt Certification Project\u003c/a\u003e.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis TUM Lean Six Sigma Yellow and Green Belt Certification will help you advance your career, increase your salary earnings and improve your organisation through the mastery and application of productivity and quality skills.\u003c/p\u003e\r\n\r\n\u003cp\u003eNOTE: In order to achieve the TUM Lean Six Sigma Yellow Belt Certificate it is MANDATORY to complete all 3 courses mentioned below, pursuing all 3 Verified Certificates. Then, automatically, you will earn the Professional Certificate. Courses can be taken in any order, but since Six Sigma: Analyze, Improve, Control builds on the material learned in Six Sigma: Define and Measure, we recommend you to take Define and Measure first.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"392:T919,"])</script><script>self.__next_f.push([1,"\u003cp\u003eWhether you want to make a strong start to a master’s degree, solidify your knowledge in a professional context or simply brush up on fundamentals in calculus, this program will get you up to speed.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program will enable you to review the relevant topics and offer you an overview of the calculus common in most engineering bachelor’s programs.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe courses in this program provide enough depth to cover the calculus you need to succeed in your engineering master’s or professional work in areas such as structural engineering, integrated product design, machine learning, geomatics and more.\u003c/p\u003e\r\n\r\n\u003cp\u003eIn the first course you will review all the basic concepts and practice and refresh your skills, from functions to differential equations. By the end you will be able to solve a wide range of differential equations.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe second course will review all the basic concepts and you will practice and refresh the skills related to functions of several variables. Topics covered include contour plots, partial and directional derivatives, extreme values, double and triple integrals and common coordinate changes.\u003c/p\u003e\r\n\r\n\u003cp\u003eThese courses are self-paced, self-contained and modular, to make it easier to review specific topics and practice as often as you want without having to follow the entire courses.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program is ideal for:\r\n\u003cul\u003e\r\n\u003cli\u003eProspective engineering students who want to meet the prerequisites for a MSc program, be better prepared or refresh their mathematics knowledge before starting a master’s degree.\u003c/li\u003e\r\n\u003cli\u003eEngineering or bachelor students who realize that they have a gap in their math knowledge or would like an additional challenge in mathematics not offered by their studies.\u003c/li\u003e\r\n\u003cli\u003eWorking professionals who would like to improve their math knowledge.\u003c/li\u003e\r\n\u003cli\u003eAnyone interested in university level mathematics.\u003c/li\u003e\r\n\u003c/u\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program will refresh your knowledge, test your skills and review the relations between the many concepts in calculus. As review courses, you are expected to have previously studied or be familiar with most of the material.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program is part of our series ‘Mastering Mathematics for Engineers’, together with ‘Mastering Linear Algebra’ and ‘Mastering Probability and Statistics’.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"393:T638,\u003cp\u003eDemand for data analysis skills are projected to grow in the U.S. 21% over the next 10 years, over four times the rate of the overall labor market. Fields like Data Science, Data Analytics, and Statistics are expected to grow up to 34%. According to the World Economic Forum, emerging global demand for data analytics skills across occupations is contributing to a “race for talent,” with more jobs available than qualified candidates. According to Burning Glass Technologies research, hybrid, more complex roles which combine field-centric skills with data analysis competencies are up to 40% higher paying than their single-focus counterparts, are high-growth, and immune to the threat of automation.\u003c/p\u003e \r\n\r\n\u003cp\u003eThis Professional Certificate program prepares students, working professionals, and decision makers to become data literate in both their professional and personal lives. In today’s data-driven world, data literacy will transform you into a “data citizen,” allowing you to communicate and make decisions based upon facts with confidence. You will emerge as a champion for a data literate culture.\u003c/p\u003e \r\n\r\n\u003cp\u003eYou will gain an understanding of how using data visualization, big data, data collection, and analytical tools to better understand business challenges and inform the decision-making process.\u003c/p\u003e \r\n\r\n\u003cp\u003eThis program is valuable for students and professionals who want to go beyond data analysis software proficiency to develop the ability to read, write, and communicate using data in context, including an understanding of data sources and constructs.\u003c/p\u003e394:T7a9,\u003cp\u003eMathTrackX is an XSeries Program that has been designed to provide learners with a solid foundation in fundamental mathematics and how it can be applied in the real world. The 6 courses in this program will assist in developing the skills, knowledge and confidence for STEM related career development or preparation for further studies.\u003c/p\u003e \r\n\r\n\u003cp\u003eGuided by experts from the School of Mathematical Sciences and the Maths Learning Centr"])</script><script>self.__next_f.push([1,"e at the University of Adelaide, this self-paced program will equip learners with a comprehensive understanding of the key underpinnings of mathematics, required for further study. Learners will develop skills in communicating mathematical solutions and arguments, and build confidence in solving mathematical problems. Topics covered include methods and applications of differentiation, integration, probability and statistics.\u003c/p\u003e\r\n\r\n\u003cp\u003eUpon successful completion, this program will be accepted as a prerequisite in place of SACE Stage 2 Mathematical Methods at the University of Adelaide. MathTrackX is not a replacement for SACE Year 12 Stage 2 Maths Methods but could be a useful study tool towards completion of this certificate.\u003c/p\u003e\r\n\r\n\u003cp\u003eRecognition of this program may also be applicable for further studies in your state or country. Please contact your accrediting body for any processes that apply if you are hoping to use this program as a bridging course into further education at a University level. \u003c/p\u003e\r\n\r\n\u003cp\u003eLearners that require further assistance with the foundational skills necessary to excel in mathematics may wish to undertake the \u003ca href=\"https://www.edx.org/learn/math/university-of-adelaide-maths-foundations\"\u003eAdelaideX: Maths Foundations\u003c/a\u003e. The Maths Foundations course is not part of the MathTrackX Program, however, it does provide the necessary skills and understanding required for tertiary-level mathematics that may assist learners before enrolling in the MathTrackX XSeries Program.\u003c/p\u003e395:T930,"])</script><script>self.__next_f.push([1,"\u003cp\u003eWhether you want to make a strong start to a master’s degree, solidify your knowledge in a professional context or simply brush up on fundamentals in linear algebra, this program will get you up to speed.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program will enable you to review the relevant topics and offer you an overview of the linear algebra common in most engineering bachelor programs.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe courses in this program provide enough depth to cover the linear algebra you need to succeed in your engineering master’s or professional work in areas such as computer graphics, systems and control, machine learning, quantum computing and more.\u003c/p\u003e\r\n\r\n\u003cp\u003eIn the first course you will review all the basic concepts and practice and refresh the skills related to vectors and linear equations. The course focuses on vectors (from both algebraic and geometric perspectives) and solving linear equations.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe second course will review all the basic concepts and practice and refresh the skills related to matrices and linear transformations. Topics covered include matrix algebra, determinants, eigenvalues and eigenvectors, diagonalization and singular value decomposition.\u003c/p\u003e\r\n\r\n\u003cp\u003eThese courses are self-paced, self-contained and modular, to make it easier to review specific topics and practice as often as you want without having to follow the entire courses.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program is ideal for:\r\n\u003cul\u003e\r\n\u003cli\u003eProspective engineering students who want to meet the prerequisites for a MSc program, be better prepared or refresh their mathematics knowledge before starting a master’s degree.\u003c/li\u003e\r\n\u003cli\u003eEngineering or bachelor students who realize that they have a gap in their math knowledge or would like an additional challenge in mathematics not offered by their studies.\u003c/li\u003e\r\n\u003cli\u003eWorking professionals who would like to improve their math knowledge.\u003c/li\u003e\r\n\u003cli\u003eAnyone interested in university level mathematics.\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program will refresh your knowledge, test your skills and review the relations between the various concepts in linear algebra. As review courses, you are expected to have previously studied or be familiar with most of the material.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program is part of our series ‘Mastering Mathematics for Engineers’, together with ‘Mastering Calculus’ and ‘Mastering Probability and Statistics’.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"396:T49c,\u003cp\u003eGive your career a boost by mastering how to fuse information from a variety of different sensors, such as, radar, lidar and camera, for accurate object positioning and tracking of moving objects.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe target audience for this program are engineers in the automotive industry who need to tackle problems related to perceiving the traffic situation around an autonomous vehicle. This course is also aimed at students with a bachelor's degree who want to pursue master level studies in automotive engineering.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program is derived from master level courses. It starts by introducing the basics of Bayesian statistics and recursive estimation theory and then gradually introduces more advanced concepts. The program offers a unique opportunity to gain practical knowledge in sensor fusion and multi-object tracking algorithms (filters).\u003c/p\u003e \r\n\r\n\u003cp\u003eBy the end of this program, you will be able to contribute to the development of sensor fusion and tracking applications for self-driving vehicles. Most of the involved methods, however, are more general and can be used for surveillance or to track, e.g., biological cells, sports athletes or space debris.\u003c/p\u003e397:T8b0,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIf you have ever wondered what all those economic news and statistics mean to you, how policy changes by the government and central bank may affect your living standard, job or investment, and why things happened in countries far away can still find their way to impact on you here, this is the program for you!\u003c/p\u003e\r\n \r\n\u003cp\u003eThis is an introductory program in Macroeconomics. It focuses on explaining many macroeconomic phenomena that we either observed first hand or heard indirectly from the media. The program considers long-term economic growth in developed and developing economies. You’ll also learn why a national (or global) economy experiences short-term cycles in activity (e.g., recessions and recoveries) around its long-term growth trend.\u003c/p\u003e\r\n \r\n\u003cp\u003eThe behaviour of an economy as a whole is often broadly predictable, but sometimes it can suddenly change due to a range of unpredictable factors such as regional conflicts, financial crises and health emergencies. This program explains what central banks and governments can do to try to smooth these fluctuations and to lift the living standard. Theories or ‘models’ widely used by governments, central banks and the private sector to conduct macroeconomic analyses are explained intuitively as well as discussed critically. The behaviour of the financial system, including the banking system and asset markets, is also considered, especially in the light of financial crises.\u003c/p\u003e\r\n \r\n\u003cp\u003eThe three courses are based but extended on an introductory macroeconomics course taught on campus at The University of Queensland. A focus is placed on core economic principles that are immediately applicable rather than formal mathematical theorisation. A distinctive feature of these courses is their strong emphasis on the international aspect of macroeconomics. Recent global economic and non-economic events have highlighted the importance of understanding the interconnection between the domestic and the international economy.\u003c/p\u003e\r\n \r\n\u003cp\u003eThis program is for everyone, whether you are studying at university, a career professional interested in expanding your economic knowledge, or simply curious about global economic behaviour and what influences it.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"398:T996,"])</script><script>self.__next_f.push([1,"\u003cp\u003eMatrix Algebra underlies many of the current tools for experimental design and the analysis of high-dimensional data. In this introductory online course in data analysis, we will use matrix algebra to represent the linear models that commonly used to model differences between experimental units. We perform statistical inference on these differences. Throughout the course we will use the R programming language to perform matrix operations.\u003c/p\u003e\n\u003cp\u003eGiven the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. You will need to know some basic stats for this course. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.\u003c/p\u003e\n\u003cp\u003eThese courses make up two Professional Certificates and are self-paced:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis for Life Sciences:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistics-and-r\"\u003ePH525.1x: Statistics and R for the Life Sciences\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-linear-models-and-matrix-algebra\"\u003ePH525.2x: Introduction to Linear Models and Matrix Algebra\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistical-inference-and-modeling-for-high-throug\"\u003ePH525.3x: Statistical Inference and Modeling for High-throughput Experiments\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/high-dimensional-data-analysis\"\u003ePH525.4x: High-Dimensional Data Analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eGenomics Data Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-bioconductor-annotation-and-analys\"\u003ePH525.5x: Introduction to Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/case-studies-in-functional-genomics\"\u003ePH525.6x: Case Studies in Functional Genomics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/advanced-bioconductor\"\u003ePH525.7x: Advanced Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis class was supported in part by NIH grant R25GM114818.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"399:T12ef,"])</script><script>self.__next_f.push([1,"\u003cp\u003eWe will explain how to perform the standard processing and normalization steps, starting with raw data, to get to the point where one can investigate relevant biological questions. Throughout the case studies, we will make use of exploratory plots to get a general overview of the shape of the data and the result of the experiment. We start with RNA-seq data analysis covering basic concepts and a first look at FASTQ files. We will also go over quality control of FASTQ files; aligning RNA-seq reads; visualizing alignments and move on to analyzing RNA-seq at the gene-level : counting reads in genes; Exploratory Data Analysis and variance stabilization for counts; count-based differential expression; normalization and batch effects. Finally, we cover RNA-seq at the transcript-level : inferring expression of transcripts (i.e. alternative isoforms); differential exon usage. We will learn the basic steps in analyzing DNA methylation data, including reading the raw data, normalization, and finding regions of differential methylation across multiple samples. The course will end with a brief description of the basic steps for analyzing ChIP-seq datasets, from read alignment, to peak calling, and assessing differential binding patterns across multiple samples.\u003c/p\u003e\n\u003cp\u003eGiven the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.\u003c/p\u003e\n\u003cp\u003eThese courses make up two Professional Certificates and are self-paced:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis for Life Sciences:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistics-and-r\"\u003ePH525.1x: Statistics and R for the Life Sciences\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-linear-models-and-matrix-algebra\"\u003ePH525.2x: Introduction to Linear Models and Matrix Algebra\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistical-inference-and-modeling-for-high-throug\"\u003ePH525.3x: Statistical Inference and Modeling for High-throughput Experiments\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/high-dimensional-data-analysis\"\u003ePH525.4x: High-Dimensional Data Analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eGenomics Data Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-bioconductor-annotation-and-analys\"\u003ePH525.5x: Introduction to Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/case-studies-in-functional-genomics\"\u003ePH525.6x: Case Studies in Functional Genomics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/advanced-bioconductor\"\u003ePH525.7x: Advanced Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis class was supported in part by NIH grant R25GM114818.\u003c/p\u003e\n\u003cp\u003eHarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the \u003ca href=\"https://www.edx.org/edx-terms-service\" title=\"Follow link\"\u003eedX honor code\u003c/a\u003e, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.\u003c/p\u003e\n\u003cp\u003eHarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our \u003ca href=\"http://harvardx.harvard.edu/research-statement\" title=\"Follow link\"\u003eresearch statement \u003c/a\u003eto learn more.\u003c/p\u003e\n\u003cp\u003eHarvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact \u003ca href=\"mailto:harvardx@harvard.edu\"\u003eharvardx@harvard.edu\u003c/a\u003e and/or \u003ca href=\"https://www.edx.org/contact-us\" title=\"Follow link\"\u003ereport your experience through the edX contact form\u003c/a\u003e.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"39a:T1129,"])</script><script>self.__next_f.push([1,"\u003cp\u003eWe begin with an introduction to the relevant biology, explaining what we measure and why. Then we focus on the two main measurement technologies: next generation sequencing and microarrays. We then move on to describing how raw data and experimental information are imported into R and how we use Bioconductor classes to organize these data, whether generated locally, or harvested from public repositories or institutional archives. Genomic features are generally identified using intervals in genomic coordinates, and highly efficient algorithms for computing with genomic intervals will be examined in detail. Statistical methods for testing gene-centric or pathway-centric hypotheses with genome-scale data are found in packages such as limma, some of these techniques will be illustrated in lectures and labs.\u003c/p\u003e\n\u003cp\u003eGiven the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.\u003c/p\u003e\n\u003cp\u003eThese courses make up two Professional Certificates and are self-paced:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis for Life Sciences:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistics-and-r\"\u003ePH525.1x: Statistics and R for the Life Sciences\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-linear-models-and-matrix-algebra\"\u003ePH525.2x: Introduction to Linear Models and Matrix Algebra\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistical-inference-and-modeling-for-high-throug\"\u003ePH525.3x: Statistical Inference and Modeling for High-throughput Experiments\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/high-dimensional-data-analysis\"\u003ePH525.4x: High-Dimensional Data Analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eGenomics Data Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-bioconductor-annotation-and-analys\"\u003ePH525.5x: Introduction to Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/case-studies-in-functional-genomics\"\u003ePH525.6x: Case Studies in Functional Genomics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/advanced-bioconductor\"\u003ePH525.7x: Advanced Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis class was supported in part by NIH grant R25GM114818.\u003c/p\u003e\n\u003cp\u003eHarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the \u003ca href=\"https://www.edx.org/edx-terms-service\" title=\"Follow link\"\u003eedX honor code\u003c/a\u003e, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.\u003c/p\u003e\n\u003cp\u003eHarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our \u003ca href=\"http://harvardx.harvard.edu/research-statement\" title=\"Follow link\"\u003eresearch statement \u003c/a\u003eto learn more.\u003c/p\u003e\n\u003cp\u003eHarvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact \u003ca href=\"mailto:harvardx@harvard.edu\"\u003eharvardx@harvard.edu\u003c/a\u003e and/or \u003ca href=\"https://www.edx.org/contact-us\" title=\"Follow link\"\u003ereport your experience through the edX contact form\u003c/a\u003e.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"39b:Tc54,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIf you’re interested in data analysis and interpretation, then this is the data science course for you. We start by learning the mathematical definition of distance and use this to motivate the use of the singular value decomposition (SVD) for dimension reduction of high-dimensional data sets, and multi-dimensional scaling and its connection to principle component analysis. We will learn about the \u003cem\u003ebatch effect,\u003c/em\u003e the most challenging data analytical problem in genomics today, and describe how the techniques can be used to detect and adjust for batch effects. Specifically, we will describe the principal component analysis and factor analysis and demonstrate how these concepts are applied to data visualization and data analysis of high-throughput experimental data.\u003c/p\u003e\n\u003cp\u003eFinally, we give a brief introduction to machine learning and apply it to high-throughput, large-scale data. We describe the general idea behind clustering analysis and descript K-means and hierarchical clustering and demonstrate how these are used in genomics and describe prediction algorithms such as k-nearest neighbors along with the concepts of training sets, test sets, error rates and cross-validation.\u003c/p\u003e\n\u003cp\u003eGiven the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.\u003c/p\u003e\n\u003cp\u003eThese courses make up two Professional Certificates and are self-paced:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis for Life Sciences:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistics-and-r\"\u003ePH525.1x: Statistics and R for the Life Sciences\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-linear-models-and-matrix-algebra\"\u003ePH525.2x: Introduction to Linear Models and Matrix Algebra\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistical-inference-and-modeling-for-high-throug\"\u003ePH525.3x: Statistical Inference and Modeling for High-throughput Experiments\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/high-dimensional-data-analysis\"\u003ePH525.4x: High-Dimensional Data Analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eGenomics Data Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-bioconductor-annotation-and-analys\"\u003ePH525.5x: Introduction to Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/case-studies-in-functional-genomics\"\u003ePH525.6x: Case Studies in Functional Genomics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/advanced-bioconductor\"\u003ePH525.7x: Advanced Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis class was supported in part by NIH grant R25GM114818.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"39c:T609,\u003cp\u003eBuilding on the concepts from the first course in the Six Sigma Program, Define and Measure, in this course, you will learn how to statistically analyze data with the Six Sigma methodology using inferential statistical techniques to determine confidence intervals and to test hypotheses based on sample data. You will also review cause and effect techniques for root cause analysis.\u003c/p\u003e\n\u003cp\u003eYou will learn how to perform correlation and regression analyses in order to confirm the root cause and understand how to improve your process and plan designed experiments.\u003c/p\u003e\n\u003cp\u003eYou will learn how to implement statistical process control using control charts and quality management tools, including the 8 Disciplines and the 5 Whys to reduce risk and manage process deviations.\u003c/p\u003e\n\u003cp\u003eTo complement the lectures, learners are provided with interactive exercises, which allow learners to see the statistics \"in action.\" Learners then master statistical concepts by completing practice problems. These are then reinforced using interactive case studies, which illustrate the application of the statistics in quality improvement situations.\u003c/p\u003e\n\u003cp\u003eUpon successful completion of this program, learners will earn the TUM Lean and Six Sigma Yellow Belt certification, confirming mastery of Lean Six Sigma fundamentals to a Green Belt level. The material is based on the American Society for Quality (www.asq.org) Body of Knowledge up to a Green Belt Level. The Professional Certificate is designed as preparation for a Lean Six Sigma Green Belt exam.\u003c/p\u003e39d:T75f,\u003cp\u003eA strong foundation in mathematics is critical for success in all science and engineering disciplines. Whether you want to make a strong start to a master’s degree, prepare for more advanced courses, solidify your knowledge in a professional context or simply brush up on fundamentals, this course will get you up to speed.\u003c/p\u003e\n\u003cp\u003eProbability theory can be applied to learn more about real-life problems, and it is useful for building models. Moreover, it provides the basis for s"])</script><script>self.__next_f.push([1,"tatistics and applications in data analysis. Therefore, it is a useful subject for any aspiring or practicing engineer.\u003c/p\u003e\n\u003cp\u003eWe will use some basic calculus, in particular (partial) differentiation and (multiple) integration. The focus will be on the interpretation rather than on the computation; so the required techniques will be low-level. If, however, you feel insecure about these topics, you can brush up on them in our calculus courses within this series.\u003c/p\u003e\n\u003cp\u003eThis course will offer you an overview of the probability theory elements common to most engineering bachelor programs. It will provide enough depth to cover the probability theory you need to succeed in your engineering master’s or profession in areas such as modeling, finance, signal processing, logistics and more.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis is a review course\u003c/strong\u003e\u003cbr /\u003e\nThis self-contained course is modular, so you do not need to follow the entire course if you wish to focus on a particular aspect. As a review course you are expected to have previously studied or be familiar with most of the material. Hence the pace will be higher than in an introductory course.\u003c/p\u003e\n\u003cp\u003eThis format is ideal for refreshing your bachelor level mathematics and letting you practice as much as you want. Through the Grasple platform, you will have access to plenty of exercises and receive intelligent, personal and immediate feedback.\u003c/p\u003e39e:T76f,\u003cp\u003eWant to know how to avoid bad decisions with data?\u003c/p\u003e\n\u003cp\u003eMaking good decisions with data can give you a distinct competitive advantage in business. This statistics and data analysis course will help you understand the fundamental concepts of sound statistical thinking that can be applied in surprisingly wide contexts, sometimes even before there is any data! Key concepts like understanding variation, perceiving relative risk of alternative decisions, and pinpointing sources of variation will be highlighted.\u003c/p\u003e\n\u003cp\u003eThese big picture ideas have motivated the development of quantitative models, but in most traditional statistics"])</script><script>self.__next_f.push([1," courses, these concepts get lost behind a wall of little techniques and computations. In this course we keep the focus on the ideas that really matter, and we illustrate them with lively, practical, accessible examples.\u003c/p\u003e\n\u003cp\u003eWe will explore questions like: How are traditional statistical methods still relevant in modern analytics applications? How can we avoid common fallacies and misconceptions when approaching quantitative problems? How do we apply statistical methods in predictive applications? How do we gain a better understanding of customer engagement through analytics?\u003c/p\u003e\n\u003cp\u003eThis course will be is relevant for anyone eager to have a framework for good decision-making. It will be good preparation for students with a bachelor's degree contemplating graduate study in a business field.\u003c/p\u003e\n\u003cp\u003eOpportunities in analytics are abundant at the moment. Specific techniques or software packages may be helpful in landing first jobs, but those techniques and packages may soon be replaced by something newer and trendier. Understanding the ways in which quantitative models really work, however, is a management level skill that is unlikely to go out of style.\u003c/p\u003e\n\u003cp\u003eThis course is part of the Business Principles and Entrepreneurial Thought XSeries.\u003c/p\u003e39f:T871,"])</script><script>self.__next_f.push([1,"\u003cp\u003eToday the principles and techniques of reproducible research are more important than ever, across diverse disciplines from astrophysics to political science. No one wants to do research that can’t be reproduced. Thus, this course is really for anyone who is doing any data intensive research. While many of us come from a biomedical background, this course is for a broad audience of data scientists. \u003c/p\u003e\n\u003cp\u003eTo meet the needs of the scientific community, this course will examine the fundamentals of methods and tools for reproducible research. Led by experienced faculty from the Harvard T.H. Chan School of Public Health, you will participate in six modules that will include several case studies that illustrate the significant impact of reproducible research methods on scientific discovery. \u003c/p\u003e\n\u003cp\u003eThis course will appeal to students and professionals in biostatistics, computational biology, bioinformatics, and data science. The course content will blend video lectures, case studies, peer-to-peer engagements and use of computational tools and platforms (such as R/RStudio, and Git/Github), culminating in a final presentation of a final reproducible research project. \u003c/p\u003e\n\u003cp\u003eWe’ll cover Fundamentals of Reproducible Science; Case Studies; Data Provenance; Statistical Methods for Reproducible Science; Computational Tools for Reproducible Science; and Reproducible Reporting Science. These concepts are intended to translate to fields throughout the data sciences: physical and life sciences, applied mathematics and statistics, and computing. \u003c/p\u003e\n\u003cp\u003eConsider this course a survey of best practices: we’d like to make you aware of pitfalls in reproducible data science, some failure - and success - stories in the past, and tools and design patterns that might help make it all easier. But ultimately it’ll be up to you to take the skills you learn from this course to create your own environment in which you can easily carry out reproducible research, and to encourage and integrate with similar environments for your collaborators and colleagues. We look forward to seeing you in this course and the research you do in the future!\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3a0:T422,\u003cp\u003eIn this course, you will learn how to organize your data within the Microsoft Office Excel software tool. Once organized, we will discuss data cleaning. You will learn how to identify outliers and anomalies in the data, and how to identify and change data-types. Together we will develop a data analysis plan, after which we will apply analysis methods and tools, including exploratory analysis, evaluation of results, and comparison with other findings.\u003c/p\u003e\n\u003cp\u003eIn this robust Excel course, you will gain a solid foundation in using advanced Excel functions such as pivot tables and vlookup to organize and analyze data sets. You will be able to create an Excel chart in a variety of chart types including scatter plot, pie charts, and more. We’ll discuss various techniques such as descriptive statistics, and review the variety of Excel add-ins available to use this powerful tool to organize, analyze, and transform your data into actionable insights. All course activities are designed and demonstrated using Windows OS and Microsoft Excel 2016.\u003c/p\u003e3a1:T521,\u003cp\u003eThe US Bureau of Labor Statistics forecasts a 32% growth in information security analyst jobs until 2032. These analysts are in demand as part of the team that keeps networks secure.\u003c/p\u003e\n\u003cp\u003eThis course provides practical hands-on computer networking and network security experience that employers want. Through innovative hands-on labs, you’ll learn how to secure a small home office network (SOHO), install and configure DHCP, and filter DNS. You’ll also get real-world practice installing and using an open-source Extended Detection and Response (XDR) system.\u003c/p\u003e\n\u003cp\u003eAdditionally, you’ll build valuable supporting knowledge of ports, protocols, and IP addresses, including IPv6 and network routing. You’ll learn about layer 2 and 3 addressing, routers, and routing tables.\u003c/p\u003e\n\u003cp\u003ePlus, you’ll develop knowledge of cybersecurity analyst tools for data protection, endpoint protection, and Security information and event management (SIEM), which you can apply"])</script><script>self.__next_f.push([1," to an organization’s compliance and threat intelligence needs, which is crucial in today’s cybersecurity landscape.\u003c/p\u003e\n\u003cp\u003eYou’ll complete a final project where you will demonstrate your ability to perform network and security planning tasks.\u003c/p\u003e\n\u003cp\u003eNetworking and network security skills pay. Invest in yourself and enroll today!\u003c/p\u003e3a2:T54b,\u003cp\u003eIn this undergraduate-level biostatistics course, the learners will be introduced to the use of statistics and study designs in biology. Upon successful completion of this course, learners will be able to design experimental, quasi-experimental and observational studies that will meet regulatory guidelines; collect, analyze, and interpret data using appropriate statistical tools. These are skills utilized in bio-statistical research in healthcare as well as other biology related fields. The importance of having these skills is recognized within public health sectors relating to analysis of drug effectiveness and risk factors for different illnesses, effectiveness of heath care interventions as well as helping explain biological phenomena.\u003c/p\u003e\n\u003cp\u003eIn addition to fulfilling requirements in undergraduate college programs, this undergraduate level course also provides future healthcare professionals with foundational coursework required for successful entry into a health professions graduate program or medical school. This course may also spark further interest for the learner towards a more advanced degree in biostatistics. The field of biostatistics provides an increasing opportunity for employment. According to the United States Bureau of Labor Statistics, this field will experience a 14% employment growth between 2010 and 2020.\u003c/p\u003e3a3:T11a6,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIn this course, we begin with approaches to visualization of genome-scale data, and provide tools to build interactive graphical interfaces to speed discovery and interpretation. Using knitr and rmarkdown as basic authoring tools, the concept of reproducible research is developed, and the concept of an executable document is presented. In this framework reports are linked tightly to the underlying data and code, enhancing reproducibility and extensibility of completed analyses. We study out-of-memory approaches to the analysis of very large data resources, using relational databases or HDF5 as \"back ends\" with familiar R interfaces. Multiomic data integration is illustrated using a curated version of The Cancer Genome Atlas. Finally, we explore cloud-resident resources developed for the Encyclopedia of DNA Elements (the ENCODE project). These address transcription factor binding, ATAC-seq, and RNA-seq with CRISPR interference.\u003c/p\u003e\n\u003cp\u003eGiven the diversity in educational background of our students we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.\u003c/p\u003e\n\u003cp\u003eThese courses make up two Professional Certificates and are self-paced:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis for Life Sciences:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistics-and-r\"\u003ePH525.1x: Statistics and R for the Life Sciences\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-linear-models-and-matrix-algebra\"\u003ePH525.2x: Introduction to Linear Models and Matrix Algebra\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/statistical-inference-and-modeling-for-high-throug\"\u003ePH525.3x: Statistical Inference and Modeling for High-throughput Experiments\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/high-dimensional-data-analysis\"\u003ePH525.4x: High-Dimensional Data Analysis\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eGenomics Data Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/introduction-to-bioconductor-annotation-and-analys\"\u003ePH525.5x: Introduction to Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/case-studies-in-functional-genomics\"\u003ePH525.6x: Case Studies in Functional Genomics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/advanced-bioconductor\"\u003ePH525.7x: Advanced Bioconductor\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis class was supported in part by NIH grant R25GM114818.\u003c/p\u003e\n\u003cp\u003eHarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. HarvardX will take appropriate corrective action in response to violations of the \u003ca href=\"https://www.edx.org/edx-terms-service\" title=\"Follow link\"\u003eedX honor code\u003c/a\u003e, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. No refunds will be issued in the case of corrective action for such violations. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.\u003c/p\u003e\n\u003cp\u003eHarvardX pursues the science of learning. By registering as an online learner in an HX course, you will also participate in research about learning. Read our \u003ca href=\"http://harvardx.harvard.edu/research-statement\" title=\"Follow link\"\u003eresearch statement \u003c/a\u003eto learn more.\u003c/p\u003e\n\u003cp\u003eHarvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. All members of the HarvardX community are expected to abide by Harvard policies on nondiscrimination, including sexual harassment, and the edX Terms of Service. If you have any questions or concerns, please contact \u003ca href=\"mailto:harvardx@harvard.edu\"\u003eharvardx@harvard.edu\u003c/a\u003e and/or \u003ca href=\"https://www.edx.org/contact-us\" title=\"Follow link\"\u003ereport your experience through the edX contact form\u003c/a\u003e.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3a4:T4c7,\u003cp\u003eManaging databases is a critical skill for Data Engineers and Database Administrators to ensure data is reliable, protected and easily accessible for organizations to make better decisions, solve problems and create business value.\u003c/p\u003e\n\u003cp\u003eWith the amount of data continually expanding and business leaders focused on building data-literate organizations, it’s no surprise that Database Administrators are in high demand and earn a median salary of US $98,860 per year according to the US Bureau of Labor Statistics.\u003c/p\u003e\n\u003cp\u003eThis course provides you with the knowledge and hands-on experience to manage and maintain databases, understand database security, design and define database schemas, tables, views, and other database objects, describe storage, perform backups and recovery, troubleshoot errors, monitor and optimize performance and automate tasks.\u003c/p\u003e\n\u003cp\u003eThis course includes hands-on practice labs and a real-world inspired project to add to your portfolio that will demonstrate your ability to perform the Database Administration tasks using relational databases (RDBMSes) such as MySQL, PostgreSQL and IBM Db2.\u003c/p\u003e\n\u003cp\u003ePrior knowledge of database fundamentals and SQL is required to complete this course.\u003c/p\u003e3a5:T92f,"])</script><script>self.__next_f.push([1,"\u003cp\u003eAnimal breeding involves the selective breeding of domestic animals with the intention to improve desirable (and heritable) qualities in the next generation. This course introduces the steps required to design a program for breeding animals and teaches the genetic and statistical concepts that are needed to build a solid breeding program. \u003c/p\u003e\n\u003cp\u003eIn this course you will learn how an animal breeder balances the need for improving desirable qualities of the animals with the need for genetic diversity and long sustainability of the program. The scientific concepts in genetics that are applied in animal breeding will be explained and you will learn to apply the models and computational methods that are used in animal breeding. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrerequisites\u003c/strong\u003e\u003cbr /\u003e\nKnowledge in the area of statistics at 2nd or 3rd year university level is needed to follow this course successfully.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFor whom\u003cbr /\u003e\n\u003c/strong\u003e Professionals working with animals will use the knowledge from this course to understand the impact of breeding on their populations, and will be able to include the genetic principles in their decisions. For further studies such as M.Sc. level courses in breeding and genetics, this course will allow you an advanced starting point.\u003c/p\u003e\n\u003cp\u003eAlthough this course is open to everyone, it is particular useful for breeders of:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCows\u003c/li\u003e\n\u003cli\u003ePoultry/Chicken\u003c/li\u003e\n\u003cli\u003eHorses\u003c/li\u003e\n\u003cli\u003ePigs/Swine\u003c/li\u003e\n\u003cli\u003eDogs\u003c/li\u003e\n\u003cli\u003eSheep\u003c/li\u003e\n\u003cli\u003eGoat\u003c/li\u003e\n\u003cli\u003eFish\u003c/li\u003e\n\u003cli\u003eShrimp\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eProfessional Certificate Program\u003cbr /\u003e\n\u003c/strong\u003e This course is part of the \u003ca href=\"https://www.edx.org/professional-certificate/wageningenx-animal-breeding-and-genetics?index=product\u0026queryID=0aba9f9feb927113b49a8e266d916a03\u0026position=1\"\u003eProfessional Certificate Program \"Animal Breeding and Genetics\u003c/a\u003e\". Join the other course in the program, \u003ca href=\"https://www.edx.org/course/evaluating-animal-breeding-programmes-2\"\u003eEvaluating Animal Breeding Programs\u003c/a\u003e, and advance your career as a breeder.\u003c/p\u003e\n\u003cp\u003eThe course is developed with financial support and input from the \u003ca href=\"http://www.koeponstichting.nl/index.php/about-us\" title=\"Koepon Foundation\"\u003eKoepon Foundation \u003c/a\u003eand the \u003ca href=\"https://africacgg.net/\" title=\"African Chicken Genetic Gains project\"\u003eAfrican Chicken Genetic Gains project\u003c/a\u003e.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3a6:Tb16,"])</script><script>self.__next_f.push([1,"Inclusive teaching is an issue that has received increased attention on college campuses around the country and around the world. Understanding how course climate impacts students and their learning \u0026nbsp;is increasingly important to faculty and administrators alike across a wide range of educational contexts. Yet creating equitable learning environments that support all students\u0026rsquo; learning can be a challenge, especially when one considers that course climate consists of a variety of factors (e.g., student-student interactions, faculty-student interactions, course content and policies). Despite the fact that creating an inclusive teaching environment is beneficial for all students\u0026rsquo; learning, issues around inclusion and disinclusion are rarely discussed in practical terms. \u003cbr /\u003e\u003cbr /\u003eParticipants in this course will consider multiple facets of inclusive teaching, including the creation of an equitable course climate, the design and implementation of accessible and inclusive classroom practices and assessments, and the selection and implementation of diverse course content. Participants will engage with key concepts in inclusive teaching and learn from experts in higher education who share their important research on student development, microaggressions, stereotype threat, and Universal Design for Learning. Participants will be equipped with tools to help them develop inclusive courses that support all learners.\u003cbr /\u003e\u003cbr /\u003eThis course was developed by the Columbia University Center for Teaching and Learning, with generous funding from the Provost\u0026rsquo;s Teaching \u0026amp; Learning MOOC RFP, and support from the Office of the Vice Provost for Teaching, Learning, and Innovation.\u003cbr /\u003e\u003cbr /\u003eInclusive teaching experts featured in this course: \u003cbr /\u003e\r\n\u003cul\u003e\r\n\u003cli\u003eStephen Brookfield, PhD, John Ireland Endowed Chair, University of St. Thomas\u003c/li\u003e\r\n\u003cli\u003eSheryl Burgstahler, PhD, Director, University of Washington Access Technology Center, Founder and Director, DO-IT Center\u003c/li\u003e\r\n\u003cli\u003eBryan Dewsbury, PhD, Assistant Professor, Biological Sciences, University of Rhode Island\u003c/li\u003e\r\n\u003cli\u003eMichele DiPietro, PhD, Executive Director, Center for Excellence in Teaching and Learning, Professor of Statistics, Kennesaw State University\u003c/li\u003e\r\n\u003cli\u003eZaretta Hammond, MA, Teacher Educator, author of Culturally Responsive Teaching and the Brain\u003c/li\u003e\r\n\u003cli\u003eStephanie Kershbaum, PhD, Associate Professor of English, University of Delaware\u003c/li\u003e\r\n\u003cli\u003eFrank Tuitt, PhD, Provost on Diversity and Inclusion, Senior Advisor to the Chancellor, University of Denver\u003c/li\u003e\r\n\u003cli\u003eDerald Wing Sue, PhD, Professor of Psychology and Education, Teachers\u0026rsquo; College, Columbia University\u003c/li\u003e\r\n\u003cli\u003eMelissa Wright, MA, Associate Director of Assessment and Evaluation, Columbia University Center for Teaching and Learning\u003c/li\u003e\r\n\u003c/ul\u003e"])</script><script>self.__next_f.push([1,"3a7:Tff7,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis course promotes a “grand strategy” approach to how climate change and demographic trends – along with the ascendancy of a global majority middle class – transform our global economy from its historic East-West paradigm to one dominated by North-South dynamics. This is a crucial perspectives for anyone involved in international trade and the globalization of markets.\u003c/p\u003e\n\u003cp\u003eThe world economy is undergoing three structural changes, each of which are enough – on their own – to define a new era of globalization . The first is climate change, which decimates lower latitudes (developing countries), denying them the benefits of globalization, while paradoxically empowering higher latitude global markets. The second is demographic aging unfolding at great rapidity across America, Europe, and Asia (Japan in particular). The third is the ascendancy of a majority global middle class whose huge resource requirements, per economists of all stripes, will drive future global economic growth – if this South-centric mass of humanity can successfully navigate climate change’s devastating impact.\u003c/p\u003e\n\u003cp\u003ePut them together and the world trade is facing an unprecedented tilting of its geopolitical axis from one historically oriented East-West to one dominated by North-South dynamics (e.g., mass northward migrations by climate refugees [see the US-Mexico border today]). The world’s 21st-century superpowers (United States, European Union, Russia, China, India) will thus compete in helping smaller powers survive and navigate these world-restructuring dynamics, in effect entering into a superpower “brand war” to win (first) economic and (then) political allegiance from among these more vulnerable nation-states.\u003c/p\u003e\n\u003cp\u003eIn this new form of competition – one that will unfold as much in the cyber realm as the real world, superpowers will seek to address the majority global middle class’s quest for stability, prosperity, and security. Having achieved consumer status for the first time, these billions, largely centered in the Global South (per World Bank statistics), will naturally be attracted to those superpowers most adept at creating a sense of geopolitical “belonging” in larger unions anchored by their large financial markets that facilitate their export-driven growth. We are already witnessing this strategic offering in processes like the EU’s state-accession model and China’s Belt and Road Initiative — despite the recent increase in trade barriers (tariffs) and their effects on international markets.\u003c/p\u003e\n\u003cp\u003eEmploying a “grand strategic” perspective, this MOOC is the first to explore that cluster of tectonic changes as they transform our world, offering competitive advantages to some international businesses while generating lessons and understanding for anyone involved in information technology, economic policy, foreign markets, and foreign direct investment.\u003c/p\u003e\n\u003cp\u003eScientists estimates that species the world over are forced by climate change to increase their evolutionary speed by roughly 10,000 times. The same will be true of humans and all their enterprises: reconfiguring national borders, disrupting labor markets, reshaping capital markets, triggering financial crises, altering how and why multinational corporations engage in outsourcing, and altering global capitalism and its free trade rule-set to its very core.\u003c/p\u003e\n\u003cp\u003eIn combination, climate change, demographic transitions and the emergence of a global majority middle class alter the very nature of economic activity this century (liberalization of investments, role of trade agreements, strategic partnerships among great powers), sending different countries down wildly different paths that beg the question: Exactly what is globalization going forward?\u003c/p\u003e\n\u003cp\u003eIn sum, if broad-framing macroeconomic change is a skill-set you want to master, this course lays the groundwork for your understanding of how globalization affects us all by altering our planet, transforming our societies, and recasting this world order of American creation.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3a8:T632,\u003cul\u003e\n\u003cli\u003eHow climate change will trigger the biggest geographic transfer of resource wealth in human history – super-empowering the New North while beggaring the Global South\u003c/li\u003e\n\u003cli\u003eHow climate change will drive South-to-North human migrations far beyond anything the world currently witnesses, triggering state failure across lower latitudes and supercharging angry nationalism and populism across higher-latitude states.\u003c/li\u003e\n\u003cli\u003eHow demographic aging across the Global North will permanently transform those nations and their markets.\u003c/li\u003e\n\u003cli\u003eHow globalization’s unfolding is largely driven by nations embracing a demographic transition from high-birth/death rates to low-birth/death rates, allowing for a “demographic dividend” that enables economic integration into global value chains.\u003c/li\u003e\n\u003cli\u003eHow the ascendancy of a majority global middle class re-runs the geopolitical experiment that was the Eurasian continent’s successive “world wars” of the early 20th century, as both Left (communism) and Right (fascism) sought to control that process.\u003c/li\u003e\n\u003cli\u003eHow superpowers will compete in “brand wars” to earn the economic, network, political, and security allegiance of smaller states threatened by these structural shifts.\u003c/li\u003e\n\u003cli\u003eHow the Western Hemisphere (North/Central/South America \u0026amp; Caribbean) are unusually advantaged relative to the Eastern Hemisphere (Europe, Africa, Asia) for the North-South integration to come.\u003c/li\u003e\n\u003cli\u003eHow to process all these tectonic changes and world-restructuring dynamics from a grand strategic perspective.\u003c/li\u003e\n\u003c/ul\u003e3a9:T48d,\u003cp\u003eThis course, presented by the Statistics Department, introduces participants to the underlying concepts, definitions, and methodology for the compilation of Financial Soundness Indicators (FSIs). FSIs were developed by the IMF in the late 1990s and currently, more than 140 countries compile and report FSIs to the IMF. These indicators are widely used by researchers, analysts, and policymakers around the world to monitor the soundness of "])</script><script>self.__next_f.push([1,"the financial system as a whole from a macroprudential perspective, as well as by IMF staff in financial stability analysis and surveillance. This course covers the history of the FSIs and their application in surveillance and macroprudential analysis, the conceptual framework for the FSIs, the data collection process for FSI compilation and its underlying aggregation and consolidation methodologies as well as the application of core and additional FSIs in macroprudential analysis. An important reference throughout this course is the Financial Soundness Indicators Compilation Guide revised in 2019. The 2019 FSIs Guide is the ultimate authority on FSI concepts and methods and is the foundation of this course.\u003c/p\u003e3aa:Td50,"])</script><script>self.__next_f.push([1,"\u003cp\u003eAnimal breeding, and especially breeding of farm animals and aquaculture species, has developed into a professional industry with modern technologies, large-scale data collection, and analyses. This has resulted in very efficient and effective breeding programs. However, in many developing countries, and for many lesser known livestock and fish species there is a need for tailor-made breeding programs. In this MOOC, you will learn about the implementation and evaluation of both large industrial scale and the tailor-made breeding programs, in terms of genetic progress and genetic diversity.\u003c/p\u003e\n\u003cp\u003eTogether with other learners, you will dive into the reasons behind crossbreeding in relation to dissemination of genetic improvement. It is essential to know how key biological factors affect the structure of a breeding program and to understand the different structures a breeding program can have.\u003c/p\u003e\n\u003cp\u003eJoin this course and learn everything about how to properly evaluate breeding programs. In several knowledge clips and assignments, you will learn how to assess the effects of legislation, competition, GxE, new technology on breeding programs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEliminating diseases\u003c/strong\u003e\u003cbr /\u003e\nTwo special sections are devoted to genetic diversity and, more specifically, to (i) monogenic recessive disorders, and (ii) genetic diversity at the population level. Monogenic recessive disorders play an important role in breeding programs for companion animals such as dogs and horses. Modern DNA sequencing technology now makes it possible not only to detect these mutations but also to design breeding strategies aimed at eliminating these diseases. Genetic diversity at population level is important to maintain flexibility in populations and to keep animal populations healthy. In this course, you will learn more about how to monitor and conserve genetic diversity in breeding programmes.\u003c/p\u003e\n\u003cp\u003eAfter finishing this course you can make informed decisions when setting up a breeding program for a specific animal in a specific production system and recognize and identify key elements of the course in real-life examples of breeding programs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrerequisites\u003c/strong\u003e\u003cbr /\u003e\nPlease know that knowledge of statistics at a 2nd or 3rd year university level is needed to follow this course successfully. This course partially builds on knowledge gained in the MOOC Genetic Models for Animal Breeding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFor Whom\u003c/strong\u003e\u003cbr /\u003e\nAlthough this course is open to everyone, it is particular useful for breeders of:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCows\u003c/li\u003e\n\u003cli\u003ePoultry/Chicken\u003c/li\u003e\n\u003cli\u003eHorses\u003c/li\u003e\n\u003cli\u003ePigs/Swine\u003c/li\u003e\n\u003cli\u003eDogs\u003c/li\u003e\n\u003cli\u003eSheep\u003c/li\u003e\n\u003cli\u003eGoat\u003c/li\u003e\n\u003cli\u003eFish\u003c/li\u003e\n\u003cli\u003eShrimp\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eProfessional Certificate Program\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis course is part of the\u003ca href=\"https://www.edx.org/professional-certificate/wageningenx-animal-breeding-and-genetics?index=product\u0026queryID=0aba9f9feb927113b49a8e266d916a03\u0026position=1\"\u003e Professional Certificate Programme \"Animal Breeding and Genetics\"\u003c/a\u003e. Join the other course in the programme, \u003ca href=\"https://www.edx.org/course/genetic-models-for-animal-breeding-2\"\u003eGenetic Models for Animal Breeding\u003c/a\u003e, and advance your career as a breeder.\u003c/p\u003e\n\u003cp\u003eThe course is developed with financial support and input from the \u003ca href=\"http://www.koeponstichting.nl/index.php/about-us\" title=\"Koepon Foundation\"\u003eKoepon Foundation\u003c/a\u003e.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3ab:T469,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eOne of the most commonly used tools in data science, pandas is a Python library used to load, process, and analyze datasets using SQL-like queries. Pandas offers several advantages, such as data representation, simpler lines of code, and the ability to handle large sets of data. A number of academic and commercial domains, including finance, economics, statistics, web analytics, and other entities, use pandas as part of their data analytics toolkit.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIn this hands-on guided project, you will learn to perform preliminary data analysis and credit risk analysis on a credit card client dataset using the Python library pandas. You will learn how to import required libraries, explore datasets, analyze data, and visualize the dataset. By the end of this project, you will have learned the fundamentals of data analysis using pandas and developed job-ready skills.\u003c/p\u003e\n\u003cp\u003eYou will be provided with access to a Cloud based-IDE which has all of the required software, including Python pandas, pre-installed. All you need is a recent version of a modern web browser to complete this project.\u003c/p\u003e3ac:Ta7f,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cspan lang=\"EN-US\"\u003e“I am a Lieutenant Colonel in passive service of the Ecuadorian Transit Commission and a teacher at Condues (Training School for Professional Drivers) in Guayaquil, and this course allowed me to understand the reality of statistics and the problems at the regional level in Latin America and the Caribbean, as well as the actions and projections to achieve a reduction in traffic crashes in my country and the others around the region.\"\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eCan you imagine being able to contrast your opinions and experiences with people like Lieutenant Colonel Enrique from Ecuador or other practicing public managers and representatives of Civil Society organizations from all over the region? In this course, you will have that opportunity and also access to videos of international specialists from various organizations (IDB, PAHO / WHO, WRI, LatinNCAP, among others), readings, best practices in the Caribbean region, recommendations, practical activities, and discussion forums.\u003c/p\u003e\n\u003cp\u003eAs traffic crashes are one of the main causes of death in Latin America and the Caribbean, their consequences are immediate and generate a great social and economic burden.\u003c/p\u003e\n\u003cp\u003eBe a part of the change now, and let's make safe mobility a reality!\u003c/p\u003e\n\u003cp\u003eThis course is \"self-paced,\" so you can enroll any time, even if the course has been open for a while. You can take it at the time that is most suitable for you inside the enrollment period of the course.\u003c/p\u003e\n\u003cp\u003eIf you choose the \u003cstrong\u003eAudit Track\u003c/strong\u003e , you will have unlimited access to the course content, but you won't be able to complete the assessed activities or receive the certificate.\u003c/p\u003e\n\u003cp\u003eIf you opt for the \u003cstrong\u003eVerified track\u003c/strong\u003e , you can access the course in an unlimited way and complete the qualified evaluations until the closing date after making a payment of $10. If you pass, in addition to the verified certificate, you will obtain a * \u003cstrong\u003e\u003ca href=\"https://cursos.iadb.org/en/indes/digital-badges\"\u003e\u003cspan lang=\"EN-US\"\u003edigital badge\u003c/span\u003e\u003c/a\u003e\u003c/strong\u003e that allows you to change the way you share your academic and professional achievements, for example, on social media.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e*Did you know there is financial aid to opt for the verified certificate?\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eedX offers financial assistance for learners who want to earn Verified Certificates but who may not be able to pay the Verified Certificate fee. Subscribe to the course and \u003cstrong\u003e\u003cem\u003e\u003ca href=\"https://courses.edx.org/financial-assistance/apply/\"\u003eapply for financial assistance\u003c/a\u003e\u003c/em\u003e\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eSee more information in the Frequently Asked Questions section below.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3ad:T972,"])</script><script>self.__next_f.push([1,"\u003cp\u003eRandomness is inherent in all processes including manufacturing. The fundamental concepts taught in this course will help learners develop powerful statistical process control methods that are the foundation of world-class manufacturing quality.\u003c/p\u003e\n\u003cp\u003eAs part of the Principles of Manufacturing MicroMasters program, this course will introduce statistical methods that apply to any unit manufacturing process. We will cover the following topics:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eRecognizing inherent variability in continuous production\u003c/li\u003e\n\u003cli\u003eIdentifying sources of process output variation\u003c/li\u003e\n\u003cli\u003eDescribing variation in a structured manner\u003c/li\u003e\n\u003cli\u003eApplying basic probability and statistics concepts to characterize process variation\u003c/li\u003e\n\u003cli\u003eDifferentiating between design specifications and process capability\u003c/li\u003e\n\u003cli\u003eSynthesizing novel approaches to unfamiliar situations by extending the core material (i.e. go beyond the “standard” uses).\u003c/li\u003e\n\u003cli\u003eAssessing the appropriateness of various statistical methods for a variety of problems\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDevelop the engineering and management skills needed for competence and competitiveness in today’s manufacturing industry with the Principles of Manufacturing MicroMasters Credential, designed and delivered by MIT’s #1-ranked Mechanical Engineering department in the world. Learners who pass the 8 courses in the program will earn the MicroMasters Credential and qualify to apply to gain credit towards MIT’s Master of Engineering in Advanced Manufacturing \u0026amp; Design program.\u003c/p\u003e\n\u003cp\u003e---\u003c/p\u003e\n\u003cp\u003ePlease note: edX Inc. has recently entered into an \u003ca href=\"https://news.mit.edu/2021/mit-harvard-transfer-edx-2u-0629\"\u003eagreement to transfer the edX platform to 2U, Inc\u003c/a\u003e., which will continue to run the platform thereafter. The sale will not affect your course enrollment, course fees or change your course experience for this offering. It is possible that the closing of the sale and the transfer of the edX platform may be effectuated sometime in the Fall while this course is running. Please be aware that there could be changes to the edX platform Privacy Policy or Terms of Service after the closing of the sale. However, 2U has committed to preserving robust privacy of individual data for all learners who use the platform. For more information see the \u003ca href=\"https://support.edx.org/hc/en-us/articles/4403415754007-edX-and-2U\"\u003eedX Help Center\u003c/a\u003e.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3ae:T43d,\u003cp\u003eCybersecurity as a profession is projected to grow much faster than the national average for all occupations.1 Want to break into the field?\u003c/p\u003e\n\u003cp\u003eThis beginner-friendly and asynchronous course will help you jumpstart your cybersecurity career. Engage in an online learning experience tailored to your experience level.\u003c/p\u003e\n\u003cp\u003eIn it, you will learn to think like a cyber pro, at your own pace. Study the key elements of the CIA Triad, cryptography basics, and the daily responsibilities of cybersecurity professionals working in the field today.\u003c/p\u003e\n\u003cp\u003eKey concepts covered in this course include:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eCybersecurity\u003c/li\u003e\n\u003cli\u003eCIA Triad\u003c/li\u003e\n\u003cli\u003eCryptography\u003c/li\u003e\n\u003cli\u003ePlaintext\u003c/li\u003e\n\u003cli\u003eCiphertext\u003c/li\u003e\n\u003cli\u003eEncryption\u003c/li\u003e\n\u003cli\u003eDecryption\u003c/li\u003e\n\u003cli\u003eCipher\u003c/li\u003e\n\u003cli\u003eKey\u003c/li\u003e\n\u003cli\u003eCaesar Cipher\u003c/li\u003e\n\u003cli\u003eSymmetric encryption\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eNo prior experience or technical installations is required to join or complete this course.\u003c/p\u003e\n\u003cp\u003eFootnote:\u003c/p\u003e\n\u003cp\u003e1 Occupational Outlook Handbook: Information Security Analysts. (2022). U.S. Bureau of Labor Statistics. March 14, 2023.\u003c/p\u003e3af:T5e1,\u003cp\u003eAlgorithms power the biggest web companies and the most promising startups. Interviews at tech companies start with questions that probe for good algorithm thinking.\u003c/p\u003e\n\u003cp\u003eIn this computer science course, you will learn how to think about algorithms and create them using sorting techniques such as quick sort and merge sort, and searching algorithms, median finding, and order statistics.\u003c/p\u003e\n\u003cp\u003eThe course progresses with Numerical, String, and Geometric algorithms like Polynomial Multiplication, Matrix Operations, GCD, Pattern Matching, Subsequences, Sweep, and Convex Hull. It concludes with graph algorithms like shortest path and spanning tree.\u003c/p\u003e\n\u003cp\u003eTopics covered:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eSorting and Searching\u003c/li\u003e\n\u003cli\u003eNumerical Algorithms\u003c/li\u003e\n\u003cli\u003eString Algorithms\u003c/li\u003e\n\u003cli\u003eGeometric Algorithms\u003c/li\u003e\n\u003cli\u003eGraph Algorithms\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThis course is part of the \u003ca href=\"https://www.edx.org/xseries/fundamentals-computer-science\"\u003eFundamen"])</script><script>self.__next_f.push([1,"tals of Computer Science XSeries Program\u003c/a\u003e:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/programming-basics-iitbombayx-cs101-1x\"\u003eProgramming Basics\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/object-oriented-programming-iitbombayx-cs101-2x\"\u003eObject-Oriented Programming\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/foundations-data-structures-iitbombayx-cs213-1x#!\"\u003eFoundations of Data Structures\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003e\u003ca href=\"https://www.edx.org/course/implementation-data-structures-iitbombayx-cs213-2x\"\u003eImplementation of Data Structures\u003c/a\u003e\u003c/li\u003e\n\u003c/ul\u003e3b0:T6c3,\u003cp\u003eIn this course, part of the Principles of Manufacturing MicroMasters program, you will learn how to analyze manufacturing systems to optimize performance and control cost. You will develop an understanding of seemingly opaque production lines with a particular emphasis on random disruptive events – their effects and how to deal with them, as well as inventory dynamics and management. \u003c/p\u003e\n\u003cp\u003eManufacturing systems are complex and require decision-making skills and analytical analysis. Managers and practitioners use a wide variety of methods to optimize the performance of manufacturing systems and control costs. The many processes and functions involved in building and maintaining these systems demand a high-level of knowledge. \u003c/p\u003e\n\u003cp\u003eIn this course, you will learn about these various methods and processes. We will start with a review of probability and statistics, and then cover topics in linear programming, queueing theory, inventory management and the Toyota Production System (TPS). Lastly, we will introduce stochastic manufacturing systems models developed here at MIT. \u003c/p\u003e\n\u003cp\u003eThe topics covered will provide the basis for learners to continue into the manufacturing field in such roles as an operations manager or supply chain manager. \u003c/p\u003e\n\u003cp\u003eDevelop the skills needed for competence and competitiveness in today’s manufacturing industry with the Principles of Manufacturing MicroMasters Credential, designed and delivered by MIT’s #1-ranked Mechan"])</script><script>self.__next_f.push([1,"ical Engineering department in the world. Learners who pass the 8 courses in the program will earn the MicroMasters Credential and qualify to apply to gain credit towards MIT’s Master of Engineering in Advanced Manufacturing \u0026amp; Design program.\u003c/p\u003e3b1:T401,\u003cp\u003eLaTeX, a document preparation system, is widely used for publishing in many scientific fields like mathematics, statistics, computer science, engineering, chemistry, physics, economics, linguistics, etc.. It is a powerful and open-source system that provides numerous facilities for automating typesetting of the document: i.e. structuring page layout, listing and auto-numbering of sections, tables, figures, generating a table of contents, managing cross-referencing, citing, and indexing. \u003c/p\u003e\n\u003cp\u003eUnlike other WYSIWYG editors, the content is written in plain text along with appropriate commands, thus, allowing the user to concentrate on the content rather than the aesthetics (the way it looks). The TeX typesetting program which LaTeX uses, was designed such that anyone can create good quality material with less efforts. \u003c/p\u003e\n\u003cp\u003eThis course introduces the basic concepts of LaTeX. Participants taking this course will be able to create and design documents in LaTeX and presentations in Beamer with confidence.\u003c/p\u003e3b2:T502,\u003cp\u003eThis is an introductory course on options and other financial derivatives, and their applications to risk management. We will start with discrete-time, binomial trees models, but most of the course will be in the framework of continuous-time, Brownian Motion driven models. A basic introduction to Stochastic, Ito Calculus will be given. The benchmark model will be the Black-Scholes-Merton pricing model, but we will also discuss more general models, such as stochastic volatility models. We will discuss both the Partial Differential Equations approach, and the probabilistic, martingale approach. We will also cover an introduction to modeling of interest rates and fixed income derivatives.\u003c/p\u003e\n\u003cp\u003eI teach the same class at Caltech, as an advance"])</script><script>self.__next_f.push([1,"d undergraduate class. This means that the class may be challenging, and demand serious effort. On the other hand, successful completion of the class will provide you with a full understanding of the standard option pricing models, and will enable you to study the subject further on your own, or otherwise. You should have a working knowledge of basic calculus, statistics, and probability and be interested in the use of mathematical modeling. Please go to Unit 0 in the Course Outline to take the prerequisites assessment.\u003c/p\u003e3b3:T4cc,\u003cp\u003eLinear algebra is at the core of all of modern mathematics, and is used everywhere from statistics and data science, to economics, physics and electrical engineering. However, learning the subject is not principally about acquiring computational ability, but is more a matter of fluency in its language and theory.\u003c/p\u003e\n\u003cp\u003eIn this course, we will start with systems of linear equations, and connect them to vectors and vector spaces, matrices, and linear transformations. We will be emphasizing the vocabulary throughout, so that students become comfortable working with the different aspects.\u003c/p\u003e\n\u003cp\u003eWe will then introduce matrix and vector operations such as matrix multiplication and inverses, paying particular attention to their underlying purposes. Students will learn not just how to calculate them, but also why they work the way that they do.\u003c/p\u003e\n\u003cp\u003eWe willdiscuss the key concepts of basis and dimension, which form the foundation for many of the more advanced concepts of linear algebra.\u003c/p\u003e\n\u003cp\u003eThe last chapter concerns inner products, which allow us to use linear algebra for approximating solutions; we will see how this allows for applications ranging from statistics and linear regression to digital audio.\u003c/p\u003e3b4:T605,\u003cp\u003eOur capacity to collect and store data has exponentially increased, but deriving information from data from a scientific perspective requires a foundational knowledge of probability.\u003c/p\u003e\r\n\u003cp\u003eAre you interested in a career in the emerging data science field, or as an act"])</script><script>self.__next_f.push([1,"uarial scientist? Or want better to understand statistical theory and mathematical modeling?\u003c/p\u003e\r\n\u003cp\u003eIn this statistics and data analysis course, we will provide an introduction to mathematical probability to help meet your career goals in the exciting new areas becoming known as information science.\u003c/p\u003e\r\n\u003cp\u003eIn this course, we will first introduce basic probability concepts and rules, including Bayes theorem, probability mass functions and CDFs, joint distributions and expected values.\u003c/p\u003e\r\n\u003cp\u003eThen we will discuss a few important probability distribution models with discrete random variables, including Bernoulli and Binomial distributions, Geometric distribution, Negative Binomial distribution, Poisson distribution, Hypergeometric distribution and discrete uniform distribution.\u003c/p\u003e\r\n\u003cp\u003eTo continue learning about probability, enroll in \u003ca href=\"https://prod-edx-mktg-edit.edx.org/course/probability-distribution-models-purduex-416-2x#!\"\u003eProbability: Distribution Models \u0026amp; Continuous Random Variables\u003c/a\u003e, which covers continuous distribution models, central limit theorem and more.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe Center for Science of Information, a National Science Foundation Center, supports learners by offering free educational resources in information science.\u003c/p\u003e3b5:T6e0,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eCustomer satisfaction is central for the long term survival of any customer oriented business entity. Understanding its target customers, their composition, emotions, requirements, likes and dislikes, are some of the essential enablers for any organization in developing offerings, which will build a brand and ensure its customers’ loyalty. And when we talk of ‘understanding’ we talk of ‘empathy’. So, empathy is defined as the action of understanding, being sensitive and vicariously experiencing the feelings, thoughts and experience of another person. It allows to step in someone else’s shoes and understand the emotions that other is feeling. Empathy can be practised at the organizational level and at individual (delivery point)"])</script><script>self.__next_f.push([1," level. At both levels, it will help to show the customer that they are valued and their concerns matter. \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eAs per a 2020 statistics of US customer service, 89% of the companies compete on the quality of customer service alone and 48% of customers have stopped doing business with a company after poor experience. Hence, the need for providing good customer experience on a sustained basis, cannot be overemphasized. Empathy will help in understanding the customer needs, developing tailor-made products and improving the delivery channels. \u003cspan lang=\"EN-US\"\u003eThus empathy can play an important role in enhancing customer service and can be effectively used as a powerful tool for overall business development. \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe course is designed to provide better understanding of various types of empathy and their application to different situations at work place, to achieve higher levels of customer satisfaction.\u003c/p\u003e3b6:T412,\u003cp\u003eIn this statistics and data analysis course, you will learn about continuous random variables and some of the most frequently used probability distribution models including, exponential distribution, Gamma distribution, Beta distribution, and most importantly, normal distribution.\u003c/p\u003e\r\n\u003cp\u003eYou will learn how these distributions can be connected with the Normal distribution by Central limit theorem (CLT). We will discuss Markov and Chebyshev inequalities, order statistics, moment generating functions and transformation of random variables.\u003c/p\u003e\r\n\u003cp\u003eThis course along with the recommended pre-requisite,\u003ca href=\"https://www.edx.org/course/probability-basic-concepts-discrete-purduex-416-1x\"\u003eProbability: Basic Concepts \u0026amp; Discrete Random Variables\u003c/a\u003e,will you give the skills and knowledge to progress towards an exciting career in information and data science.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe Center for Science of Information, a National Science Foundation Center, supports learners by offering free educational resources in information science.\u003c/p\u003e3b7:T4d1"])</script><script>self.__next_f.push([1,",\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eOne of the most commonly used tools in data science, pandas is a Python library used to load, process, and analyze datasets using SQL-like queries. Pandas offers several advantages, such as data representation, simpler lines of code, and the ability to handle large sets of data. A number of academic and commercial domains, including finance, economics, statistics, web analytics, and other entities, use pandas as part of their data analytics toolkit.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIn this hands-on guided project, you will learn to perform preliminary data analysis and credit risk analysis on a credit card client dataset using the Python library pandas. You will learn how to import required libraries, explore datasets, analyze data, and visualize the dataset. By the end of this project, you will have learned the fundamentals of data analysis using pandas and developed job-ready skills.\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eYou will be provided with access to a Cloud based-IDE which has all of the required software, including Python pandas, pre-installed. All you need is a recent version of a modern web browser to complete this project.\u003c/p\u003e3b8:T4d0,\u003cp\u003eIn this course you will learn to use some mathematical tools that can help predict and analyze sporting performances and outcomes. This course will help coaches, players, and enthusiasts to make educated decisions about strategy, training, and execution. We will discuss topics such as the myth of the Hot Hand and the curse of the Sports Illustrated cover; how understanding data can improve athletic performance; and how best to pick your Fantasy Football team. We will also see how elementary Calculus provides insight into the biomechanics of sports and how game theory can help improve an athlete’s strategy on the field. \u003c/p\u003e\n\u003cp\u003eIn this course you will learn:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eHow a basic understanding of probability and statistics can be used to analyze sports and other real life situations.\u003c/li"])</script><script>self.__next_f.push([1,"\u003e\n\u003cli\u003eHow to model physical systems, such as a golf swing or a high jump, using basic equations of motion.\u003c/li\u003e\n\u003cli\u003eHow to best pick your Fantasy Football, March Madness, and World Cup winners by using ranking theory to help you determine athletic and team performance. \u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBy the end of the course, you will have a better understanding of math, how math is used in the sports we love, and in our everyday lives.\u003c/p\u003e3b9:T682,\u003cp\u003eThe skills and expertise required for a career in finance are in high demand across countless industries. From asset management, to corporations, to official institutions, the career opportunities for qualified finance professionals continue to grow and evolve. For example, demand for financial analysts is predicted to grow at a faster than average rate of 11% through 2026 (\u003ca href=\"https://www.thebalancecareers.com/top-jobs-for-finance-majors-2064048\"\u003eSource\u003c/a\u003e). And according to Glassdoor, the median salary of a quantitative financial analyst was $106,575. (\u003ca href=\"https://www.wallstreetmojo.com/quantitative-financial-analyst/\"\u003eSource\u003c/a\u003e)\u003c/p\u003e\r\n\r\n\u003cp\u003eThe MITx MicroMasters® Program in Finance offers recent graduates, early to mid-stage professionals, and other individuals interested in pursuing a career in finance, an opportunity to advance in the finance field or fast-track an MIT Sloan Master of Finance through a rigorous, comprehensive online curriculum, delivered by the world-renowned MIT Sloan School of Management.\u003c/p\u003e \r\n\r\n\u003cp\u003eDrawn from the STEM-based curriculum taught on campus, all five online courses in this program mirror on-campus graduate-level MIT coursework and cover the following topics: modern finance, financial accounting, mathematical methods for quantitative finance, and derivatives markets. Learners who complete and pass each course in this online program may earn a MicroMasters program certificate in finance, and are considered affiliate members of the MIT Alumni Association. Those learners are eligible to apply to the MIT Sloan Master of Finance and upon acce"])</script><script>self.__next_f.push([1,"ptance, earn credit for the work performed online.\u003c/p\u003e3ba:T420,\u003cp\u003e\u003cimg style=\"max-height:175px\" align=\"right\" alt=\"\" src=\"https://www.edx.org/sites/default/files/edx-prize-logo-2018-nominee.png\"/\u003eGain expertise in the growing field of Supply Chain Management through an innovative online program consisting of five courses and a final capstone exam. The MicroMasters Program in Supply Chain from MITx is an advanced, professional, graduate-level foundation in Supply Chain Management. It represents the equivalent of one semester's worth of coursework at MIT.\u003c/P\u003e \r\n\r\n\u003cp\u003eThe MicroMasters program certificate will showcase your understanding of supply chain analytics, design, technology, dynamics and end-to-end supply chain management. Build on the program certificate and take advantage of a great opportunity to be accepted into the #1 ranked supply chain management Masters Degree program for a fraction of the cost.\u003c/P\u003e\r\n\r\n\u003cp\u003e\u003cb\u003e\u003ci\u003ePlease be advised that certain courses in the program may require online proctoring for assessments. Specific details will be provided at the start of the given course.\u003c/p\u003e\u003c/b\u003e\u003c/i\u003e3bb:T4a7,\u003cp\u003eMaking good business decisions can make or break an entrepreneur. This business and management XSeries, brought to you by Babson College, the #1 school in Entrepreneurship (U.S. News \u0026 World Report), will explore topics such as financial accounting, analytics, customer behavior, and strategy all with the lens of an entrepreneur who has to make critical decisions for the success of the business. \u003c/p\u003e\r\n\u003cp\u003eWhether you are contributing to the strategy and development of your company, starting your own business, or working for a non-profit, this series will prepare you to make better, more informed decisions and contribute to the success and health of the organization. Before we begin with some of these core business concepts, we will define what entrepreneurial thinking is and discover some key methodologies to help with these decisions. \u003c/p\u003e\r\n\u003cp\u003eThis XSeries is targeted at:\u003c/p\u003e\r\n\u003cul\u003e\r\n\u003cli\u003eLearners inter"])</script><script>self.__next_f.push([1,"ested in starting their own business.\u003c/li\u003e\r\n\u003cli\u003eLearners who want to or are asked to contribute to the decisions of any organization.\u003c/li\u003e\r\n\u003cli\u003eLearners who are ready to move forward in his or her organization.\u003c/li\u003e\r\n\u003cli\u003eLearners who are ready to take action!\u003c/li\u003e\r\n\u003c/ul\u003e3bc:T520,\u003cp\u003eIn today’s data-driven landscape, proficiency in data analytics and visualization is indispensable for professionals across industries. These skills equip employees with the ability to meaningfully contribute to an organization’s insights by empowering them to make well-informed decisions and identify key trends. Data-driven insights can streamline operations by identifying bottlenecks, optimizing processes, and reducing inefficiencies.\u003c/p\u003e\r\n\r\n\u003cp\u003eData isn’t only about specific insights though. Looking at data sparks innovation and explores areas of untapped potential. Using a systematic approach to problem-solving, employees can more readily identify root causes of issues in order to enhance transparency and accountability. When decisions are based on data, employees understand the rationale behind choices and can take ownership of their roles.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis self-paced, online certificate consists of four courses that position professionals to understand and present meaningful data. Each course features interactive videos to help you understand both the analytical concepts and the software utilized. You will apply the concepts taught using a separate data source in order to practice and gain the confidence necessary to connect, explore, and analyze data sources into the future.\u003c/p\u003e3bd:T6d5,\u003cp\u003eIn the last two centuries, humanity's average wealth has increased tenfold, while at the same time, child mortality has fallen tenfold. Yet, hundreds of millions of people still earn less than $2 a day, animal cruelty is worse than ever, and risks from climate change, pandemics, and artificial intelligence appear to be on the rise. Many of us care about these issues, but can we do anything about them?\u003c/p\u003e\r\n\r\n\u003cp\u003eThis interdisciplinary progra"])</script><script>self.__next_f.push([1,"m will provide you with a collection of practical tools from economics, public health, behavioral science, ethics, exact sciences, technology, the life sciences, and more to help you achieve a positive and significant impact. From the small decisions we make every day, through the initiatives we choose to support, to our most significant life choices, such as what career to pursue, relying on evidence and analytical methodologies can help you drive a change you believe in.\u003c/p\u003e\r\n\r\n\u003cp\u003eOur program is divided into two courses:\r\nThe first, \u003ca href=\"https://edx.org/learn/ethics/tel-aviv-university-making-a-difference-i-evidence-based-impact\"\u003eMaking a Difference Ⅰ: Evidence-based Impact\u003c/a\u003e, focuses on the foundations of cost-effectiveness, measurement, evaluation, and key questions around who to extend our compassion and concern towards. We’ll also explore some of the causes and opportunities with the strongest evidence for impact.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe second course, \u003ca href=\"https://edx.org/learn/ethics/tel-aviv-university-making-a-difference-ii-high-stakes\"\u003eMaking a Difference Ⅱ: High Stakes\u003c/a\u003e, explores tools for prioritization and assessment when there’s limited information and the stakes are extremely high - including risks to the survival and prosperity of human civilization.\u003c/p\u003e3be:T574,\u003cp\u003eEl curso que se propone es ideal para investigadores y alumnos que se encuentren cursando trabajos de fin de grado, trabajos de fin de máster o realizando tesis, así como todos aquellos del área de la administración que quieran realizar un análisis cuantitativo o cualitativo en sus estudios. El curso pretende acercar al alumno al método científico y, en concreto, cómo éste se viene aplicando al estudio y análisis de los métodos de casos.\u003c/p\u003e\n\u003cp\u003eEl curso pretende construir una base básica-inicial en los principios estadísticos, econométricos y metodológicos generales que permitan a los alumnos desarrollar un análisis completo desde el diseño de la investigación, la preparación de los datos, segmentación y "])</script><script>self.__next_f.push([1,"codificación, hasta la obtención de resultados y respuestas a las preguntas de investigación. El curso enfatiza el método científico y el sistema de análisis de datos guiados por un modelo que pone énfasis en la descripción del punto de vista del participante, la generación entre las unidades de datos y el proceso de interpretación.\u003c/p\u003e\n\u003cp\u003eUnidades:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFuentes de Información para la Investigación\u003c/li\u003e\n\u003cli\u003eManejo de Paquetes Estadísticos\u003c/li\u003e\n\u003cli\u003eModelos Econométricos\u003c/li\u003e\n\u003cli\u003eAnálisis e Interpretación de Datos\u003c/li\u003e\n\u003cli\u003eProcesos de Investigación Cualitativa\u003c/li\u003e\n\u003cli\u003eÉtica en la Investigación\u003c/li\u003e\n\u003cli\u003eExamen Final\u003c/li\u003e\n\u003c/ul\u003e3bf:T710,\u003cp\u003eEste \u003cstrong\u003ecurso en línea\u003c/strong\u003e brinda una introducción al \u003cstrong\u003eanálisis de datos para business intelligence\u003c/strong\u003e. Aprenderás de herramientas y técnicas de estadística descriptiva e inferencial. Serás capaz de analizar data y gráficos para transformarla en información de valor que te permita obtener \u003cstrong\u003ecriterios para la toma de decisiones.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCon este curso podrás \u003cstrong\u003eutilizar datos para cumplir objetivos concretos como\u003c/strong\u003e :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eDescubrir quién es el cliente que representa mayor valor para la empresa\u003c/li\u003e\n\u003cli\u003eIdentificar cómo controlar los gastos\u003c/li\u003e\n\u003cli\u003eIdentificar cómo hacer más rápida la cadena de producción\u003c/li\u003e\n\u003cli\u003eSaber que esperar sobre un producto que acaba de ser lanzado al mercado\u003c/li\u003e\n\u003cli\u003eConocer cómo afecta determinado evento en las ventas\u003c/li\u003e\n\u003cli\u003eSaber cuál es el producto menos rentable para eliminarlo del portafolio\u003c/li\u003e\n\u003cli\u003eIdentificar las mejores epocas para hacer esfuerzos de posicionamiento de determinado producto\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eTendrás dominio de los conceptos básicos y aprenderás a utilizar \u003cstrong\u003eestimadores\u003c/strong\u003e , histogramas, \u003cstrong\u003eescalas de medición\u003c/strong\u003e , varianza, desviación estandar, probabilidad normal, probabilidad informal, valor esperado de una variable aleatoria, \u003cstrong\u003eintervalos de confianza\u003c/strong\u003e , distribución de Poisson y más.\u003c/p"])</script><script>self.__next_f.push([1,"\u003e\n\u003cp\u003eEste es el primer curso del \u003cstrong\u003ePrograma de Certificación Profesional\u003c/strong\u003e \u003cstrong\u003ede Inteligencia de Negocios\u003c/strong\u003e. El segundo curso es sobre \u003cstrong\u003eHerramientas de Business Intelligence\u003c/strong\u003e. Te recomendamos completar ambos para que adquieras conocimiento teórico y experiencia práctica. Tendrás el respaldo de la experiencia de \u003cstrong\u003eJorge Samayoa, Ph.D. por la Universidad de Purdue.\u003c/strong\u003e\u003c/p\u003e3c0:T599,\u003cul\u003e\n\u003cli\u003eCómo realizar un análisis de datos con Excel\u003c/li\u003e\n\u003cli\u003eCómo usar estadística inferencial en empresas\u003c/li\u003e\n\u003cli\u003ePor qué realizar pruebas de hipótesis y cómo hacerlas\u003c/li\u003e\n\u003cli\u003eCómo entender el análisis de regresión y aplicarlo a casos reales\u003c/li\u003e\n\u003cli\u003eCómo estimar intervalos de confianza en una empresa o negocio\u003c/li\u003e\n\u003cli\u003eCómo usar escalas de medición, distribución de frecuencia, representaciones gráficas, datos cualitativos, datos cuantitativos\u003c/li\u003e\n\u003cli\u003eCómo calcular y aplicar las medidas estadísticas de tendencia central\u003c/li\u003e\n\u003cli\u003eDiferencia entre variables aleatorias discretas y continuas, cómo usarlas y cómo determinar un valor esperado\u003c/li\u003e\n\u003cli\u003eAplicar la distribución de probabilidad continua\u003c/li\u003e\n\u003cli\u003eCuáles son los fundamentos de estadística descriptiva y de probabilidad uniforme, exponencial, binomial\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eLo que no puedes dejar pasar\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePracticarás con ejercicios para aplicar la teoría a negocios reales\u003c/li\u003e\n\u003cli\u003eAprenderás a utilizar Excel para utilizar inteligencia de negocios\u003c/li\u003e\n\u003cli\u003eContarás con la guía de un PhD en el campo para recibir conocimiento de valor previamente utilizado y actualizado\u003c/li\u003e\n\u003cli\u003eTendrás acceso a videos guiados para aprender paso a paso y de forma sencilla desde los conceptos más básicos hasta los más especializados\u003c/li\u003e\n\u003cli\u003eAccederás a una red de contactos de profesionales para hacer networking\u003c/li\u003e\n\u003c/ul\u003e3c1:T5e1,\u003cp\u003e¿Quieres ser capaz de valorar de forma analítica a Cristiano Ronaldo o a Fernando Torres usando el Proceso Analítico Jerárquico (AHP), método empleado a nivel mu"])</script><script>self.__next_f.push([1,"ndial para la valoración de todo tipo de activos?\u003c/p\u003e\n\u003cp\u003eEl mundo del deporte mueve miles de millones de euros, muchos de los cuales se invierten en los traspasos de deportistas entre equipos. La valoración de futbolistas es tema complejo en el que influyen muchas variables con relaciones complejas.\u003c/p\u003e\n\u003cp\u003eEn el curso aprenderás a utilizar herramientas que permiten seleccionar de una forma objetiva el mejor jugador para una posición dada o estimar el valor del traspaso de un jugador utilizando las cantidades pagadas por jugadores similares en operaciones recientes.\u003c/p\u003e\n\u003cp\u003eEl contenido del curso es el siguiente:\u003c/p\u003e\n\u003cp\u003eUnidad 1: El mundo del deporte y la valoración\u003cbr /\u003e\nUnidad 2: El proceso analítico jerárquico (AHP)\u003cbr /\u003e\nUnidad 3: Aplicación de AHP a la valoración de deportistas\u003cbr /\u003e\nUnidad 4: Ejemplos\u003c/p\u003e\n\u003cp\u003eEste curso está actualmente en modo autónomo o “self-paced”. ¿Qué significa esto? Que puedes empezarlo cuando quieras y seguirlo a tu ritmo ya que no hay fecha prevista de cierre y cada 6-8 semanas se generarán certificados a aquellos que lo hayan superado. Por otro lado los profesores participarán algo menos en los foros, seguirás teniendo soporte por su parte pero es posible que tarde algo más en contestar tus dudas. \u003c/p\u003e\n\u003cp\u003eThis course is taught in Spanish with English subtitles.\u003c/p\u003e3c2:T46f,\u003cp\u003eIn the first half of this course, we'll investigate DNA replication, and ask the question, where in the genome does DNA replication begin? You will learn how to answer this question for many bacteria using straightforward algorithms to look for hidden messages in the genome. \u003c/p\u003e\r\n\u003cp\u003eIn the second half of the course, we'll examine a different biological question, and ask which DNA patterns play the role of molecular clocks. The cells in your body manage to maintain a circadian rhythm, but how is this achieved on the level of DNA? Once again, we will see that by knowing which hidden messages to look for, we can start to understand the amazingly complex language of DNA. Perhaps surprisingly, "])</script><script>self.__next_f.push([1,"we will apply randomized algorithms to solve problems. \u003c/p\u003e\r\n\u003cp\u003eFinally, you will get your hands dirty and apply existing software tools to find recurring biological motifs within genes that are responsible for helping Mycobacterium tuberculosis go \"dormant\" within a host for many years before causing an active infection. \u003c/p\u003e\r\n\u003cp\u003eThis course begins a series of classes illustrating the power of computing in modern biology.\u003c/p\u003e3c3:T477,\u003cp\u003eAlgorithmics and programming are fundamental skills for engineering students, data scientists and analysts, computer hobbyists or developers.\u003c/p\u003e\n\u003cp\u003eLearning how to program algorithms can be tedious if you aren’t given an opportunity to immediately practice what you learn. In this course, you won't just focus on theory or study a simple catalog of methods, procedures, and concepts. Instead, you’ll be given a challenge wherein you'll be asked to beat an algorithm we’ve written for you by coming up with your own clever solution.\u003c/p\u003e\n\u003cp\u003eTo be specific, you’ll have to work out a route faster than your opponent through a maze while picking up objects.\u003c/p\u003e\n\u003cp\u003eEach week, you will learn new material to improve your artificial intelligence in order to beat your opponent. This structure means that as a learner, you’ll confront each abstract notion with a real-world problem.\u003c/p\u003e\n\u003cp\u003eWe’ll go over data-structures, basic and advanced algorithms for graph theory, complexity/accuracy trade-offs, and even combinatorial game theory.\u003c/p\u003e\n\u003cp\u003eThis course has received financial support from the Patrick and Lina Drahi Foundation.\u003c/p\u003e3c4:T429,\u003cp\u003eWith the continuous generation of massive amounts of biomedical data on a daily basis, whether from research laboratories or clinical labs, we need to improve our ability to understand and analyze the data in order to take full advantage of its power in scientific discoveries and patient care. For non-bioinformaticians, “handling” big data remains a daunting task. This course was designed to facilitate the understanding, analysis, and interpret"])</script><script>self.__next_f.push([1,"ation of biomedical big data to those in the biomedical field with limited or no significant experience in bioinformatics. The goal of this course is to “demystify” the process of analyzing biomedical big data through a series of lectures and online hands-on training sessions and demos. You will learn how to use publicly available online resources and tools for genomic, transcriptomic, and proteomic data analysis, as well as other analytic tools and online resources. This course is funded by a research grant from the US National Institutes of Health (NIH)-Big Data to Knowledge (BD2K) Initiative.\u003c/p\u003e3c5:T94b,"])</script><script>self.__next_f.push([1,"\u003cp\u003eEl mercado laboral actual demanda \u003cstrong\u003ehabilidades de inteligencia de negocios\u003c/strong\u003e. Para ello, te guiaremos a través de dos cursos en los que aprenderás \u003cstrong\u003ecómo cumplir objetivos empresariales \u003c/strong\u003ecomo: reducir costos, analizar datos, optimizar operaciones, anticipar demanda, tomar decisiones acertadas y obtener información valiosa de tus clientes. Todo, a partir de data que habrás sabido extraer, recolectar, integrar, depurar y analizar. Podrás integrar datos a repositorios y convertirlos en información de valor. Sabrás cómo usar herramientas y software de business intelligence. \u003c/p\u003e\r\n\r\n\u003cp\u003eEste \u003cstrong\u003e Programa de Certificación Profesional en Business Intelligence \u003c/strong\u003e está compuesto de dos cursos desarrollados por \u003cstrong\u003e Jorge Samayoa, Ph.D. por la Universidad de Purdue \u003c/strong\u003e. Tendrás el respaldo de su experiencia como consultor en big data, Director de la Maestría de Investigación de Operaciones en Universidad Galileo y docente universitario.\u003c/p\u003e\r\n\r\n\u003cp\u003eEl primer curso online de \u003cstrong\u003eestadística aplicada a negocios \u003c/strong\u003ete dará acceso a videos, ejemplos profesionales y ejercicios prácticos. Te permitirá conocer \u003cstrong\u003e criterios para tomar decisiones \u003c/strong\u003ea partir del análisis de datos e información relevante. Iniciarás utilizando herramientas como \u003cstrong\u003eExcel \u003c/strong\u003epara el análisis de datos y serás capaz de utilizar probabilidad, de trabajar con estimadores, de utilizar econometría y de realizar análisis regresional.\u003c/p\u003e\r\n\r\n\u003cp\u003eEn el segundo \u003cstrong\u003ecurso \u003c/strong\u003e aprenderás sobre decision making: \u003cstrong\u003ecómo tomar decisiones empresariales a partir de datos. \u003c/strong\u003eSerás capaz de utilizar sistemas para recolectar datos (OLAP), modelos para tomar de decisiones, sistemas de soporte para la toma de decisiones (DSS), buenas prácticas de inteligencia de negocios, herramientas y software. Podrás utilizar \u003cstrong\u003ePower BI como herramienta de inteligencia de negocios \u003c/strong\u003e. Tendrás acceso a tutoriales guiados y casos prácticos aplicados a negocios para reforzar tu conocimiento.\u003c/p\u003e\r\n\r\n\u003cp\u003eEste es tu momento de \u003cstrong\u003eavanzar profesional. \u003c/strong\u003e Anímate a inscribirte en los dos cursos del \u003cstrong\u003e Programa de Certificación Profesional en Inteligencia de Negocios \u003c/strong\u003e. Notarás un \"antes\" y un \"después\" en tu carrera profesional.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3c6:T68d,\u003cp\u003eThis is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; neural networks and deep learning; survival models; multiple testing. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).\u003c/p\u003e\n\u003cp\u003eThis is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data science. Computing in this course is done in Python. There are lectures devoted to Python, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chatper. We also offer the separate and original version of this course called \u003ca href=\"https://www.edx.org/learn/statistics/stanford-university-statistical-learning\" rel=\"noopener\" target=\"_blank\"\u003eStatistical Learning with R\u003c/a\u003e – the chapter lectures are the same, but the lab lectures and computing are done using R.\u003c/p\u003e\n\u003cp\u003eThe lectures cover all the material in An Introduction to Statistical Learning, with Applications in Python by James, Witten, Hastie, Tibshirani, and Taylor (Springer, 2023. The pdf for this book is available for free on the \u003ca href=\"https://www.statlearning.com/\"\u003ebook website\u003c/a\u003e.\u003c/p\u003e3c7:T68d,\u003cp\u003eThis is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines"])</script><script>self.__next_f.push([1," and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; neural networks and deep learning; survival models; multiple testing. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).\u003c/p\u003e\n\u003cp\u003eThis is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data science. Computing is done in R. There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter. We also offer a separate version of the course called \u003ca href=\"https://www.edx.org/learn/data-analysis-statistics/stanford-university-statistical-learning-with-python\" rel=\"noopener\" target=\"_blank\"\u003eStatistical Learning with Python\u003c/a\u003e – the chapter lectures are the same, but the lab lectures and computing are done using Python.\u003c/p\u003e\n\u003cp\u003eThe lectures cover all the material in An Introduction to Statistical Learning, with Applications in R (second addition) by James, Witten, Hastie and Tibshirani (Springer, 2021). The pdf for this book is available for free on the \u003ca href=\"https://www.statlearning.com/\"\u003ebook website\u003c/a\u003e.\u003c/p\u003e3c8:T47c,\u003cp\u003eLinear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding. This course, part ofour\u003ca href=\"https://www.edx.org/professional-certificate/harvardx-data-science\"\u003eProfessional Certificate Program in Data Science\u003c/a\u003e, covers how to implement linear regression and adjust for confounding in practice using R. \u003c/p\u003e\n\u003cp\u003eIn data science applications, it is very common to be interested in the relationship between two or more variables. The motivating case study we examine in this course relates to the data-driven approach used to construct baseball teams described in Moneyball. We will try to determine which measured outcomes best predict"])</script><script>self.__next_f.push([1," baseball runs by using linear regression. \u003c/p\u003e\n\u003cp\u003eWe will also examine confounding, where extraneous variables affect the relationship between two or more other variables, leading to spurious associations. Linear regression is a powerful technique for removing confounders, but it is not a magical process. It is essential to understand when it is appropriate to use, and this course will teach you when to apply this technique.\u003c/p\u003e3c9:T429,\u003cp\u003eIdentifying effective policies is a process of trial and error, innovation and experimentation, success and failure. This course provides the basic scientific and statistical tools needed to identify whether a policy or program is generating impact. Organized into modules, the course covers topics ranging from the attribution problem to what is meant by statistical significance (margin of error) to the analysis of data generated by a randomized control trial. The course also helps answer practical questions related to impact evaluation, such as how large of a sample is needed and what can be done when compliance with an experimental design is imperfect or when data is missing for part of the sample.\u003c/p\u003e\n\u003cp\u003eThis course was created collaboratively by Georgetown University and the World Bank's Strategic Impact Evaluation Fund with support from the Georgetown Center for New Designs in Learning and Scholarship, Georgetown University Initiative of Innovation, Development and Evaluation (gui2de), and The Open Learning Campus of the World Bank Group.\u003c/p\u003e3ca:T496,\u003cp\u003eEconomic development is a process of trial and error, innovation and experimentation, success and failure. Given the right institutions, some not unfavorable resource endowments, and a bit of luck, incomes can grow, health can improve, and human development can flourish; other times, things don’t turn out so well.\u003c/p\u003e\n\u003cp\u003eGiven the urgency of development challenges, it is imperative that we learn quickly from our mistakes and build robustly on our successes. The hope is that by understanding what kinds of innovations and p"])</script><script>self.__next_f.push([1,"olicies “work” to improve the lives of the deprived and vulnerable, and how they work, we might be better placed to accelerate the process of development more generally. But how can policy makers and international development practitioners be sure they’re “making a difference?”\u003c/p\u003e\n\u003cp\u003eThis course was created collaboratively by Georgetown University and the World Bank's Strategic Impact Evaluation Fund with support from the Georgetown Center for New Designs in Learning and Scholarship, Georgetown University Initiative of Innovation, Development and Evaluation (gui2de), and The Open Learning Campus of the World Bank Group.\u003c/p\u003e3cb:T56f,\u003cp\u003eIn this course, you will learn about some of the advanced skills you will need for real-world healthcare data analysis. You will continue to practice these skills using the statistical programming software called R and examples from the healthcare industry. The topics covered in this course will help you to engage in the more advanced data wrangling that is often necessary for data analysis and to make data-informed decisions in the healthcare field. While the course focuses on application and the use of these statistical methods, there is some discussion of the mathematical underpinning, relevant formulae, and assumptions necessary for understanding the application of statistical methods. \u003c/p\u003e\n\u003cp\u003eThis self-paced course is comprised of written content, video content, step-by-step follow-along activities, and assessments to reinforce your learning (Assessments available to Verified Track learners only). \u003c/p\u003e\n\u003cp\u003eThe course is comprised of 6 modules that you should complete in order, as each subsequent module builds on the previous one. \u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eModule 1: Causal Inference and Tools for Model Specification\u003c/li\u003e\n\u003cli\u003eModule 2: Matching to Reduce Model Dependence\u003c/li\u003e\n\u003cli\u003eModule 3: Simpson's Paradox and Fixed Effects\u003c/li\u003e\n\u003cli\u003eModule 4: Random Effects\u003c/li\u003e\n\u003cli\u003eModule 5: Repeated Measures and Longitudinal Data\u003c/li\u003e\n\u003cli\u003eModule 6: Missing Data and Bootstrapping\u003c/li\u003e\n\u003c/ul\u003e3cc:T4"])</script><script>self.__next_f.push([1,"a7,\u003cp\u003eIn this course, you will begin learning about more advanced multivariate statistical methods that are regularly used in healthcare data analysis. You will also practice applying these statistical methods to examples from the healthcare industry. The topics covered in this course will prepare you for interpreting data and making data-informed decisions in real-world healthcare settings. While the course focuses on application and the use of these statistical methods, there is some discussion of the mathematical underpinning, relevant formulae, and assumptions necessary for understanding the application of statistical methods. \u003c/p\u003e\n\u003cp\u003eThis self-paced course is comprised of written content, video content, step-by-step follow-along activities, and assessments to reinforce your learning (Assessments available to Verified Track learners only). \u003c/p\u003e\n\u003cp\u003eThe course is comprised of 4 modules that you should complete in order, as each subsequent module builds on the previous one. \u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eModule 1: Non-Linear Trends \u003c/li\u003e\n\u003cli\u003eModule 2: Interacting Variables and Finding Outliers \u003c/li\u003e\n\u003cli\u003eModule 3: Logistic Regression \u003c/li\u003e\n\u003cli\u003eModule 4: Logistic Regression Variants\u003c/li\u003e\n\u003c/ul\u003e3cd:T4b5,\u003cp\u003eIn this course, you will develop a working knowledge of linear relationship data in healthcare and practice using R statistical programming to analyze this data. You will learn about some of the most common univariate and multivariate statical methods used in healthcare data analysis and practice applying them in a statistical software package. While the course focuses on application and the use of these statistical methods, there is some discussion of the mathematical underpinning, relevant formulae, and assumptions necessary for understanding the application of statistical methods.\u003c/p\u003e\n\u003cp\u003eThis self-paced course is comprised of written content, video content, step-by-step follow-along activities, and assessments to reinforce your learning (Assessments available to Verified Track learners only).\u003c/p\u003e\n\u003cp\u003eThe course is comprised of 4"])</script><script>self.__next_f.push([1," modules that you should complete in order, as each subsequent module builds on the previous one.\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eModule 1: Introduction to Correlation and Linear Relationships\u003c/li\u003e\n\u003cli\u003eModule 2: Simple OLS Linear Regression\u003c/li\u003e\n\u003cli\u003eModule 3: Dummy Variables and Multiple OLS Linear Regression\u003c/li\u003e\n\u003cli\u003eModule 4: OLS Linear Regression Diagnostic Tests\u003c/li\u003e\n\u003c/ul\u003e3ce:T7d3,\u003cp\u003eHow to process big data is an ongoing challenge facing machine learning. Currently, the problem of machine learning processing large-scale data is very common. How to propose a machine learning algorithm that meets the needs of big data processing is a hot research topic in the era of big data. The \"Big Data Machine Learning\" course is a basic theoretical course for senior undergraduates and graduate students in the Department of Information Science. Its purpose is to train students to comprehensively understand the theoretical basis of big data machine learning and firmly master the methods and solutions of big data machine learning. Ability to solve practical problems. This course mainly studies machine learning and deep learning methods, aiming to realize the application of big data machine learning. The main contents of this course include:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eBasic theory of statistical learning\u003c/li\u003e\n\u003cli\u003e.Basic methods of machine learning\u003c/li\u003e\n\u003cli\u003eDeep learning theories and methods\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eAn ongoing challenge for machine learning is how to deal with big data. At present, the problem of machine learning dealing with large-scale data is widespread. How to propose a machine learning algorithm to meet the needs of big data processing is a hot research topic in the big data era. The course \" Big Data Machine Learning\" is a basic theory course for senior undergraduates and postgraduates in information science department. Its purpose is to cultivate students' comprehensive ability to understand the theoretical basis of Big Data Machine Learning, master the methods of Big Data Machine Learning firmly, and solve practical problems. This course focuses "])</script><script>self.__next_f.push([1,"on the methodsof machine learning and deep learning, and aims to realize the application of big data machine learning. The main contents of the course include:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eThe basic theories of statistical learning\u003c/li\u003e\n\u003cli\u003eThe basic methods of machine learning\u003c/li\u003e\n\u003cli\u003eThe theories and methods of deep learning\u003c/li\u003e\n\u003c/ol\u003e3cf:T747,The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic modeling and the related field of statistical inference are the keys to analyzing data and making scientifically sound predictions.\u003cbr /\u003e\u003cbr /\u003eThis course is part of a 2-part sequence on the basic tools of probabilistic modeling. Topics covered in this course include:\u0026nbsp;\u0026nbsp;\u003cbr /\u003e\r\n\u003cul\u003e\r\n\u003cli\u003elaws of large numbers\u003c/li\u003e\r\n\u003cli\u003ethe main tools of Bayesian inference methods\u003c/li\u003e\r\n\u003cli\u003ean introduction to classical statistical methods\u003c/li\u003e\r\n\u003cli\u003ean introduction to random processes (Poisson processes and Markov chains)\u003c/li\u003e\r\n\u003c/ul\u003e\r\n\u003cbr /\u003eThis course is a follow-up to Introduction to Probability: Part I - The Fundamentals, which introduced the general framework of probability models, multiple discrete or continuous random variables, expectations, conditional distributions, and various powerful tools of general applicability. The contents of the two parts of the course are essentially the same as those of the corresponding MIT class, which has been offered and continuously refined over more than 50 years. It is a challenging class, but will enable you to apply the tools of probability theory to real-world applications or your research.\u003cbr /\u003e\u003cbr /\u003eProbabilistic models use the language of mathematics. But instead of relying on the traditional \"theorem - proof\" format, we develop the material in an intuitive - but still rigorous and mathematically precise - manner. Furthermore, while the applications are multiple and evident, we emphasize the basic concepts and methodologies that are universally applicable.\u003cbr /\u003e\u003cbr /\u003e\u003cem\u003ePhoto by Pablo Ruiz M\u0026uacute;zqu"])</script><script>self.__next_f.push([1,"iz on Flickr.\u0026nbsp;(\u003ca href=\"https://creativecommons.org/licenses/by-nc-sa/2.0/\" target=\"_blank\" rel=\"noopener noreferrer\"\u003eCC BY-NC-SA 2.0\u003c/a\u003e)\u003c/em\u003e3d0: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 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\u003e3d1:T51a,\u003cp\u003eIn the information age, data is all around us. Within this data are answers to compelling questions across many societal domains (politics, business, science, etc.). But if you had access to a large dataset, would you be able to find the answers you seek?\u003c/p\u003e\n\u003cp\u003eThis course, part of the Data Science MicroMasters program, will introduce you to a collection of powerful, open-source, tools needed "])</script><script>self.__next_f.push([1,"to analyze data and to conduct data science. Specifically, you'll learn how to use:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003epython\u003c/li\u003e\n\u003cli\u003ejupyter notebooks\u003c/li\u003e\n\u003cli\u003epandas\u003c/li\u003e\n\u003cli\u003enumpy\u003c/li\u003e\n\u003cli\u003ematplotlib\u003c/li\u003e\n\u003cli\u003egit\u003c/li\u003e\n\u003cli\u003eand many other tools.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou will learn these tools all within the context of solving compelling data science problems.\u003c/p\u003e\n\u003cp\u003eAfter completing this course, you'll be able to find answers within large datasets by using python tools to import data, explore it, analyze it, learn from it, visualize it, and ultimately generate easily sharable reports.\u003c/p\u003e\n\u003cp\u003eBy learning these skills, you'll also become a member of a world-wide community which seeks to build data science tools, explore public datasets, and discuss evidence-based findings. Last but not least, this course will provide you with the foundation you need to succeed in later courses in the Data Science MicroMasters program.\u003c/p\u003e3d2:T720,\u003cp\u003eDesigned for anyone writing their first academic research project, \u003cem\u003eAcademic English: How to Write a Thesis\u003c/em\u003e guides you through the process of becoming an academic research writer into producing your first academic research publication. Created by experts in academic writing, the course spans 5 modules covering what it means to be an academic writer, getting ideas for research, finding and sourcing relevant literature, appropriately reporting research methods and results according to disciplinary expectations, and developing critical insights into your research findings. A world of academic language and expertise is at your fingertips in \u003cem\u003eAcademic English\u003c/em\u003e!\u003c/p\u003e\n\u003cp\u003eThis ground-breaking course covers a wealth of useful ideas, tips and language features focusing on each step of the research publication process. Featuring a range of exemplars from high-quality published research sources, and insightful commentary from disciplinary experts across the arts \u0026amp; humanities, social sciences, physical and life sciences, \u003cem\u003eAcademic English\u003c/em\u003e takes you through the key stages of planning, drafting, writin"])</script><script>self.__next_f.push([1,"g and revising a research thesis, dissertation or journal article. We cover the main structures and language features of each written section including the introduction, literature review, methodology, results, discussion and conclusion, with an array of individual and peer activities designed to bridge the gap between what you know and what you can do as a writer. Reflective course assessments test your knowledge of critical disciplinary academic content while guiding you through the academic writing process.\u003c/p\u003e\n\u003cp\u003eStart today! Join us on \u003cem\u003eAcademic English: How to Write a Thesis\u003c/em\u003e and take the first step on your journey into becoming a successful academic writer with us!\u003c/p\u003e3d3:Ta1a,"])</script><script>self.__next_f.push([1,"\u003cp\u003eModule 1: Becoming an academic writer\u003c/p\u003e\n\u003cp\u003eWhere to begin as an academic research writer? How can you take an idea and turn it into a research thesis or article? This module explores what it means to be an academic research writer in the 21st Century, covering concepts such as identifying current or future problems for research, challenging established facts or beliefs, and understanding and replicating existing research in your discipline. We also explore expert opinions on starting the academic writing process, and the different forms in which academic research writing may appear.\u003c/p\u003e\n\u003cp\u003eModule 2: Understanding academic language and conventions\u003c/p\u003e\n\u003cp\u003eAcademic writing is the means of communicating with members of your discipline or research area. It is therefore important to learn how academics communicate with each other in written form, the language that makes academic writing different from other kinds of writing, and the conventions of citation and referencing used in your particular field of research. This module covers these ideas and more while presenting some useful tools that can help you bridge the gap between non-academic and academic writing forms.\u003c/p\u003e\n\u003cp\u003eModule 3: Planning and writing the introduction and the literature review\u003c/p\u003e\n\u003cp\u003eDespite some differences, the structure, language and processes used in writing the introduction and literature review sections of a research thesis or article are remarkably similar across disciplines. This module explores how writers can establish their research territory, present the gaps in current knowledge, and formulate their goals and aims, before considering how the work of others can be integrated into your own arguments and stance.\u003c/p\u003e\n\u003cp\u003eModule 4: Exploring the method and results sections\u003c/p\u003e\n\u003cp\u003eThis module covers key aspects of reporting research methods and data types. We explain how to report participant samples, research instruments, experimental procedures and research design, before covering how to report a range of qualitative and quantitative data types includings interview and observation data, surveys, statistical tests, tables and charts.\u003c/p\u003e\n\u003cp\u003eModule 5: Discussing and concluding your findings\u003c/p\u003e\n\u003cp\u003eNow the research is complete, what does it all mean? This final module helps you find and select the key points of your research findings so that you can summarise what it all means for the academic reader in your discipline or research area. We cover the art of making claims about your findings while considering your study's limitations and ideas for future research.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3d4:T723,\u003cp\u003e\u003cspan lang=\"EN-US\"\u003ePeople metrics and analytics is the application of statistical techniques into HR data elements using key measures or indicators to examine the performance of the HR function and employees, and to subsequently disclose the value of people’s intangible assets in the integrated reports as part of the 6 Capitals. \u003c/span\u003eIn many organizations, HR departments collect data on the various aspects of people management such as recruitment, selection, employee turnover, training, remuneration and performance management but hardly integrate statistical techniques with metrics (measures or indicators) to understand the reasons underlying the pattern of relationships between a given set of variables, and in some cases even predict the cause-effect-relationships. \u003cspan lang=\"EN-US\"\u003eConsequently, decisions regarding the attraction, sourcing, deploying, developing, and retaining of talent are based on intuition and not evidence.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis course will provide participants with insights on how to apply people analytics linked to specific metrics and based on an organizational HR measurement system.\u003cspan lang=\"EN-US\"\u003e Hence a more effective approach is to adopt a structured HR measurement system for determining a business need, identifying a human capital problem, organizing data, analyzing and reporting on the patterns of relationships. This is useful for defining specific metrics to track, analyze and information likely to enable managerial decisions. The course provides v\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003ealue-based metrics, analytics and reporting methods such as internal HR dashboards to visualise people-specific data and external human capital disclosure in the corporate annual reports taking into consideration the international reporting standards.\u003c/span\u003e\u003c/p\u003e3d5:T9dc,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThree innovations are driving the data revolution in medicine. \u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eNext Generation Sequencing, and in particular, the ability to sequence individual genomes at diminishing costs.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eElectronic Medical Records, and our ability to mine, using machine learning techniques, huge datasets of medical records.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eWearable devices, the Web, social networks and crowdsourcing - exemplifying the surprising capacity to collect medical data using non-conventional resources.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eIn order to take advantage of these technologies and participate in the revolution, physicians need a new toolbox that is generally lacking in the medical school curriculum. \u003c/p\u003e\n\u003cp\u003eThis course is a product of a decade of a collaborative effort between researchers from the computational biology program at Bar-Ilan University, and clinicians from Sheba Medical Center to develop and deliver an extended curriculum in genomics and biomedical informatics. The program has been endorsed by the Israeli Medicine Association and Ministry of Health. Here, we present a condensed online course that includes selected topics chosen from the extended program. \u003c/p\u003e\n\u003cp\u003eThis GaBI course on edX presents clinicians and digital health enthusiasts with an overview of the data revolution in medicine, and how to take advantage of it for research and in the clinic. In the scope of this single course, you will not become a bioinformatician, but you will be able to familiarize yourself with the main concepts, tools, algorithms, and databases used in this field, and understand the types of problems that these analysis techniques can help address. \u003c/p\u003e\n\u003cp\u003eThe syllabus covers the main topics of this discipline in a logical order: \u003c/p\u003e\n\u003cp\u003e● Methods used to obtain medical data (genotypic and phenotypic) \u003c/p\u003e\n\u003cp\u003e● Analysis of biological molecules such as DNA, RNA, and proteins using various computational tools from the field of bioinformatics \u003c/p\u003e\n\u003cp\u003e● Use of machine learning and artificial intelligence tools to mine the huge databases of medical information accumulating in Electronic Medical Records (EMRs), the Web, and numerous data science projects in medicine \u003c/p\u003e\n\u003cp\u003e● Analysis of complex interaction networks between DNA, RNA and protein molecules to gain a more holistic and systematic view of biological systems and medical conditions \u003c/p\u003e\n\u003cp\u003e● Practical applications in the clinic and in personalized medicine research, and the use of cutting edge technology to improve health\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3d6:T4b6,\u003cp\u003eLean Six Sigma Black Belt Course Topics: - Basic Quality Principles - Managing Projects and Teams - Identifying the Costs of Poor Quality - The DMAIC Phases - Validating Measurement Systems - Graphical Analysis Tools - Lean Tools - Statistical Analysis Tools - Designing Experiments to Test Improvement Theories - Controlling the Improved Process\u003c/p\u003e\n\u003cp\u003eAfter the completion of the program, Attain Partners | Juran-certified Lean Six Sigma Black Belts will be experts having the ability to: - Develop, coach, and lead cross-functional process improvement team and initiatives - Mentor and advise management on prioritizing, planning, and launching Lean Six Sigma projects - Use, teach, and disseminate Lean Six Sigma tools and methods to green belts, yellow belts, and other team members Participants who successfully complete the course also earn 4 CEUs. Other learning objectives include: - Completion of a Lean Six Sigma project - Mastering the Lean and DMAIC methods for improvement - Mastering both graphical and statistical tools that enable the Lean and DMAIC methods - Obtaining in-depth understanding of the Lean Six Sigma philosophy, theory, strategy, tactics, and quality management tools\u003c/p\u003e3d7:Td77,"])</script><script>self.__next_f.push([1,"\u003cp\u003eEvery employee is involved in business processes to create products or services. The causes of decreasing customer satisfaction and increasing quality costs are often unknown, so derived solutions often only address symptoms. Six Sigma methods and tools enable a systematic solution of typical process problems and lead to sustainable operational excellence.\u003c/p\u003e\n\u003cp\u003eGo from Yellow to Green Belt in this project-based Lean Six Sigma course. With the TUM Yellow Belt, you have mastered the body of knowledge of our Green Belt (according to the American Society for Quality standards). Our Green Belt certification requires the implementation of a Six Sigma project (as recommended by the International Society of Six Sigma Professionals), just as the driving experience is necessary to obtain a driver’s license.\u003c/p\u003e\n\u003cp\u003eTo earn the TUM Lean Six Sigma Green Belt certification, you will implement a predefined standard project on environmental littering. The goal: “Improve the cleanliness of areas around selected places in your hometown and control the sustainability of your measures.” This project topic supports the \u003ca href=\"https://sdgs.un.org/goals\"\u003eUnited Nations Sustainability Goal #11: Sustainable Cities and Communities\u003c/a\u003e. To reach this goal you will drive along the DMAIC, accompanied by a Master Black Belt as co-pilot. The route is determined by our navigation software (sigma guide). We will stop at every important sigma tool, which you will then apply in practice and document in a project storybook. This storybook will demonstrate the operational excellence of and in your work. (Please note: The implementation of company-specific, individually supported business projects for certification are not included in this edX/TUM course)\u003c/p\u003e\n\u003cp\u003eWe will guide you through your improvement project. Each DMAIC phase concludes with a project review by a Lean Six Sigma Master Black Belt. With the feedback on your achieved project results, we keep you on track before you start the next DMAIC phase. You will also participate in weekly live sessions online, where we will discuss the tools and logic of Six Sigma in-depth and answer any questions. The e-book for the course, \u003ca href=\"https://link.springer.com/book/10.1007/978-3-030-31915-1\"\u003eSix Sigma Green Belt Certification Project\u003c/a\u003e, is included in the course price.\u003c/p\u003e\n\u003cp\u003eGreen Belt Certification: Learners will be awarded the TUM Lean Six Sigma Green Belt Certification after completing this course and all of its requirements, including:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePrevious acquisition of the TUM Lean Six Sigma Yellow Belt certificate,\u003c/li\u003e\n\u003cli\u003eApplication of the sigma tools (sigma guide),\u003c/li\u003e\n\u003cli\u003eDocumentation of the results in a project storybook,\u003c/li\u003e\n\u003cli\u003eFive graded reviews of the project storybook along with the DMAIC phases,\u003c/li\u003e\n\u003cli\u003eParticipation in at least 10 open-online-sessions,\u003c/li\u003e\n\u003cli\u003eDelivery of the completed project storybook and the collected data. \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOnce a year we present the \u003cstrong\u003eEnvironment Green Belt Award\u003c/strong\u003e for the best Lean Six Sigma certification project. In addition to the basic certification criteria - a correctly implemented and documented project - an excellent project is required, with a project sponsor and a strong, sustainable reduction of litter at a local hotspot.\u003c/p\u003e\n\u003cp\u003eThis course won the 2021 Runner-Up Award - Blended Learning - Academic Division 2021 of the International E-Learning Association (IELA)\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3d8:T40c,\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eTo identify\u003c/strong\u003e and define suitable Six Sigma project topics.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eTo implement\u003c/strong\u003e a Six Sigma project along with its DMAIC phases with all relevant qualitative and quantitative tools.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eTo evaluate\u003c/strong\u003e the success of a Six Sigma project with process performance indicators and the derived financial benefits.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eTo apply\u003c/strong\u003e all relevant DMAIC tools, for example, Project Definition, Voice of Customer/CtQ's, Project Charter, Process Mapping, Cause \u0026amp; Effect Matrix, Hypotheses, and their Statistical Tests, Root-Cause-Analysis, etc.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cspan lang=\"EN-GB\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eIELA Award 2021 -\u003cspan lang=\"EN-GB\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThis course won the 2021 \u003cem\u003eRunner-Up Award - Blended Learning - Academic Division 2021\u003c/em\u003e of the International E-Learning Association (IELA) { \u003cspan lang=\"EN-GB\"\u003e\u003ca href=\"http://www.ielassoc.org/awards_program/past_winners.html\" title=\"Award\"\u003ehttp://www.ielassoc.org/awards_program/past_winners.html }\u003c/a\u003e\u003c/span\u003e\u003c/p\u003e3d9:T441,\u003cp\u003e\"Basics of Machine Learning\" is designed to provide participants with a comprehensive understanding of the fundamental concepts and tools of machine learning. The course covers key topics such as probability density estimation, linear regression, classification techniques like linear discriminants, logistic regression, and support vector machines, as well as ensemble methods such as bagging and boosting. Additionally, the course introduces the basics of deep neural networks, laying the groundwork for more advanced learning techniques.\u003c/p\u003e\n\u003cp\u003eThroughout the course, students will gain a solid foundation in the fundamental approaches of machine learning. By working on practical exercises, participants will cement their understanding of the techniques covered and gain valuable hands-on experience.\u003c/p\u003e\n\u003cp\u003eBy the end of the course, students will have the knowledge and skills required to confidently utilize machine learning tools and techniques in their own projects, providing a stro"])</script><script>self.__next_f.push([1,"ng foundation for further study or professional development in this rapidly evolving field.\u003c/p\u003e3da:T868,"])</script><script>self.__next_f.push([1,"\u003cp\u003eWe, at RCPE (\u003ca href=\"http://www.rcpe.at\" rel=\"noopener\" target=\"_blank\"\u003eResearch Center Pharmaceutical Engineering\u003c/a\u003e) and TU-Graz (\u003ca href=\"https://www.edx.org/school/tugrazx\" rel=\"noopener\" target=\"_blank\"\u003eGraz University of Technology\u003c/a\u003e), are excited to share our latest expertise and knowledge in pharmaceutical production with you.\u003c/p\u003e\n\u003cp\u003ePharmaceutical production processes and process development are grounded in the scientific field of pharmaceutical engineering, which is a multidisciplinary area of study.\u003c/p\u003e\n\u003cp\u003eTo effectively tackle pharmaceutical development and production processes, it's essential to have a multidisciplinary team collaborating. At RCPE, our scientific team scientists, pharmacists, physicists, chemical engineers, mathematicians, process control engineers, and quality experts, among others. Together, we've worked to create a comprehensive learning experience for you. While many of our colleagues are engaged in research projects supporting international pharmaceutical companies, we, as your teaching team, have taken on the responsibility of imparting this knowledge to you.\u003c/p\u003e\n\u003cp\u003eThis course is tailored for individuals in the pharmaceutical industry or other professionals seeking to deepen their understanding of powder handling and characterization techniques. Specifically focused on solid dosage forms and powders, the course delves into key aspects relevant to their production and processing. Please note that other dosage forms, such as liquids for parental or oral delivery, topical creams, or aerosols, are not covered in this course. Solid dosage forms are emphasized due to their widespread use and inherent complexities. By the course's conclusion, participants will gain proficiency in handling powders and solid dosage forms, as well as strategies to navigate common challenges encountered during manufacturing and processing.\u003c/p\u003e\n\u003cp\u003eThroughout this course, you will gain practical knowledge and insights that can be directly applied to pharmaceutical manufacturing processes, enabling you to enhance product quality, efficiency, and regulatory compliance in your professional endeavors.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3db:T419,\u003cp\u003eYou will learn:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eYou will gain proficiency in handling powders and solid dosage forms, as well as strategies to navigate common challenges encountered during manufacturing and processing.\u003c/li\u003e\n\u003cli\u003eYou will learn about various techniques used to analyze particle characteristics, and how these characteristics impact the behavior of pharmaceutical powders.\u003c/li\u003e\n\u003cli\u003eWe will discuss the principles of direct compression, including powder blend characterization, formulation considerations, and process optimization strategies for achieving robust tablet formulations.\u003c/li\u003e\n\u003cli\u003eYou will learn the causes and mechanisms of segregation, as well as strategies to mitigate its effects during manufacturing processes.\u003c/li\u003e\n\u003cli\u003eWe will discuss the principles of powder sampling, including sampling techniques, sample preparation, and statistical considerations for sampling in pharmaceutical manufacturing.\u003c/li\u003e\n\u003cli\u003eWe will examine the key properties of tablets, and their significance in pharmaceutical formulation and development.\u003c/li\u003e\n\u003c/ul\u003e3dc:T5a7,\u003cp\u003e\u003cem\u003eThis course is part of the IPSAMOOC project, a joint venture Federica Weblearning - IPSA, the International Political Science Association\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEmile Durkheim, one of the founders of modern empirical social science, once stated that the comparative method is the only one that suits the social sciences. But Descartes hadposited that \"comparaison n'est pas raison,\" which means that comparison is not reason (or theory) by itself. So what's the right answer? \u003c/p\u003e\n\u003cp\u003eThis course provides an introduction and overview of systematic comparative analysis in the social sciences, and shows you how to use this method for constructive explanation and theory building. \u003c/p\u003e\n\u003cp\u003eA major portion of the course is devoted to new approaches and software that have been developed in recent yearsto handle highly complex cases. Such cases includecomparisons of EU member states, Latin American political systems,and particular policy areas. Procedures such as Qualitative Comparati"])</script><script>self.__next_f.push([1,"ve Analysis (QCA) and related methods are able to reduce complexity and to arrive at \"configurational\" solutions based on set theory and Boolean algebra. These are more meaningful in this context thancommonly used, broad-based statistical methods. \u003c/p\u003e\n\u003cp\u003eInthe last section, these methods are contrasted with more common statistical comparative methods at the macro-level. We'll discuss various states or societies and their respective strengths and weaknesses.\u003c/p\u003e3dd:T5e4,\u003cp\u003eIn this course, experts will discuss the options a researcher must consider when embarking on clinical research. What research design should I choose? How do I start the process of getting my research approved? How will I analyze the data I collect? These are all important questions that a researcher faces. \u003c/p\u003e\n\u003cp\u003eWe will discuss the key decisions a researcher needs to make when preparing for and conducting research, as well as tools for data analysis. You will learn what a pragmatic clinical trial is and how to calculate power and sample size for your study. You will also be exposed to more complex study designs sometimes used in pragmatic clinical trials, such as Bayesian and adaptive designs. \u003c/p\u003e\n\u003cp\u003eThis course includes the following 11 lectures: \u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eOverview of Design Options for Pragmatic Clinical Trials\u003c/li\u003e\n\u003cli\u003eOutcome Measures in Clinical Trials\u003c/li\u003e\n\u003cli\u003eNon-inferiority Trials\u003c/li\u003e\n\u003cli\u003eBasic Analytic Methods\u003c/li\u003e\n\u003cli\u003eBasic Power and Sample Size Calculations\u003c/li\u003e\n\u003cli\u003eSMART: Adaptive Treatment Strategies\u003c/li\u003e\n\u003cli\u003eIntroduction to Bayesian Methods\u003c/li\u003e\n\u003cli\u003eBayesian Designs\u003c/li\u003e\n\u003cli\u003eQuasi-Experiment in Health Services Research\u003c/li\u003e\n\u003cli\u003eAdaptive Trial Design\u003c/li\u003e\n\u003cli\u003eLogistics of Clinical Trials\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThis course is intended for anyone interested in comparative effectiveness research (CER) and patient-centered outcomes research (PCOR) methods. \u003c/p\u003e\n\u003cp\u003eThis course is supported by grant number R25HS023214 from the Agency for Healthcare Research and Quality.\u003c/p\u003e3de:T53b,\u003cp\u003eThis course is designed to introduce you to the compl"])</script><script>self.__next_f.push([1,"ex discipline of portfolio management. The course gives you an insight into the practice and theory of portfolio management. You'll be taught from the perspective of allocators who need to balance risk and return.\u003c/p\u003e\n\u003cp\u003eThe course helps address the overwhelming importance of Modern Portfolio Theory, and delves into quantitative measurements of risk and return like the Sharpe ratio. This course also introduces students to the practical world of investment management. Significant course time is spent on understanding the risk/return tradeoffs that are sought by a Portfolio Manager’s client or superior.\u003c/p\u003e\n\u003cp\u003eWe will also review Investment Policy Statements, understand the need for clear and concise communication and the balance of quantitative and qualitative factors when determining investment decisions.\u003c/p\u003e\n\u003cp\u003eFinally, Portfolio Management covers the spectrum of investment options all the way from ETFs to direct venture investments. This course will show you how all these various instruments are capital market tools that a PM can use to achieve a discipline and well thought out investment strategy.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cspan lang=\"EN-CA\"\u003eThis course is part 4 of the New York Institute of Finance’s Portfolio Management Professional Certificate program.\u003c/span\u003e\u003c/em\u003e\u003c/p\u003e3df:T50f,\u003cp\u003eThis course by Imperial College London is designed to help you develop the skills you need to succeed in your A-level maths exams. \u003c/p\u003e\n\u003cp\u003eYou will investigate key topic areas to gain a deeper understanding of the skills and techniques that you can apply throughout your A-level study. These skills include: \u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFluency – selecting and applying correct methods to answer with speed and efficiency\u003c/li\u003e\n\u003cli\u003eConfidence – critically assessing mathematical methods and investigating ways to apply them\u003c/li\u003e\n\u003cli\u003eProblem solving – analysing the ‘unfamiliar’ and identifying which skills and techniques you require to answer questions\u003c/li\u003e\n\u003cli\u003eConstructing mathematical argument – using mathematical tools such as diagrams, graphs, logica"])</script><script>self.__next_f.push([1,"l deduction, mathematical symbols, mathematical language, construct mathematical argument and present precisely to others\u003c/li\u003e\n\u003cli\u003eDeep reasoning – analysing and critiquing mathematical techniques, arguments, formulae and proofs to comprehend how they can be applied\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eOver seven modules, your initial skillset will be extended to give a clear understanding of how background knowledge underpins the A \u003cbr /\u003e\n-level course. You’ll also be encouraged to consider how what you know fits into the wider mathematical world.\u003c/p\u003e3e0:T483,\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eImprove\u003c/strong\u003e fluency and accuracy when using laws of indices and surds in a variety of calculations\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLearn\u003c/strong\u003e how to solve the types of inequalities you'll encounter at A-level and various ways to represent these\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDiscover\u003c/strong\u003e how to divide any polynomial by either a linear or quadratic polynomial\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLearn\u003c/strong\u003e about the information found in different forms of the Cartesian equation of a circle and use these to solve coordinate geometry problems\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eInvestigate\u003c/strong\u003e the main transformations of graphs; translation, enlargement and reflection, and use these transformations to sketch new graphs\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eUnderstand\u003c/strong\u003e the constant acceleration formulae through travel graphs illustration, speed, velocity, distance and displacement against time\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eExplore\u003c/strong\u003e statistical sampling methods and weigh up the advantages and disadvantages of each one\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eLearn\u003c/strong\u003e how to interpret data presented in a variety of forms including box plots, cumulative frequency curves, histograms and bar charts\u003c/li\u003e\n\u003c/ul\u003e3e1:T699,\u003cp\u003eLearn data literacy online using R programming\u003c/p\u003e\n\u003cp\u003eWhat is data literacy and why is it important? In this data literacy course, you will learn how to become data literate. This will be accomplished by performing data analysis, data visualization, and communicating with data, using real datasets and examples that are relevant to "])</script><script>self.__next_f.push([1,"a variety of audiences and academic disciplines. Data is part of every field, but not everyone has had the opportunity to gain the skills necessary to find the data they need and use it in ways that add to their work. Whether you are in public health, healthcare, banking, law, education, graduate school, or a variety of other fields, there is a way to understand and make use of related data. \u003c/p\u003e\n\u003cp\u003eEarn your data literacy certificate online\u003c/p\u003e\n\u003cp\u003eThis free four-week course will give you the opportunity to build and leverage your data skills for upward mobility at any stage in your career. It will take you through the six steps of the data lifecycle, using different case studies and contexts, and teach you how to analyze, manage, and communicate data, working in R to achieve basic R programming competencies. R is a statistical programming language that is a great resource to analyze data, manage data, and visualize data.\u003c/p\u003e\n\u003cp\u003eNo experience is required to learn this in-demand skill. By the end of this data literacy training course, you will be able to identify key principles of data analysis, use critical thinking skills, and become proficient in building powerful visuals. If you are interested in building a career in data analytics, first learning these foundational lessons is vital.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePhoto by NASA on Unsplash\u003c/em\u003e\u003c/p\u003e3e2:Td31,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cspan lang=\"EN-US\"\u003eApply the systematic Lean Six Sigma method and tools to increase the quality and availability of your products and services and lead your process\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e to sustainable operational excellence. \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eWithin the course program of the \u003cstrong\u003eTUM Lean Six Sigma Green Belt for employees,\u003c/strong\u003e you will:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan lang=\"EN-US\"\u003eIdentify, implement, and evaluate an \u003cstrong\u003eindividual Business Project\u003c/strong\u003e of your organization.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"EN-US\"\u003eDrive along the DMAIC phases, \u003cstrong\u003eguided by our digital and personal resources\u003c/strong\u003e.\u003c/span\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eWe will stop at every important Sigma tool, which you will then apply in practice and document in your project storybook. These results will be reviewed at each DMAIC milestone and serve as the basis for individual coaching and your certification. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eWith our digital and personal guidance, we assist you to …\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan lang=\"EN-US\"\u003esuccessfully complete your project\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"EN-US\"\u003ecreate benefits for your organization, significantly exceeding our course fee\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"EN-US\"\u003edevelop the generic competence to identify, implement, and evaluate future projects. \u003c/span\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eDigital Guidance\u003c/span\u003e\u003c/strong\u003e\u003cspan lang=\"EN-US\"\u003e : Our videos, the related eBook, our software sigmaGuide and the tasks introduce every step of your project.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003ePersonal Guidance\u003c/span\u003e\u003c/strong\u003e\u003cspan lang=\"EN-US\"\u003e - we will accompany your project individually in three different formats:\u003c/span\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eDMAIC Phase Reviews:\u003c/span\u003e\u003c/strong\u003e\u003cspan lang=\"EN-US\"\u003e After each of the 5 \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003eDMAIC phases, we will review the results in your project storybook, correct errors, show alternatives, and suggest the next steps.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eProject Coaching:\u003c/span\u003e\u003c/strong\u003e\u003cspan lang=\"EN-US\"\u003e Coaching takes place at defined points in DEFINE, MEASURE, and ANALYSE to guide your project methodically.\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eGreen Belt Lectures:\u003c/span\u003e\u003c/strong\u003e\u003cspan lang=\"EN-US\"\u003e Our online Green Belt lectures reinforce specific topics. Each lecture series consists of 16 one-hour Zoom sessions, held on Tuesdays at 8:00 UTC and 15:00 UTC, here: \u003ca href=\"https://tum-conf.zoom.us/j/96975983511\" rel=\"noopener\" target=\"_blank\"\u003ehttps://tum-conf.zoom.us/j/96975983511 \u003c/a\u003e\u003c/span\u003ePasscode: 122362. Check it out!\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eYou will additionally acquire the basics of digital Lean competence with our interactive process mining module.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003eAre you a student or a job seeker\u003c/span\u003e\u003c/strong\u003e \u003cstrong\u003e\u003cspan lang=\"EN-US\"\u003e?\u003c/span\u003e\u003c/strong\u003e\u003cspan lang=\"EN-US\"\u003e Implement our\u003c/span\u003e \u003cstrong\u003e__\u003cspan lang=\"EN-US\"\u003e predefined\u003c/span\u003e __Sustainability Project\u003c/strong\u003e in our alternative Lean Six Sigma certification course – \u003ca href=\"https://www.edx.org/course/green-belt\" rel=\"noopener\" target=\"_blank\"\u003e\u003cspan lang=\"EN-US\"\u003ehere on edX\u003c/span\u003e\u003c/a\u003e\u003cspan lang=\"EN-US\"\u003e\u003ca href=\"https://www.edx.org/course/green-belt\" rel=\"noopener\" target=\"_blank\"\u003e.\u003c/a\u003e\u003c/span\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3e3:T4b6,\u003cul\u003e\u003cli\u003eHow to make conjectures, construct logical arguments, and justify your reasoning\u003c/li\u003e\n\t\u003cli\u003eThe concept of function in mathematics, function characteristics and properties, and rate of change of functions\u003c/li\u003e\n\t\u003cli\u003eModeling of common relations and functions such as linear, power, exponential, and logarithmic functions using statistical regression and matrix methods\u003c/li\u003e\n\t\u003cli\u003eAn exploration of algebra and geometry, and the connections between the two in analytic geometry\u003c/li\u003e\n\t\u003cli\u003eProperties and applications of exponential and logarithmic functions including exponential growth and decay and The Logistic Function\u003c/li\u003e\n\t\u003cli\u003eThe development of the trigonometric functions and identities along with applications of trigonometry\u003c/li\u003e\n\t\u003cli\u003eLimits and rate of change of functions as a precursor to Calculus\u003c/li\u003e\n\t\u003cli\u003eOther Coordinates Systems – an investigation of parametrization of the plane and the polar coordinate system along with exploration and use of vectors\u003c/li\u003e\n\t\u003cli\u003eSequences and Series including The Method of Induction\u003c/li\u003e\n\t\u003cli\u003eBasic probability and combinatorics used in an investigation and development of the Binomial Theorem and its connections to Pascal’s Triangle\u003c/li\u003e\n\u003c/ul\u003e3e4:T576,\u003cp\u003eThis course by Imperial College London is designed to help you develop the skills you need to succeed in your A-level mathematics exams.You’ll also be encouraged to consider how what you know fits into the wider mathematical world. \u003c/p\u003e\n\u003cp\u003eOver seven modules, covering an introduction to calculus, Newton’s laws and statistical hypothesis testing your initial skillset will be extended to give a clear understanding of how background knowledge underpins the A -level course. \u003c/p\u003e\n\u003cp\u003eYou will investigate key topic areas to gain a deeper understanding of the skills and techniques that you can apply throughout your A-level study. These skills include: \u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFluency – selecting and applying correct methods to answer with speed and efficiency\u003c/li\u003e\n\u003cli\u003eConfidence – critically assessing mathematical method"])</script><script>self.__next_f.push([1,"s and investigating ways to apply them\u003c/li\u003e\n\u003cli\u003eProblem solving – analysing the ‘unfamiliar’ and identifying which skills and techniques you require to answer questions\u003c/li\u003e\n\u003cli\u003eConstructing mathematical argument – using mathematical tools such as diagrams, graphs, logical deduction, mathematical symbols, mathematical language, data handling, construct mathematical argument and present precisely to others\u003c/li\u003e\n\u003cli\u003eDeep reasoning – analysing and critiquing mathematical techniques, arguments, formulae and proofs to comprehend how they can be applied\u003c/li\u003e\n\u003c/ul\u003e3e5:T991,"])</script><script>self.__next_f.push([1,"\u003cp\u003eModern healthcare presents numerous opportunities for error, making it essential for healthcare teams to create a culture of safety and work together to identify and mitigate risks. Sponsored by Stanford University School of Medicine, SafetyQuest offers a series of online CME/CE gaming modules (levels 1-4) designed to provide an immersive learning experience about the underlying causes of patient safety issues. \u003c/p\u003e\n\u003cp\u003eThis problem-solving educational program emphasizes preventing errors in all healthcare settings while aligning care with the key quality aims of the Institute of Medicine. Through interactive gaming scenarios, learners will work to save patients from preventable harm, gain essential quality improvement tools, and develop safety strategies to improve patient outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWho Should Take This Course\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ePhysicians, nurses, and healthcare administrators\u003c/li\u003e\n\u003cli\u003eMedical professionals looking to improve teamwork and patient safety culture\u003c/li\u003e\n\u003cli\u003eClinicians interested in problem-solving and quality improvement in healthcare\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eWho is Eligible for CME/CE Credits and Certificates\u003c/strong\u003e\u003ca href=\"https://med.stanford.edu/cme.html\"\u003e\u003c/p\u003e\n\u003cp\u003eStanford Center for Continuing Medical Education\u003c/a\u003e offers accredited continuing education for interprofessional healthcare team members, especially clinicians (i.e., physicians, nurses, physician assistants, etc.). Be sure to check the accreditation and credit designation stated on the activity page to view the credit type and amount awarded for your profession.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAccreditation\u003c/strong\u003e\u003cbr /\u003e\nIn support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCredit Designation\u003cbr /\u003e\nAmerican Medical Association (AMA)\u003c/strong\u003e\u003cbr /\u003e\nStanford Medicine designates this Enduring Material for a maximum of 2.00 \u003cem\u003eAMA PRA Category 1 Credits TM\u003c/em\u003e. Physicians should claim only the credit commensurate with the extent of their participation in the activity.\u003ca href=\"https://stanford.cloud-cme.com/default.aspx?P=8\u0026EID=48975\" rel=\"noopener\" target=\"_blank\" title=\"(opens in a new window)\"\u003e\u003c/p\u003e\n\u003cp\u003eClick here\u003c/a\u003e for more CME/CE information.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3e6:T5ad,\u003cp\u003eInterrupted time series analysis and regression discontinuity designs are two of the most rigorous ways to evaluate policies with routinely collected data. ITSx comprehensively introduces analysts to interrupted time series analysis (ITS) and regression discontinuity designs (RD) from start to finish, including selection and setup of data sources, statistical analysis, interpretation and presentation, and identification of potential pitfalls.\u003c/p\u003e\n\u003cp\u003eAt the conclusion of the course, students will have all the tools necessary to propose, conduct and correctly interpret an analysis using ITS and RD approaches. This will help them position themselves as a go-to person within their company, government department, or academic department as the technical expert on this topic.\u003c/p\u003e\n\u003cp\u003eITS and RD designs avoid many of the pitfalls associated with other techniques. As a result of their analytic strength, the use of ITS and RD approaches has been rapidly increasing over the past decade. These studies have cut across the social sciences, including:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eStudying the effect of traffic speed zones on mortality\u003c/li\u003e\n\u003cli\u003eQuantifying the impact of incentive payments to workers on productivity\u003c/li\u003e\n\u003cli\u003eAssessing whether alcohol policies reduce suicide\u003c/li\u003e\n\u003cli\u003eMeasuring the impact of incentive payments to physicians on quality of care\u003c/li\u003e\n\u003cli\u003eDetermining whether the use of HPV vaccination influences adolescent sexual behavior\u003c/li\u003e\n\u003c/ul\u003e3e7:T5a6,\u003cp\u003eThis psychology course is all about the relationship between health and behavior. We will examine stress as a concept and learn about its relation to health and psychological adjustment. We will discuss abnormal behavior and how psychologists assess it as well as a wide range of psychological disorders and approaches to their treatment.\u003c/p\u003e\n\u003cp\u003eThis course includes video-based lectures and demonstrations, interviews with real research psychologists and a plethora of practice questions to help prepare you for the AP® Psychology exam.\u003c/p\u003e\n\u003cp\u003eThis is the fifth in a six-c"])</script><script>self.__next_f.push([1,"ourse AP® Psychology sequencethat is designed to prepare you for the AP® Psychology exam.\u003c/p\u003e\n\u003cp\u003eAdditional Courses:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.edx.org/course/apr-psychology-part-1-what-psychology-ubcx-psyc-1x#\"\u003eAP® Psychology - Course 1: What is Psychology?\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.edx.org/course/apr-psychology-part-2-how-brain-works-ubcx-psyc-2x\"\u003eAP® Psychology - Course 2: How the Brain Works\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.edx.org/course/apr-psychology-part-3-how-mind-works-ubcx-psyc-3x\"\u003eAP® Psychology - Course 3: How the Mind Works\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.edx.org/course/apr-psychology-part-4-how-behavior-works-ubcx-psyc-4x\"\u003eAP® Psychology - Course 4: How Behavior Works\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.edx.org/course/apr-psychology-part-6-exam-preparation-ubcx-psyc-6x\"\u003eAP® Psychology - Course 6: Exam Preparation \u0026amp; Review\u003c/a\u003e\u003c/p\u003e3e8:T61e,\u003cp\u003e\u003cstrong\u003e__\u003c/strong\u003e _ \u003cstrong\u003eVisualizing Natural Language Processing\u003c/strong\u003e _ is the second course in the \u003ca href=\"https://courses.edx.org/dashboard/programs/b8d701a5-01e2-4e28-8313-cdf889550314/\"\u003e\u003cstrong\u003e\u003cem\u003eText Analytics with Python\u003c/em\u003e\u003c/strong\u003e professional certificate\u003c/a\u003e (or you can study it as a stand-alone course). Natural language processing (NLP) is only useful when its results are meaningful to humans. This second course continues by looking at how to make sense of our results using real-world visualizations.\u003c/p\u003e\n\u003cp\u003eHow can we understand the incredible amount of knowledge that has been stored as text data? This course is a practical and scientific introduction to text analytics. That means you’ll learn how it works and why it works at the same time.\u003c/p\u003e\n\u003cp\u003eOn the practical side, you’ll learn how to visualize and interpret the output of text analytics. You’ll learn how to create visualizations ranging from word clouds, heatmaps, and line plots to distribution plots, choropleth maps, and facet grids. You’ll work through real case-studies using jupyter notebooks and to visualize the results of machine learning in Python using pac"])</script><script>self.__next_f.push([1,"kages like pandas, matplotlib, and seaborn.\u003c/p\u003e\n\u003cp\u003eOn the scientific side, you’ll learn what it means to understand language computationally. How do word embeddings and topic models relate to human cognition? Artificial intelligence and humans don’t view language in the same way. You’ll see how both deep learning and human beings interact with the meaning that is encoded in language.\u003c/p\u003e3e9: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\u003e3ea:T4c8,\u003cp\u003eThe demand for skilled data science practitioners in industry, academia, and government is rapidly growing. The HarvardX Data Science progra"])</script><script>self.__next_f.push([1,"m prepares you with the necessary knowledge base and useful skills to tackle real-world data analysis challenges. The program covers concepts such as probability, inference, regression, and machine learning and helps you develop an essential skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with Unix/Linux, version control with git and GitHub, and reproducible document preparation with RStudio.\u003c/p\u003e\r\n\r\n\u003cp\u003eIn each course, we use motivating case studies, ask specific questions, and learn by answering these through data analysis. Case studies include: Trends in World Health and Economics, US Crime Rates, The Financial Crisis of 2007-2008, Election Forecasting, Building a Baseball Team (inspired by Moneyball), and Movie Recommendation Systems.\u003c/p\u003e\r\n\r\n\u003cp\u003eThroughout the program, we will be using the R software environment. You will learn R, statistical concepts, and data analysis techniques simultaneously. We believe that you can better retain R knowledge when you learn how to solve a specific problem.\u003c/p\u003e3eb:T54d,\u003cp\u003eIn today's ever-changing business landscape, it is more important than ever for leaders to have an understanding of artificial intelligence and data analytics. AI is expected to drive significant growth and value for the global economy, generated by the companies and countries that leverage it over the coming years. With that in mind, it's critical for leaders, managers, executives, and board members to develop their AI skills and understand how to leverage data to make the right decisions to grow their businesses.\u003c/p\u003e \r\n\r\n\u003cp\u003eThis Professional Certificate in AI and Data Analytics, brought to you by Babson College, the #1 school in Entrepreneurship (U.S. News \u0026 World Report), will give leaders a basic understanding of AI and how autonomous data can be used to make critical business decisions. The courses in this program will give learners the skills, strategies, and tactics to create AI-powered business models and explain how AI will impact t"])</script><script>self.__next_f.push([1,"heir customers, employees, investors, operations, and product/service offerings.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe program will also dive deeper into data analytics to help business leaders understand the fundamental concepts of sound statistical thinking. Key concepts such as understanding variation, perceiving the relative risk of alternative decisions, and pinpointing sources of variation will be highlighted.\u003c/p\u003e3ec:T935,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis certificate program consists of two mini-courses: (1) A Gentle Introduction to Probability; and (2) Random Variables – Great Expectations to Bell Curves.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe first course provides an introduction to basic probability concepts. Our emphasis is on applications in science and engineering, with the goal of enhancing modeling and analysis skills for a variety of real-world problems.\u003c/p\u003e \r\n\r\n\u003cp\u003eIn order to make the course completely self-contained (and to bring back long-lost memories), we’ll start off with Bootcamp lessons to review concepts from set theory and calculus. We’ll then discuss the probability axioms that serve as the basis for all of our subsequent work – what makes probability tick?\u003c/p\u003e \r\n\r\n\u003cp\u003eThe next venues on our tour are the concepts of independence and conditional probability, which allow us to see how the probabilities of different events are related to each other, and how new information can be used to update probabilities. The course culminates in a discussion of Bayes Rule and its various interesting consequences related to probability updates.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe second course discusses properties and applications of random variables.\u003c/p\u003e \r\n\r\n\u003cp\u003eWe’ll begin by introducing the concepts of discrete and continuous random variables. For instance, how many customers are likely to arrive in the next hour (discrete)? What’s the probability that a lightbulb will last more than a year (continuous)?\u003c/p\u003e\r\n\r\n\u003cp\u003eWe’ll learn about various properties of random variables such as the expected value, variance, and moment generating function. This will lead us to a discussion of functions of random variables. Such functions have many uses, including some wonderful applications in computer simulations.\u003c/p\u003e\r\n\r\n\u003cp\u003eIf you enjoy random variables, then you’ll really love joint (two-dimensional) random variables. We’ll provide methodology to extract marginal (one-dimensional) and conditional information from these big boys. This work will enable us to study the important concepts of independence and correlation.\u003c/p\u003e\r\n\r\n\u003cp\u003eAlong the way, we’ll start working with the R statistical package to do some of our calculations and analysis.\u003c/p\u003e\r\n\r\n\u003cp\u003eBy the end of the course, you will have the technology to model and evaluate a variety of real-world systems in which randomness is present.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3ed:T47e,\u003cp\u003eOur Lean Six Sigma Black Belt course begins with self-paced learning to give you a basic understanding of the methodology and is augmented with recorded instructor-led webinars to reinforce learning points. It will qualify you to teach the Lean Six Sigma methodologies, tools and application in all functions and levels at your company. Throughout and after the course learners can also complete a project working 1-on-1 with a Master Black Belt mentor and coach to deliver a completed project for their company. Our typical Black Belt projects yield an average of $250K in savings for the organization. Our course proves it’s value within 3-6 months from the beginning of the training.\u003c/p\u003e\r\n\r\n\u003cp\u003eLean Six Sigma Black Belt Course Topics:\r\n\u003cul\u003e\r\n\u003cli\u003eBasic Quality Principles\u003c/li\u003e\r\n\u003cli\u003eManaging Projects and Teams\u003c/li\u003e\r\n\u003cli\u003eIdentifying the Costs of Poor Quality\u003c/li\u003e\r\n\u003cli\u003eThe DMAIC Phases\u003c/li\u003e\r\n\u003cli\u003eValidating Measurement Systems\u003c/li\u003e\r\n\u003cli\u003eGraphical Analysis Tools\u003c/li\u003e\r\n\u003cli\u003eLean Tools\u003c/li\u003e\r\n\u003cli\u003eStatistical Analysis Tools\u003c/li\u003e\r\n\u003cli\u003eDesigning Experiments to Test Improvement Theories\u003c/li\u003e\r\n\u003cli\u003eControlling the Improved Process\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e3ee:Ta40,"])</script><script>self.__next_f.push([1,"\u003cp\u003eDemand planning has always been an essential process for virtually every company. But due to consumer behavior changes in recent years, improving forecasting has become a priority. That is the reason why Certified Demand Planners have never been in such high demand.\u003c/p\u003e\r\n\r\n\u003cp\u003eDemand management in today’s business environment is a challenging task. Disruptive events like pandemics, economic sanctions, and armed conflicts have a huge impact on both consumer behavior and goods availability. Top companies are currently recruiting Skilled Demand Planners to be able to adapt to the \"new normal\" conditions, and ISCEA’s Certified Forecaster and Demand Planner (CFDP) Program will place you at the center stage.\u003c/p\u003e\r\n\r\n\u003cp\u003eWhether you are looking to start or boost your career as an Internationally Recognized Demand planner - ISCEA’s CFDP certificate will make you stand out from the crowd. The CFDP credential is sought by recruiters worldwide because it demonstrates that you can reduce procurement, inventory control, and replenishment costs while maximizing the value from inventory management, supply chain planning, ERP, statistical forecasting, and other software investments. These goals can be achieved by improving demand forecasting processes and forecast accuracy.\u003c/p\u003e\r\n\r\n\u003cp\u003eWhen you finish this program, you will be able to help companies cope with rapidly changing demand and improve decision-making processes in the context of Strategic Planning, S\u0026OP (Sales and Operations Planning) / IBP (Integrated Business Planning) processes, and Demand-Driven Supply Chain practices. You will also be able to develop seasonal demand forecasting and new product forecasting strategies, as well as implement a successful CPFR (Collaborative Planning, Forecasting, and Replenishment) process from the ground up.\u003c/p\u003e\r\n\r\n\u003cp\u003eWith no prerequisites, the online courses in this Professional Certification Program will provide you with the knowledge and skills highly regarded by top companies looking to increase their profitability and remain competitive.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis Certification Program is offered by the International Supply Chain Education Alliance (ISCEA), a world-renowned and globally recognized developer of internationally recognized certification programs for supply chain professionals, including Certified Supply Chain Manager (CSCM), Certified Supply Chain Analyst (CSCA) and Certified Forecaster and Demand Planner (CFDP).\u003c/p\u003e\r\n\r\n\u003cp\u003eTo become an ISCEA certified professional Forecaster and Demand Planner (CFDP), you must complete all three preparatory courses and successfully pass the CFDP Exam.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3ef:T49f,\u003cp\u003eAs part of our \u003ca href=\"https://www.edx.org/professional-certificate/harvardx-data-science\"\u003eProfessional Certificate Program in Data Science\u003c/a\u003e, this course covers the basics of data visualization and exploratory data analysis. We will use three motivating examples and ggplot2, a data visualization package for the statistical programming language R. We will start with simple datasets and then graduate to case studies about world health, economics, and infectious disease trends in the United States. \u003c/p\u003e\n\u003cp\u003eWe'll also be looking at how mistakes, biases, systematic errors, and other unexpected problems often lead to data that should be handled with care. The fact that it can be difficult or impossible to notice a mistake within a dataset makes data visualization particularly important. \u003c/p\u003e\n\u003cp\u003eThe growing availability of informative datasets and software tools has led to increased reliance on data visualizations across many areas. Data visualization provides a powerful way to communicate data-driven findings, motivate analyses, and detect flaws. This course will give you the skills you need to leverage data to reveal valuable insights and advance your career.\u003c/p\u003e3f0:T4ad,\u003cp\u003eCyberattacks have surged by 71% and are predicted to continue increasing. This alarming statistic highlights the continued demand for cybersecurity professionals. Jumpstart your cybersecurity career with this introductory IBM course, which introduces you to fundamental cybersecurity concepts, threats, and preventive measures. \u003c/p\u003e\n\u003cp\u003eExplore the evolution of cybersecurity and the motivations of the actors behind cyberattacks, including types such as malware, ransomware, and other security threats. You'll also relate critical thinking to common cybersecurity practices and architecture. \u003c/p\u003e\n\u003cp\u003eHear from industry experts regarding their own careers, skills, perspectives, and experiences. Frameworks, standards, and organizations play a fundamental role in cybersecurity, which you will also investigate. \u003c/p\u003e\n\u003cp\u003eThroughout, you’ll buil"])</script><script>self.__next_f.push([1,"d practical knowledge and technical expertise through hands-on labs. Your final project will enable you to effectively demonstrate your understanding of cybersecurity principles. \u003c/p\u003e\n\u003cp\u003eThis course is for anyone who wants to know the basics of cybersecurity and is part of a series designed to help you start a career as a Cybersecurity Analyst.\u003c/p\u003e3f1:T894,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIn this course, we go beyond the calculus textbook, working with practitioners in social, life and physical sciences to understand how calculus and mathematical models play a role in their work.\u003c/p\u003e\n\u003cp\u003eThrough a series of case studies, you’ll learn:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eHow standardized test makers use functions to analyze the difficulty of test questions;\u003c/li\u003e\n\u003cli\u003eHow economists model interaction of price and demand using rates of change, in a historical case of subway ridership;\u003c/li\u003e\n\u003cli\u003eHow an x-ray is different from a CT-scan, and what this has to do with integrals;\u003c/li\u003e\n\u003cli\u003eHow biologists use differential equation models to predict when populations will experience dramatic changes, such as extinction or outbreaks;\u003c/li\u003e\n\u003cli\u003eHow the Lotka-Volterra predator-prey model was created to answer a biological puzzle;\u003c/li\u003e\n\u003cli\u003eHow statisticians use functions to model data, like income distributions, and how integrals measure chance;\u003c/li\u003e\n\u003cli\u003eHow Einstein’s Energy Equation, E=mc2 is an approximation to a more complicated equation.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWith real practitioners as your guide, you’ll explore these situations in a hands-on way: looking at data and graphs, writing equations, doing calculus computations, and making educated guesses and predictions.\u003c/p\u003e\n\u003cp\u003eThis course provides a unique supplement to a course in single-variable calculus. Key topics include application of derivatives, integrals and differential equations, mathematical models and parameters.\u003c/p\u003e\n\u003cp\u003eThis course is for anyone who has completed or is currently taking a single-variable calculus course (differential and integral), at the high school (AP or IB) or college/university level. You will need to be familiar with the basics of derivatives, integrals, and differential equations, as well as functions involving polynomials, exponentials, and logarithms.\u003c/p\u003e\n\u003cp\u003eThis is a course to learn applications of calculus to other fields, and NOT a course to learn the basics of calculus. Whether you’re a student who has just finished an introductory Calculus course or a teacher looking for more authentic examples for your classroom, there is something for you to learn here, and we hope you’ll join us!\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3f2:T456,\u003cp\u003eIn this course, part of our \u003ca href=\"https://www.edx.org/professional-certificate/harvardx-data-science\"\u003eProfessional Certificate Program in Data Science\u003c/a\u003e,you will learn valuable concepts in probability theory. The motivation for this course is the circumstances surrounding the financial crisis of 2007-2008. Part of what caused this financial crisis was that the risk of some securities sold by financial institutions was underestimated. To begin to understand this very complicated event, we need to understand the basics of probability. \u003c/p\u003e\n\u003cp\u003eWe will introduce important concepts such as random variables, independence, Monte Carlo simulations, expected values, standard errors, and the Central Limit Theorem. These statistical concepts are fundamental to conducting statistical tests on data and understanding whether the data you are analyzing is likely occurring due to an experimental method or to chance. \u003c/p\u003e\n\u003cp\u003eProbability theory is the mathematical foundation of statistical inference which is indispensable for analyzing data affected by chance, and thus essential for data scientists.\u003c/p\u003e3f3:T59a,\u003cp\u003eCausal diagrams have revolutionized the way in which researchers ask: What is the causal effect of X on Y? They have become a key tool for researchers who study the effects of treatments, exposures, and policies. By summarizing and communicating assumptions about the causal structure of a problem, causal diagrams have helped clarify apparent paradoxes, describe common biases, and identify adjustment variables. As a result, a sound understanding of causal diagrams is becoming increasingly important in many scientific disciplines.\u003c/p\u003e\n\u003cp\u003eThe first part of this course is comprised of seven lessons that introduce causal diagrams and its applications to causal inference. The first lesson introduces causal DAGs, a type of causal diagrams, and the rules that govern them. The second, third, and fourth lessons use causal DAGs to represent common forms of bias. The fifth lesson uses causal DAGs to represent time-v"])</script><script>self.__next_f.push([1,"arying treatments and treatment-confounder feedback, as well as the bias of conventional statistical methods for confounding adjustment. The sixth lesson introduces SWIGs, another type of causal diagrams. The seventh lesson guides learners in constructing causal diagrams.\u003c/p\u003e\n\u003cp\u003eThe second part of the course presents a series of case studies that highlight the practical applications of causal diagrams to real-world questions from the health and social sciences.\u003c/p\u003e\n\u003cp\u003eProfessor Photo Credit: Anders Ahlbom\u003c/p\u003e3f4:Ta38,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThe study of neuroscience aims to understand the human brain, cognition, the nervous system, and much more. There are many branches of neuroscience, including medical neuroscience, computational neuroscience, and behavioral neuroscience. Studying the human brain can be difficult, so scientists often first use mice in research because of the similarities in neuroanatomy, neurophysiology, and neurobiology. By studying the brains of mice, we gain insight into how the human brain works. This behavioral neuroscience course on edx covers the fundamentals of research involving mice, mice behavior and how it can be used to understand human behavior, and more. Enroll now to:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eUnderstand how to handle laboratory mice responsibly according to federal law by completing animals care and use training.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUnderstand how research on animals must be scientifically justified, humane and ethical, use appropriate research methods, and provide new knowledge.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eCollect behavioral data from mouse videos from compulsive-like, non-compulsive-like, and randomly bred mouse strains; a mouse model of obsessive-compulsive disorder (OCD).\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eObtain competency in using behavioral tests to measure repetitive behaviors in laboratory mice analogous to compulsions in humans.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eEstablish a foundation in using behavioral tests in laboratory mice to be able to confidently learn how to use new tests.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDevelop an ability to analyze behavioral neuroscience data using mice and understand repetitive (compulsive-like) behaviors in animal models.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDevelop an ability to interpret and discuss results in the context of human behavior, and especially psychiatric disorders, and the mouse model of OCD.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eObtain a competency in describing key characteristics of OCD in humans and human behavior as described in the diagnostic and statistical manual of mental disorders (DSM), including obsessive and intrusive thoughts, repetitive behaviors, and hoarding disorder.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDevelop a capability to formulate original research hypotheses as used in the fields of behavioral sciences, neuropsychology, and behavioral neuroscience.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eObtain a competency in describing and discussing how basic research contributes to the animal model of OCD and how it may have the potential to improve human mental health conditions, quality of life and the biological basis of human behavior.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLearners who join this course should be free of objections to using mice in research.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3f5:T67f,\u003cp\u003eAnalytics have revolutionized sport, providing a competitive advantage to organizational decision-making both on and off the field. This course introduces best practices utilized by data analysts in sport business analytics. The course touches on sports data collection, fact finding, visualization, and metrics that guide strategic decision-making in the sport industry. This course will touch on many aspects of the sports industry, including professional sports. You do do not need a comprehensive background in data science, computer science/Python, or machine learning to complete this edX course.\u003c/p\u003e\n\u003cp\u003eThrough the duration of this self-paced, online course, learners will:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eIdentify the concepts and characteristics of sports analytics in the sporting industry, historically and today.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eInterpret aspects of analytics in the sporting industry (e.g., the impact of analytics in sport, player data points, athlete performance data tracking, organizational key performance indicators, etc.)\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eComprehend and engage in critical thinking with analytic topics in the sporting industry.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eObtain a perspective of the growing trend and field of sport analytics.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eGain insight on the strategies, analytical techniques, and concepts used to evaluate players, team performance, and front-office strategies.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eDiscuss topics related to sport analytics\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eUtilize data analysis and statistical analysis techniques to identify problems and propose innovative solutions for improving performance both on the field and in sport management.\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e3f6:T70f,\u003cp\u003eSupply shortages, pandemics, military wars, trade wars, and other disruptive events have a significant impact in both consumer behaviour and product availability. Companies are becoming aware that historical sales data sets might no longer be relevant; and that the customary forecasting methods are not the best for their new current situation. \u003c/p\u003e\n\u003cp\u003eThis is th"])</script><script>self.__next_f.push([1,"e reason why, demand for skilled, critical and flexible Demand Planners with broad perspective is on the rise.\u003c/p\u003e\n\u003cp\u003eIn this course you will be able to decide if previously used forecasting techniques are the right ones for today's \"New Normal\" business environment. You will be capable of forecasting customer demand of different offerings going through different stages in their product life cycles; using causal and judgemental techniques, market research, statistical methods, time series of past sales and most recent customer orders.\u003c/p\u003e\n\u003cp\u003eYou will also be able to separate relevant from non-relevant data, and mitigate the impact of low forecast accuracy in demand planning, inventory management and profitability.\u003c/p\u003e\n\u003cp\u003eBy the end of this course, that is part of the edX Professional Certificate program to become a Certified Forecaster and Demand Planner (CFDP), you will be able to choose the right forecasting method for each data pattern and understand how to improve forecast performance with Machine Learning and Lean Six Sigma principles.\u003c/p\u003e\n\u003cp\u003eCFDP certified professionals are globally preferred by recruiters for decision making positions because they are capable of forecasting both slow and rapidly changing seasonal, intermittent and new product demand.\u003c/p\u003e\n\u003cp\u003eTo become an ISCEA Certified Forecaster and Demand Planner (CFDP), you must complete all three preparatory courses and successfully pass the CFDP Exam.\u003c/p\u003e3f7:Tabc,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis course by Imperial College London is designed to help you develop the skills you need to succeed in your A-level further maths exams.\u003c/p\u003e\n\u003cp\u003eYou will investigate key topic areas to gain a deeper understanding of the skills and techniques that you can apply throughout your A-level study. These skills include:\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e Fluency – selecting and applying correct methods to answer with speed and efficiency\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e Confidence – critically assessing mathematical methods and investigating ways to apply them\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e Problem solving – analysing the ‘unfamiliar’ and identifying which skills and techniques you require to answer questions\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e Constructing mathematical argument – using mathematical tools such as diagrams, graphs, logical deduction, mathematical symbols, mathematical language, construct mathematical argument and present precisely to others\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e Deep reasoning – analysing and critiquing mathematical techniques, arguments, formulae and proofs to comprehend how they can be applied\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eOver eight modules, you will be introduced to \u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e Simple harmonic motion and damped oscillations.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e Impulse and momentum.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e The work done by a constant and a variable force, kinetic and potential energy (both gravitational and elastic) conservation of energy, the work-energy principle, conservative and dissipative forces, power.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e Oblique impact for elastic and inelastic collision in two dimensions. \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e The Poisson distribution, its properties, approximation to a binomial distribution and hypothesis testing.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e The distribution of sample means and the central limit theorem. \u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e Chi-squared tests, contingency tables, fitting a theoretical distribution and goodness of fit.\u003c/span\u003e\u003cspan lang=\"EN-US\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e*\u003cspan lang=\"EN-US\"\u003e Type I and type II errors in statistical tests.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eYour initial skillset will be extended to give a clear understanding of how background knowledge underpins the A -level further mathematics course. You’ll also be encouraged to consider how what you know fits into the wider mathematical world.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3f8:Tb5d,"])</script><script>self.__next_f.push([1,"\u003cp\u003eHow to derive and solve a second order differential equation that models simple harmonic motion.\u003c/p\u003e\n\u003cp\u003eHow to derive a second order differential equation for damped oscillations.\u003c/p\u003e\n\u003cp\u003eThe meaning of underdamping, critical damping and overdamping.\u003c/p\u003e\n\u003cp\u003eHow to solve coupled differential equations.\u003c/p\u003e\n\u003cp\u003eHow to calculate the impulse of one object on another in a collision.\u003c/p\u003e\n\u003cp\u003eHow to use the principle of conservation of momentum to model collisions in one dimension.\u003c/p\u003e\n\u003cp\u003eHow to use Newton’s experimental law to model inelastic collisions in one dimension.\u003c/p\u003e\n\u003cp\u003eHow to calculate the work done by a force and the work done against a resistive force.\u003c/p\u003e\n\u003cp\u003eHow to calculate gravitational potential energy and kinetic energy.\u003c/p\u003e\n\u003cp\u003eHow to calculate elastic potential energy.\u003c/p\u003e\n\u003cp\u003eHow to solve problems in which energy is conserved.\u003c/p\u003e\n\u003cp\u003eHow to solve problems in which some energy is lost through work against a dissipative force.\u003c/p\u003e\n\u003cp\u003eHow to calculate power and solve problems involving power. \u003c/p\u003e\n\u003cp\u003eHow to model elastic collision between bodies in two dimensions.\u003c/p\u003e\n\u003cp\u003eHow to model inelastic collision between two bodies in two dimensions.\u003c/p\u003e\n\u003cp\u003eHow to calculate the energy lost in a collision.\u003c/p\u003e\n\u003cp\u003eHow to calculate probability for a Poisson distribution.\u003c/p\u003e\n\u003cp\u003eHow to use the properties of a Poisson distribution.\u003c/p\u003e\n\u003cp\u003eHow to use a Poisson distribution to model a binomial distribution.\u003c/p\u003e\n\u003cp\u003eHow to use a hypothesis test to test for the mean of a Poisson distribution.\u003c/p\u003e\n\u003cp\u003eHow to estimate a population mean from sample data. \u003c/p\u003e\n\u003cp\u003eHow to estimating population variance using the sample variance. How to calculate and interpret the standard error of the mean.\u003c/p\u003e\n\u003cp\u003eHow and when to apply the Central Limit Theorem to the distribution of sample means. \u003c/p\u003e\n\u003cp\u003eHow to use the Central Limit Theorem in probability calculations, using a continuity correction where appropriate. \u003c/p\u003e\n\u003cp\u003eHow to apply the Central Limit Theorem to the sum of n identically distributed independent random variables.\u003c/p\u003e\n\u003cp\u003eHow to conduct a chi-squared test with the appropriate number of degrees of freedom to test for independence in a contingency table and interpret the results of such a test.\u003c/p\u003e\n\u003cp\u003eHow to fit a theoretical distribution, as prescribed by a given hypothesis involving a given ratio, proportion or discrete uniform distribution, to given data.\u003c/p\u003e\n\u003cp\u003eHow to use a chi-squared test with the appropriate number of degrees of freedom to carry out a goodness of fit test.\u003c/p\u003e\n\u003cp\u003eHow to calculate the probability of making a Type I error from tests based on a Poisson or Binomial distribution. \u003c/p\u003e\n\u003cp\u003eHow to calculate probability of making Type I error from tests based on a normal distribution. \u003c/p\u003e\n\u003cp\u003eHow to calculate P(Type II error) and power for a hypothesis test for tests based on a normal, Binomial or a Poisson distribution (or any other A level distribution).\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3f9:T71a,\u003cp\u003eIn this online course, you will learn the theory behind the design of the Value Added Tax (VAT) gap estimation model of the International Monetary Fund’s Revenue Administration Gap Analysis Program (RA-GAP), and you will also learn how to use the model to produce your own VAT gap estimates. You will become familiarized with the overall structure of the model, and how each of its components interacts. You will learn what the inputs needed for the model are, how to prepare them, and how the model uses these inputs to compute the potential VAT, which is compared to the actual VAT to determine the VAT gap. \u003c/p\u003e\n\u003cp\u003eThe online course comprises five main components, or modules. The first module starts by providing some general background on the concept of tax gaps, and then covers the theory behind the design of the VAT gap estimation model. The second module moves on to looking at the various policy structures of a VAT are to be input into the model. The third module provides instructions on how various measures of actual VAT are to be prepared for use in the model, and the reason for why these measures are needed. The fourth module focusses on the compilation of the statistical data needed to construct the potential VAT base, and how this potential VAT base is input into the model. The final module then returns to the model and demonstrates how to execute the model to obtain your results, and, more importantly, how to review and interpret those results. \u003c/p\u003e\n\u003cp\u003eIn short, this course is designed to enable countries to produce VAT gap estimates on a regular and consistent basis, using the VAT gap estimation model of the IMF’s RA-GAP program; which is a well-established tax gap model. \u003c/p\u003e\n\u003cp\u003eThis online course is offered by the IMF with financial support from the Government of Japan.\u003c/p\u003e3fa:Td93,"])</script><script>self.__next_f.push([1,"\u003cp dir=\"ltr\"\u003e\u003cspan style=\"font-size: 11pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"\u003eMachine learning and AI are rapidly transforming the world, impacting organizations of all sizes. As executives push for AI/ML strategies, DevOps teams have been upskilling and bridging the gap between operations and development for the last several years for traditional applications. The complex machine learning application arrives just as cross-team collaboration becomes familiar.\u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan style=\"font-size: 11pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"\u003e\n\nThese data-dependent applications present fresh challenges for deployment and development, demanding expertise from developers and data scientists, data engineers, and machine learning engineers. How can existing engineers, with their container, Kubernetes, and cloud knowledge, navigate this terrain? Can non-engineers seeking smoother data-intensive projects find common ground with statistically-savvy data scientists? We think so! Enter Kubeflow, an open source, Kubernetes-powered toolkit that enables teams of any scale or maturity to harness the potential of machine learning. Rather than reinventing the wheel, Kubeflow simplifies the deployment of proven open-source ML systems across any cloud and even on-premise \u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan style=\"font-size: 11pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"\u003e\n\nThis course begins with Kubeflow, covering its origins, deployment options, individual components, and standard integrations. By the end, you'll grasp how MLOPs can ensure the successful production of ML systems, how Kubeflow opens up ML for everyone, regardless of scale, understand\u003c/span\u003e\u003cspan style=\"font-size: 11pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: bold; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"\u003e\u003c/span\u003e\u003cspan style=\"font-size: 11pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"\u003e how to choose the ideal Kubeflow distribution for your needs so you can see Kubeflow’s \"simple, portable, scalable\" promise in action, and launch your own Kubeflow project. We will even touch upon some additional open source integrations so you can make Kubeflow work for you!\u003c/span\u003e\u003c/p\u003e\n\u003cp dir=\"ltr\"\u003e\u003cspan style=\"font-size: 11pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"\u003e\n\nThis course caters to everyone wanting to leverage the power of machine learning. Whether you're an engineer, data scientist, or simply curious about Kubeflow, join us and discover how you can contribute to the future of machine learning!\u003c/span\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3fb:Td4c,"])</script><script>self.__next_f.push([1,"\u003cul\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003e\u003cspan style=\"font-size: 11pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"\u003eDiscuss the value of MLOPs for production systems and how it relates to DevOps\u003c/span\u003e\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003e\u003cspan style=\"font-size: 11pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"\u003eRecognize common machine learning platform patterns and the problems they seek to solve\u003c/span\u003e\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003e\u003cspan style=\"font-size: 11pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"\u003eExplain the model development lifecycle \u003c/span\u003e\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003e\u003cspan style=\"font-size: 11pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"\u003eDefine and identify common machine learning frameworks\u003c/span\u003e\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003e\u003cspan style=\"font-size: 11pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"\u003eDiscuss the value proposition and goal of the universal training operator\u003c/span\u003e\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003e\u003cspan style=\"font-size: 11pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"\u003eResearch and select a Kubeflow distribution based on your needs or, at the very least, have an informed conversation with a vendor.\u003c/span\u003e\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003e\u003cspan style=\"font-size: 11pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"\u003eLaunch and leverage a Kubeflow Notebook.\u003c/span\u003e\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003e\u003cspan style=\"font-size: 11pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"\u003eLaunch a primary Kubeflow pipeline.\u003c/span\u003e\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003e\u003cspan style=\"font-size: 11pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"\u003eDiscuss additional popular Kubeflow integrations.\u003c/span\u003e\u003c/p\u003e\u003c/li\u003e\n\u003cli\u003e\u003cp dir=\"ltr\"\u003e\u003cspan style=\"font-size: 11pt; font-family: Arial,sans-serif; color: #000000; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;\"\u003eFamiliarize yourself with Katib and Hyperparameter tuning\u003c/span\u003e\u003c/p\u003e\u003c/li\u003e\n\u003c/ul\u003e"])</script><script>self.__next_f.push([1,"3fc:T56b,\u003cp\u003eViviamo nel \"big data age\", qualunque attività umana, qualsiasi azione, produce dati.\u003cbr /\u003e\nQuesto corso affronta le diverse fasi dell’indagine statistica, dal campionamento statistico all’utilizzo degli strumenti inferenziali per ottenere stime, puntuali o intervallari, e per verificare la plausibilità di ipotesi su una o più caratteristiche della popolazione oggetto di studio.\u003cbr /\u003e\nIn altre parole, lo scopo formativo del corso è di condurre lo studente verso una matura capacità di sintesi e di astrazione nella formalizzazione e nell’analisi dei problemi economico-gestionali attraverso l’impiego degli strumenti di statistica inferenziale ritenuti più adeguati agli obiettivi di analisi e alle caratteristiche dei dati raccolti.\u003c/p\u003e\n\u003cp\u003eToday, we live in a big data age where all human activities and actions produce \u003cbr /\u003e\ndata. In this course, we will trace the steps of statistical surveys: from statistical \u003cbr /\u003e\nsampling, to the inferential tools used for interval and point estimates and for verifying the plausibility of hypotheses on one or more characteristics of the population studied. \u003cbr /\u003e\nIn other words, the scope of this course is to provide the students with a sensible synthetic ability as well as with a mature competence of abstraction when formalizing and analyzing the economic and managerial problems with the most appropriate tools.\u003c/p\u003e3fd:T87e,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cspan lang=\"FR\"\u003eCe cours dispensé par le département des \u003c/span\u003e\u003cspan lang=\"FR\"\u003estatistiques\u003c/span\u003e\u003cspan lang=\"FR\"\u003e du FMI couvre les éléments fondamentaux nécessaires à l’établissement et à la diffusion de statistiques exhaustives sur \u003c/span\u003e\u003cspan lang=\"FR\"\u003ela dette du secteur public (SDSP), qui seront utiles aux responsables de l’élaboration des politiques, aux décideurs et à d’autres utilisateurs\u003c/span\u003e\u003cspan lang=\"FR\"\u003e.\u003c/span\u003e\u003cspan lang=\"FR\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eLe cours introduit le cadre conceptuel nécessaire aux SDSP tel qu’il est présenté dans les \u003cem\u003e\u003cspan lang=\"FR\"\u003eStatistiques de la dette du secteur public — Guide pour les statisticiens et les utilisateurs\u003c/span\u003e\u003c/em\u003e\u003cspan lang=\"FR\"\u003e ( \u003cem\u003eGuide\u003c/em\u003e ) et dans le contexte du cadre des statistiques\u003c/span\u003e\u003cspan lang=\"FR\"\u003e de finances publiques (SFP), qui est harmonisé avec d’autres cadres de \u003c/span\u003e\u003cspan lang=\"FR\"\u003estatistiques\u003c/span\u003e\u003cspan lang=\"FR\"\u003e macroéconomiques. Nous examinons les concepts de base, les définitions et les classifications, de même que les principales normes comptables (y compris la valorisation et la consolidation) pertinentes pour l’établissement des SDSP. \u003c/span\u003e\u003cspan lang=\"FR\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eLe cours aborde la couverture institutionnelle et des instruments de dette à considérer pour produire des SDSP exhaustives et comparables à l’échelle internationale, et explique comment enregistrer les passifs conditionnels tels que les garanties des \u003cspan lang=\"FR\"\u003eadministrations publiques\u003c/span\u003e\u003cspan lang=\"FR\"\u003e. Par ailleurs, il traite de l’incidence des SDSP sur certaines thématiques liées à la dette (reprise de dette, remise de dette, rétrocession, crédit-bail, renflouement, etc.).\u003c/span\u003e\u003cspan lang=\"FR\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eLe cours examine aussi des questions importantes pour l’établissement des SDSP, notamment celle du choix des SDSP à établir et à diffuser, ainsi que les directives et les normes du FMI concernant leur diffusion. Il présente enfin les usages possibles des SDSP, dont les analyses de viabilité de la dette (AVD) et les analyses des risques budgétaires et de la vulnérabilité des finances publiques.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"3fe:T682,\u003cp\u003eLa gestione della produzione e della logistica è una materia di natura multidisciplinare, e questo corso si propone di dare rilievo alle problematiche di natura manageriale, senza per questo sminuire l’importanza degli aspetti più propriamente tecnologici e matematico-statistici.\u003cbr /\u003e\nLa parte iniziale del corso presenta una descrizione dei sistemi di produzione, e delle strategie di produzione e di logistica. Il corso prosegue con le tecniche di previsione di supporto al Manufacturing e successivamente approfondisce il tema della programmazione della produzione, della gestione dei materiali cosiddette a stock e del controllo di qualità. La gestione della logistica e le scelte di outsourcing logistico assumono un ruolo centrale nel corso. Infine, il corso si completa con alcune lezioni monografiche, tenute da manager della produzione, racconti di casi empirici fatti da testimonial d'eccezione, su Kaizen, lean production, ed e-procurement. \u003c/p\u003e\n\u003cp\u003eOperations and logistics management is a multi-disciplinary subject, and this course highlights management issues without neglecting the more technological and mathematical-statistical aspects. \u003cbr /\u003e\nThe first part of the course describes production systems, and production and logistics strategies. It moves on to planning manufacturing support before looking at scheduling production, stock management and quality control in more detail. Logistics management and outsourcing choices play a key role in the course. The course finishes with lessons focusing on special topics, like Kaizen, lean production and e-procurement, where specific case-studies are presented by experts in the field.\u003c/p\u003e3ff:T627,\u003cp\u003ePer fare questo occorre capire l'importanza della metodologia della ricerca, non sempre immediatamente evidente a chi si trova a leggere un saggio sociologico, né tanto meno ai giovani che intraprendono un percorso di studio nel campo delle scienze sociali. Il corso, dunque, affronta il tema della logica del metodo scientifico e della sua applicazione nelle "])</script><script>self.__next_f.push([1,"scienze sociali. L'obiettivo è di consentire agli studenti di impostare e condurre correttamente il lavoro di indagine empirica, nonché di orientare la scelta degli strumenti di raccolta dati in relazione ai diversi tipi di ricerca, fornendo indicazioni circa la loro costruzione e somministrazione. Il corso comprende anche una introduzione alle tecniche di analisi statistiche con cui è possibile trovare risposte agli interrogativi iniziali formulati in sede di disegno\u003cbr /\u003e\ndella ricerca. \u003c/p\u003e\n\u003cp\u003eTo achieve this, we need to understand the importance of research methodology, and this is not immediately obvious to people reading a sociology book, or even to students starting their Social Sciences course. This MOOC, therefore, looks at logic and the scientific approach and how it is applied to the Social Sciences. The objective is to enable students to learn how to set up and implement an empirical study, including the right choice of instruments for data collection according to the specific study, and how to construct and administer them. The course also provides an introduction to statistical analysis which enables us to answer the questions that were set when the research was designed.\u003c/p\u003e400:T417,\u003cp\u003eAnalytical models are key to understanding data, generating predictions, and making business decisions. Without models it’s nearly impossible to gain insights from data. In modeling, it’s essential to understand how to choose the right data sets, algorithms, techniques and formats to solve a particular business problem.\u003c/p\u003e\n\u003cp\u003eIn this course, part of the Analytics: Essential Tools and Methods MicroMasters program, you’ll gain an intuitive understanding of fundamental models and methods of analytics and practice how to implement them using common industry tools like R.\u003c/p\u003e\n\u003cp\u003eYou’ll learn about analytics modeling and how to choose the right approach from among the wide range of options in your toolbox.\u003c/p\u003e\n\u003cp\u003eYou will learn how to use statistical models and machine learning as well as models for:\u003c/p\u003e\n\u003cul\u003e\n\u003c"])</script><script>self.__next_f.push([1,"li\u003eclassification;\u003c/li\u003e\n\u003cli\u003eclustering;\u003c/li\u003e\n\u003cli\u003echange detection;\u003c/li\u003e\n\u003cli\u003edata smoothing;\u003c/li\u003e\n\u003cli\u003evalidation;\u003c/li\u003e\n\u003cli\u003eprediction;\u003c/li\u003e\n\u003cli\u003eoptimization;\u003c/li\u003e\n\u003cli\u003eexperimentation;\u003c/li\u003e\n\u003cli\u003edecision making.\u003c/li\u003e\n\u003c/ul\u003e401:T74d,\u003cp\u003ePsychology is the academic and applied study of the human mind and behavior. Perhaps there are no more salient topics in the information age and the global economy than a comprehensive understanding of how learning takes place and what predicts and determines human behavior. The course is a primer, meant to provide substantive content through which to understand the human condition and to inspire students to continue their learning and growth. \u003c/p\u003e\n\u003cp\u003eStudents interested in the study of psychology are interested in why people do, say and think what they do. They have questions about how learning takes place, how genetics dictate certain traits but not others, how memories are formed, where to draw the line between normality and disorder, whether a damaged brain can regain function and the predictors of addiction. \u003c/p\u003e\n\u003cp\u003eThrough this introductory course, students will have the opportunity to understand the science of psychology and how psychologists measure mental function and behavior and how the results are reported. They will better understand perception and consciousness and the mysteries of sleep and dreams. Through child development, they will understand how the brain is ready for learning and pre-wired for language. Motivation, cognition and personality will be analyzed through the lens of past and current psychological thought. Students will understand the statistical reality of the normal distribution and how that is relevant to most traits. They will review new research on the plasticity of the brain and consider whether intelligence is fixed or flexible. Lastly, the array of disorders and dysfunction will be analyzed and discussed and the line between normal and abnormal will be considered.\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.edx.org/high-school-initiative\"\u003eLearn more about our Hig"])</script><script>self.__next_f.push([1,"h School and AP* Exam Preparation Courses\u003c/a\u003e\u003c/p\u003e402:T6da,\u003cp\u003eThis course provides the essential foundations required to understand the operation of semiconductor devices such as transistors, diodes, solar cells, light-emitting devices, and more. The material will primarily appeal to electrical engineering students whose interests are in applications of semiconductor devices in circuits and systems. The treatment is physical and intuitive, and not heavily mathematical.\u003c/p\u003e\n\u003cp\u003eTechnology users will gain an understanding of the semiconductor physics that is the basis for devices. Semiconductor technology developers may find it a useful starting point for diving deeper into condensed matter physics, statistical mechanics, thermodynamics, and materials science. The course presents an electrical engineering perspective on semiconductors, but those in other fields may find it a useful introduction to the approach that has guided the development of semiconductor technology for the past 50+ years.\u003c/p\u003e\n\u003cp\u003eStudents taking this course will be required to complete two (2) proctored exams using the edX online Proctortrack software. \u003cbr /\u003e\nCompleted exams will be scanned and sent using Gradescope for grading.\u003c/p\u003e\n\u003cp\u003eSemiconductor Fundamentals is one course in a growing suite of unique, 1-credit-hour short courses being developed in an edX/Purdue University collaboration. Students may elect to pursue a verified certificate for this specific course alone or as one of the six courses needed for the edX/Purdue MicroMasters program in Nanoscience and Technology. For further information and other courses offered and planned, please see the Nanoscience and Technology page. Courses like this can also apply toward a Purdue University MSECE degree for students accepted into the full master’s program.\u003c/p\u003e403:T4b4,\u003cp\u003eStudents will learn about the following specific topics:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eenergy bands\u003c/li\u003e\n\u003cli\u003eband gaps\u003c/li\u003e\n\u003cli\u003eeffective masses\u003c/li\u003e\n\u003cli\u003eelectrons and holes\u003c/li\u003e\n\u003cli\u003ebasics of quantum mechanics\u003c/li\u003e\n\u003cli\u003ethe Fermi function\u003c/l"])</script><script>self.__next_f.push([1,"i\u003e\n\u003cli\u003ethe density-of-states\u003c/li\u003e\n\u003cli\u003eintrinsic carrier density\u003c/li\u003e\n\u003cli\u003edoping and carrier concentrations\u003c/li\u003e\n\u003cli\u003ecarrier transport\u003c/li\u003e\n\u003cli\u003egeneration-recombination\u003c/li\u003e\n\u003cli\u003equasi-Fermi levels\u003c/li\u003e\n\u003cli\u003ethe semiconductor equations\u003c/li\u003e\n\u003cli\u003eenergy band diagrams\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAmong the important learning objectives, the course will introduce learners to the process of drawing and interpreting energy band diagrams. Energy band diagrams are a powerful, conceptual way to qualitatively understand the operation of semiconductor devices. In a concise way, they encapsulate most of the device-relevant specifics of semiconductor physics. Drawing and interpreting an energy band diagram is the first step in understanding the operation of a device.\u003c/p\u003e\n\u003cp\u003eThis course material is typically covered in the first few weeks of an introductory semiconductor device course, but this class provides a fresh perspective informed by new understanding of electronics at the nanoscale.\u003c/p\u003e404:T560,\u003cp\u003eWe encounter signals and systems extensively in our day-to-day lives, from making a phone call, listening to a song, editing photos, manipulating audio files, using speech recognition softwares like Siri and Google now, to taking EEGs, ECGs and X-Ray images. Each of these involves gathering, storing, transmitting and processing information from the physical world. This course will equip you to deal with these tasks efficiently by learning the basic mathematical framework of signals and systems.\u003c/p\u003e\n\u003cp\u003eThis course is divided into two parts. In this part (EE210.1x), we will explore the various properties of signals and systems, characterization of Linear Shift Invariant Systems, convolution and Fourier Transform, while the next part (\u003ca href=\"https://www.edx.org/course/signals-systems-part-2-iitbombayx-ee210-2x-1\"\u003eEE210.2x\u003c/a\u003e), will deal with the Sampling theorem, Z-Transform, discrete Fourier transform and Laplace transform. Ideas introduced in this course will be useful in understanding further electrical engineering courses which deal with c"])</script><script>self.__next_f.push([1,"ontrol systems, communication systems, power systems, digital signal processing, statistical signal analysis and digital message transmission. The concepts taught in this course are also useful to students of other disciplines like mechanical, chemical, aerospace and other branches of engineering and science.\u003c/p\u003e405:T42d,\u003cp\u003eMachine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. This area is also concerned with issues both theoretical and practical.\u003c/p\u003e\r\n\u003cp\u003eIn this course, we will present algorithms and approaches in such a way that grounds them in larger systems as you learn about a variety of topics, including:\u003c/p\u003e\r\n\u003cul\u003e\r\n\u003cli\u003estatistical supervised and unsupervised learning methods\u003c/li\u003e\r\n\u003cli\u003erandomized search algorithms\u003c/li\u003e\r\n\u003cli\u003eBayesian learning methods\u003c/li\u003e\r\n\u003cli\u003ereinforcement learning\u003c/li\u003e\r\n\u003c/ul\u003e\r\n\u003cp\u003eThe course also covers theoretical concepts such as inductive bias, the PAC and Mistake‐bound learning frameworks, minimum description length principle, and Ockham's Razor. In order to ground these methods the course includes some programming and involvement in a number of projects.\u003c/p\u003e\r\n\u003cp\u003eBy the end of this course, you should have a strong understanding of machine learning so that you can pursue any further and more advanced learning.\u003c/p\u003e\r\n\u003cp\u003eThis is a three-credit course.\u003c/p\u003e406:T47f,\u003cp\u003eWant to study for an MBA but unsure of the basic data analysis still required? This online course prepares you for studying in an MBA program and in business generally.\u003c/p\u003e\n\u003cp\u003eData analysis appears throughout any rigorous MBA program and in today’s business environment understanding the fundamentals of collecting, presenting, describing and making inferences from data sets is essential for success.\u003c/p\u003e\n\u003cp\u003eThe goal of this course is to teach you fundamental data analysis skills so you are prepared for your MBA study and able to focus your efforts on core MBA curriculum, rather than continually playing catch-up with the underlying stati"])</script><script>self.__next_f.push([1,"stical knowledge needed.\u003c/p\u003e\n\u003cp\u003eWe also hope that learning these data analysis skills will equip you with the ability to understand, to a greater degree, the data you encounter in your working lives and in the world around you - an essential life-skill in today’s data driven environment\u003c/p\u003e\n\u003cp\u003eThis course assumes no prior knowledge of data analysis. Concepts are explained as clearly as possible and regular activities give you the opportunity to practice your skills and improve your confidence.\u003c/p\u003e407:T6b7,\u003cp\u003eIn the last decade, the amount of data available to organizations has reached unprecedented levels. Data is transforming business, social interactions, and the future of our society. In this course, you will learn how to use data and analytics to give an edge to your career and your life. We will examine real world examples of how analytics have been used to significantly improve a business or industry. These examples include Moneyball, eHarmony, the Framingham Heart Study, Twitter, IBM Watson, and Netflix. Through these examples and many more, we will teach you the following analytics methods: linear regression, logistic regression, trees, text analytics, clustering, visualization, and optimization. We will be using the statistical software R to build models and work with data. The contents of this course are essentially the same as those of the corresponding MIT class (The Analytics Edge). It is a challenging class, but it will enable you to apply analytics to real-world applications.\u003c/p\u003e\n\u003cp\u003eThe class will consist of lecture videos, which are broken into small pieces, usually between 4 and 8 minutes each. After each lecture piece, we will ask you a \"quick question\" to assess your understanding of the material. There will also be a recitation, in which one of the teaching assistants will go over the methods introduced with a new example and data set. Each week will have a homework assignment that involves working in R or LibreOffice with various data sets. (R is a free statistical and computing software en"])</script><script>self.__next_f.push([1,"vironment we'll use in the course. See the Software FAQ below for more info). At the end of the class there will be a final exam, which will be similar to the homework assignments.\u003c/p\u003e408:T59d,\u003cp\u003eAs part of the Principles of Manufacturing MicroMasters program, this course will build on statistical process control foundations to add process modeling and optimization.Building on formal methods of designed experiments, the course develops highly applicable methods for creating robust processes with optimal quality. \u003c/p\u003e\n\u003cp\u003eWe will cover the following topics: \u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eEvaluating the causality of inputs and parameters on the output measures\u003c/li\u003e\n\u003cli\u003eDesigning experiments for the purpose of process improvement\u003c/li\u003e\n\u003cli\u003eMethods for optimizing processes and achieving robustness to noise inputs\u003c/li\u003e\n\u003cli\u003eHow to integrate all of these methods into an overall approach to process control that can be widely applied\u003c/li\u003e\n\u003cli\u003eDeveloping a data-based statistical ability to solving engineering problems in general\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe course will conclude with a capstone activity that will integrate all the Statistical Process Control topics. \u003c/p\u003e\n\u003cp\u003eDevelop the engineering andmanagement skills needed for competence and competitiveness in today’s manufacturing industry with the Principles of Manufacturing MicroMasters Credential, designed and delivered by MIT’s #1-ranked Mechanical Engineering department in the world. Learners who pass the 8 courses in the program earn the MicroMasters Credential and qualify to apply to gain credit for MIT’s Master of Engineering in Advanced Manufacturing \u0026amp; Design program.\u003c/p\u003e409:T788,\u003cp\u003e\u003cem\u003eChemical Thermodynamics II: Equilibrium and Kinetics\u003c/em\u003e is an online course that will teach you how to apply fundamental concepts and principles of thermodynamics to multi-component systems. \u003c/p\u003e\n\u003cp\u003eThe second in a two-part series, this course builds on concepts from \u003ca href=\"https://www.edx.org/learn/chemistry/massachusetts-institute-of-technology-chemical-thermodynamics-i\" rel=\"noopener\" target="])</script><script>self.__next_f.push([1,"\"_blank\"\u003e5.601x Chemical Thermodynamics I\u003c/a\u003e. Together, the pair covers chemical thermodynamics, statistical mechanics, phase equilibria, and kinetics.\u003c/p\u003e\n\u003cp\u003eIn this course, you will leverage the ideas of entropy and free energy (previously developed in 5.601x) to describe chemical potential and how it relates to equilibrium. You will explore chemical equilibria between different chemical species and phase equilibria between different phases of the same chemical species, finally combining both to examine the multiple equilibria present in non-reactive mixtures of species.\u003c/p\u003e\n\u003cp\u003eYou will then change gears to focus on the evolution of chemical systems with time, exploring the concepts of kinetics. After appreciating links between molecular mechanisms and reaction dynamics, you will explore a variety of advanced topics, including catalysis, enzymatics, chain reactions, and oscillating chemical reactions.\u003c/p\u003e\n\u003cp\u003eBased on the MIT undergraduate course 5.602 Thermodynamics II and Kinetics required of all Chemistry majors, this online version is best suited for learners with some knowledge of undergraduate general chemistry and multivariable calculus. However, any learner who wants to get an introduction to fundamental concepts in equilibrium and kinetics will benefit. We welcome undergraduate university students reviewing or supplementing their own course work, as well as mid-career professionals seeking to advance their careers or pursuing post-baccalaureate engineering and technology degrees.\u003c/p\u003e40a:T4e9,\u003cp\u003eSituations where resources are shared among users appear in a wide variety of domains, from lines at stores and toll booths to queues in telecommunication networks. The management of these shared resourcescan have direct consequences on users,whether it be waiting times or blocking probabilities. \u003c/p\u003e\n\u003cp\u003eIn this course, you'll learn how to describe a queuing system statistically, how to model the random evolution of queue lengths over time and calculate key performance indicators, such as an average delay or a l"])</script><script>self.__next_f.push([1,"oss probability. \u003c/p\u003e\n\u003cp\u003eThis course is aimed at engineers, students and teachers interested in network planning. \u003c/p\u003e\n\u003cp\u003ePractical coursework will be carried out using ipython notebooks on a Jupyterhub server which you will be given access to. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudent testimonial\u003c/strong\u003e\u003cbr /\u003e\n\"Great MOOC ! The videos, which are relatively short, provide a good recap on Markov chains and how they apply to queues. The quizzes work well to check if you've understood.\" Loïc, beta-tester \u003c/p\u003e\n\u003cp\u003e\"The best MOOC on edX! I'm finishing week 2 and I've never seen that much care put in a course lab! And I love these little gotchas you put into quizzes here and there! Thank you!\" rka444, learner from Session 1, February - March 2018\u003c/p\u003e40b:T763,\u003cp\u003eThis course provides the graduate-level introduction to understand, analyze, characterize and design the operation of semiconductor devices such as transistors, diodes, solar cells, light-emitting devices, and more.\u003c/p\u003e\n\u003cp\u003eThe material will primarily appeal to electrical engineering students whose interests are in applications of semiconductor devices in circuits and systems. The treatment is physics-based, provides derivations of the mathematical descriptions, and enables students to quantitatively analyze device internal processes, analyze device performance, and begin the design of devices given specific performance criteria.\u003c/p\u003e\n\u003cp\u003eTechnology users will gain an understanding of the semiconductor physics that is the basis for devices. Semiconductor technology developers may find it a useful starting point for diving deeper into condensed matter physics, statistical mechanics, thermodynamics, and materials science. The course presents an electrical engineering perspective on semiconductors, but those in other fields may find it a useful introduction to the approach that has guided the development of semiconductor technology for the past 50+ years.\u003c/p\u003e\n\u003cp\u003eStudents taking this course will be required to complete:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003etwo (2) projects\u003c/li\u003e\n\u003cli\u003eone (1) proctored exam usin"])</script><script>self.__next_f.push([1,"g the edX online Proctortrack software.\u003c/li\u003e\n\u003cli\u003enine (9) homework assignments.\u003c/li\u003e\n\u003cli\u003ethirty-one (31) online quizzes are spread throughout the 16-week semester.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCompleted homework and exam will be scanned and submitted using Gradescope for grading.\u003c/p\u003e\n\u003cp\u003eThis course is one of a growing suite of graduate-level courses being developed in an edX/Purdue University collaboration. Courses like this can apply toward a \u003ca href=\"https://engineering.purdue.edu/ECE/Academics/Online\"\u003ePurdue University MSECE degree\u003c/a\u003e for students accepted into the full master’s program.\u003c/p\u003e40c:T5b6,\u003cp\u003eWe encounter signals and systems extensively in our day-to-day lives, from making a phone call, listening to a song, editing photos, manipulating audio files, using speech recognition softwares like Siri and Google now, to taking EEGs, ECGs and X-Ray images. Each of these involves gathering, storing, transmitting and processing information from the physical world. This course will equip you to deal with these tasks efficiently by learning the basic mathematical framework of signals and systems.\u003c/p\u003e\n\u003cp\u003eThis course is divided into two parts. In the first part (\u003ca href=\"https://www.edx.org/course/signals-systems-part-1-iitbombayx-ee210-1x-1#!\"\u003eEE210.1x\u003c/a\u003e), we explored the various properties of signals and systems, characterization of Linear Shift Invariant Systems, convolution and Fourier Transform. Building on that, in this part (EE210.2x) we will deal with the Sampling theorem, Z-Transform, discrete Fourier transform and Laplace transform. The contents of the first part are prerequisites for doing this part. Ideas introduced in this course will be useful in understanding further electrical engineering courses which deal with control systems, communication systems, power systems, digital signal processing, statistical signal analysis and digital message transmission. The concepts taught in this course are also useful to students of other disciplines like mechanical, chemical, aerospace and other branches of engineering and "])</script><script>self.__next_f.push([1,"science.\u003c/p\u003e40d:T5ec,\u003cp\u003eThis course introduces the Schrödinger equation, using the tight-binding method to discuss the concept of bandstructure and E(k) relations, followed by an introduction to the NEGF method with simple illustrative examples. Concept of spinors is introduced along with the application of the NEGF method to spintronic devices.\u003c/p\u003e\n\u003cp\u003eNo prior background in quantum mechanics or statistical mechanics is assumed.\u003c/p\u003e\n\u003cp\u003eVerified students taking this course will be required to complete three (3) proctored exams using the edX online Proctortrack software. To be sure your computer is compatible, see \u003ca href=\"https://www.proctortrack.com/tech-requirements/\"\u003eProctortrack Technical Requirements\u003c/a\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNanoscience and Technology MicroMasters\u003c/strong\u003e ®\u003c/p\u003e\n\u003cp\u003eIntroduction to Quantum Transport is one course in a growing suite of unique, one-credit-hour short courses developed in an edX/Purdue University collaboration. Students may elect to pursue a verified certificate for this specific course alone or as one of the six courses needed for the edX/Purdue MicroMasters® program in Nanoscience and Technology.\u003c/p\u003e\n\u003cp\u003eFor further information and other courses offered, see the \u003ca href=\"https://www.edx.org/micromasters/purdue-nano-science-technology\"\u003eNanoscience and Technology MicroMasters\u003c/a\u003e® page. Courses like this can also apply toward a \u003ca href=\"https://engineering.purdue.edu/ECE/Academics/Online\"\u003ePurdue University MSECE degree\u003c/a\u003e for students accepted into the full master’s program.\u003c/p\u003e40e:T5bc,\u003cp\u003eThis module is currently offered in the Lyles School of Civil Engineering as part of the CE57200 “Prestressed Concrete Design” 3-Credits (CR) course in the area of structural engineering available to senior undergraduate/graduate students. It integrates science and engineering principles to design prestressed concrete members and structural systems. The application of scientific and engineering knowledge is demonstrated in solving engineering problems associated with the design of precast p"])</script><script>self.__next_f.push([1,"restressed building members both composite and non-composite for superimposed loads, and one-way post-tensioned floor slabs systems bonded and unbonded also composite and non-composite for superimposed loads. Design of pretensioned Hollow-Core slabs, Double-Tee and I-Beam members, and one-way post-tensioned floor slabs is exercised using current building code requirements to provide experiences in realistic design practice. The following subjects are used to solve engineering problems: calculus and differential equations; use of computer tools, data manipulation, statistical analysis, numerical calculation, and reinforced concrete design principles.\u003c/p\u003e\n\u003cp\u003eThe course is developed in three modules each of 1-CR. Module 2 (this module) is focused on the essentials of design of pretensioned concrete structures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis course is available to practicing engineers for 1.5 CEUs for learners completing the course on the verified track.\u003c/strong\u003e\u003c/p\u003e40f:T5be,\u003cp\u003eThis module is currently offered in the Lyles School of Civil Engineering as part of the CE57200 “Prestressed Concrete Design” 3-Credits (CR) course in the area of structural engineering available to senior undergraduate/graduate students. It integrates science and engineering principles to design prestressed concrete members and structural systems. The application of scientific and engineering knowledge is demonstrated in solving engineering problems associated with the design of precast prestressed building members both composite and non-composite for superimposed loads, and one-way post-tensioned floor slabs systems bonded and unbonded also composite and non-composite for superimposed loads. Design of pretensioned Hollow-Core slabs, Double-Tee and I-Beam members, and one-way post-tensioned floor slabs is exercised using current building code requirements to provide experiences in realistic design practice. The following subjects are used to solve engineering problems: calculus and differential equations; use of computer tools, data manipulation, s"])</script><script>self.__next_f.push([1,"tatistical analysis, numerical calculation, and reinforced concrete design principles.\u003c/p\u003e\n\u003cp\u003eThe course is developed in three modules each of 1-CR. Module 3 (this module) is focused on the essentials of design of post-tensioned concrete structures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis course is available to practicing engineers for 1.5 CEUs for learners completing the course on the verified track.\u003c/strong\u003e\u003c/p\u003e410:T5fe,\u003cp\u003eThis module is currently offered in the Lyles School of Civil Engineering as part of the CE57200 “Prestressed Concrete Design” 3-Credits (CR) course in the area of structural engineering available to senior undergraduate/graduate students. It integrates science and engineering principles to design prestressed concrete members and structural systems. The application of scientific and engineering knowledge is demonstrated in solving engineering problems associated with the design of precast prestressed building members both composite and non-composite for superimposed loads, and one-way post-tensioned floor slabs systems bonded and unbonded also composite and non-composite for superimposed loads. Design of pretensioned Hollow-Core slabs, Double-Tee and I-Beam members, and one-way post-tensioned floor slabs is exercised using current building code requirements to provide experiences in realistic design practice. The following subjects are used to solve engineering problems: calculus and differential equations; use of computer tools, data manipulation, statistical analysis, numerical calculation, and reinforced concrete design principles.\u003c/p\u003e\n\u003cp\u003eIn the edX platform, the course is developed in three modules each of 1-credit. \u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFundamentals of Prestressed Concrete (this course)\u003c/li\u003e\n\u003cli\u003ePretensioned Structures\u003c/li\u003e\n\u003cli\u003ePost-Tensioned Structures\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eThis course is available to practicing engineers for 1.5 CEUs for learners completing the course on the verified track.\u003c/strong\u003e\u003c/p\u003e411:T4cd,\u003cp\u003eHow do robots “see”, respond to and learn from their interactions with the world around them"])</script><script>self.__next_f.push([1,"? This is the fascinating field of visual intelligence and machine learning. Visual intelligence allows a robot to “sense” and “recognize” the surrounding environment. It also enables a robot to “learn” from the memory of past experiences by extracting patterns in visual signals.\u003c/p\u003e\n\u003cp\u003eYou will understand how Machine Learning extracts statistically meaningful patterns in data that support classification, regression and clustering. Then by studying Computer Vision and Machine Learning together you will be able to build recognition algorithms that can learn from data and adapt to new environments.\u003c/p\u003e\n\u003cp\u003eBy the end of this course, part of the Robotics MicroMasters program, you will be able to program vision capabilities for a robot such as robot localization as well as object recognition using machine learning.\u003c/p\u003e\n\u003cp\u003eProjects in this course will utilize MATLAB and OpenCV and will include real examples of video stabilization, recognition of 3D objects, coding a classifier for objects, building a perceptron, and designing a convolutional neural network (CNN) using one of the standard CNN frameworks.\u003c/p\u003e412:T787,\u003cp\u003eYou may have heard of actuarial science, or you might even know an actuary, but do you know what an actuary does? During the course you'll hear from a wide variety of actuaries about their careers.\u003c/p\u003e\n\u003cp\u003eAnd don't be scared that the course will be \"just a whole lot of mathematics\". Together, we will go beyond the math to learn how actuaries approach problems relating to risk, using examples from:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eFinance\u003c/li\u003e\n\u003cli\u003eInvestments\u003c/li\u003e\n\u003cli\u003eBanking\u003c/li\u003e\n\u003cli\u003eInsurance\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eYou will learn how actuarial science applies mathematical and statistical methods to assess risk in these industries and other professions.\u003c/p\u003e\n\u003cp\u003eYou'll experience \"hands-on\" learning using Excel (or an equivalent spreadsheet tool) to project and investigate the financial condition of a company choosing appropriate strategies for the company through the use of simulations.\u003c/p\u003e\n\u003cp\u003eThe course has been carefully"])</script><script>self.__next_f.push([1," designed for students from a wide variety of backgrounds, with secondary/high school level being the only assumption of mathematical background. Even if you don't have any background in, for example, calculus, the course has been designed so you can skip over these sections without affecting your understanding of the rest of the course. You also do not need to have any Excel or other spreadsheet background to take the course.\u003c/p\u003e\n\u003cp\u003eFor those with stronger mathematical backgrounds, extension questions are provided to test you further. You'll learn a huge amount about actuarial science no matter what your background is!\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\"Great introduction to this specialized field; every day there are new challenges where as a student you are put in a situation to learn and apply the lessons with practical exercises. Great structure of the course, with main concepts to review at the end of a lesson. I would recommend to anyone who would like to learn more about actuarial science.\"\u003c/em\u003e - Previous student\u003c/p\u003e413:T816,"])</script><script>self.__next_f.push([1,"\u003cp\u003eDevelop the fundamental skills needed for global excellence in manufacturing and competitiveness with the Principles of Manufacturing MicroMasters Credential, designed and delivered by MIT’s #1-world ranked Mechanical Engineering department. Build your career with the credential or use it as credits towards a Master’s Degree by applying to MIT’s world-renowned \u003ca href=\"http://manufacturing.mit.edu/\" target=\"_blank\"\u003eMaster of Engineering in Advanced Manufacturing and Design Blended Program\u003c/a\u003e.\u003c/p\u003e\r\n\r\n\u003cp\u003eThis program provides students with a fundamental basis for understanding and controlling rate, quality and cost in a manufacturing enterprise.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe Principles of Manufacturing are a set of elements common to all manufacturing industries that revolve around the concepts of flow and variations. These principles have emerged from working closely with manufacturing industries at both the research and operational levels. \u003c/p\u003e\r\n\r\n\u003cp\u003eTargeted towards graduate-level engineers, product designers, and technology developers with an interest in a career in advanced manufacturing, the program will help learners understand and apply these principles to product and process design, factory and supply chain design, and factory operations. \u003c/p\u003e\r\n\r\n\u003cp\u003eThis curriculum focusses on the analysis, characterization and control of flow and variation at different levels of the enterprise through the following subject areas:\u003c/p\u003e \r\n\r\n\u003cul\u003e\r\n\u003cli\u003e\u003cstrong\u003eUnit Process Variation and Control:\u003c/strong\u003e Modeling and controlling temporal and spatial variation in unit processes\u003c/li\u003e \r\n\u003cli\u003e\u003cstrong\u003eFactory Level System Variation and Control:\u003c/strong\u003e Modeling and controlling flows in manufacturing systems with stochastic elements and inputs.\u003c/li\u003e \r\n\u003cli\u003e\u003cstrong\u003eSupply Chain – System Variation and Control: \u003c/strong\u003eHow to operate and design optimal manufacturing-centered supply chains.\u003c/li\u003e \r\n\u003cli\u003e\u003cstrong\u003eBusiness Flows:\u003c/strong\u003e Understanding the uses and flow of business information to start up, scale up and operate a manufacturing facility.\u003c/li\u003e\r\n\u003c/ul\u003e"])</script><script>self.__next_f.push([1,"414:T93a,"])</script><script>self.__next_f.push([1,"\u003cp\u003eIdentify, implement and evaluate an individual business project of your organization and compensate the course fee by its financial benefits. \r\n\r\n\u003cp\u003eYou will travel along the DMAIC, guided by digital resources and a Master Black Belt as co-pilot. You will stop at each key Sigma tool and document it in your project storybook. These results are reviewed at each DMAIC milestone and serve as the basis for individual coaching and certification. Your completed storybook will demonstrate the operational excellence of your work and the benefits you gained.\r\nOur goal is for you to successfully complete your project, for the benefits of your project to exceed the course fee, and for you to develop the generic competence to successfully identify, implement, and evaluate future projects. To this end, we will guide you digitally and in person.\r\n\r\n\u003cp\u003eOur digital guidance provides videos, eBooks and tasks to introduce every step of a project. SigmaGuide software offers frameworks for each step, while Minitab provides quantitative tools. Documenting every step of the project in a traceable way is necessary to prove methodical competence and provide a basis for steering the project.\r\n\r\n\u003cp\u003eOur \u003cb\u003epersonal guidance\u003c/b\u003e supports your individual needs in three formats:\r\n\u003cul\u003e\r\n\u003cli\u003e\u003cb\u003eDMAIC Phase Reviews\u003c/b\u003e will review the results of each phase, correct errors, show alternatives and suggest next steps.\u003c/li\u003e\r\n\u003cli\u003e\u003cb\u003eProject Coaching\u003c/b\u003e involves checking the suitability of the project topic and determining the focus (PreDEFINE), structuring the project and prioritizing the problems to be solved (DEFINE), prioritize your hypotheses and plan the collection of necessary data (MEASURE), optimizing the analysis of data, and reaching key milestones (ANALYSE). The IMPROVE phase typically does not require methodical support and the path through CONTROL is determined by ANALYSE.\u003c/li\u003e\r\n\u003cli\u003e\u003cb\u003eGreen Belt Lectures\u003c/b\u003e: Our online Green Belt lectures reinforce topics covered in the course material, such as scientific observation, tool application, hypothesis generation, data collection, hypothesis testing (Minitab), and problem modeling - do's and don'ts.\u003c/li\u003e\r\n\u003c/ul\u003e\u003c/p\u003e\r\n\r\n\u003cp\u003eIn addition, you will acquire the basics of digital lean competence with our interactive process mining module. You can also use process mining for your project - if feasible.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"415:T814,"])</script><script>self.__next_f.push([1,"\u003cp\u003eGain a solid foundation in essential quantitative and analytical skills to succeed as a business professional and prepare yourself to pursue an MBA.\u003c/p\u003e\r\n\r\n\u003cp\u003eIn this Professional Certificate program, you will learn key quantitative and analytical skills needed to succeed as a business professional. In the era of big data, it is vital for business professionals to understand what the numbers say, whether it be analyzing financial reports or presenting data in an effective way.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe subjects covered in this program not only provide foundational business skills but also prepare you to further your business studies and pursue an MBA, where you can build upon these skills and gain theoretical and practical training at a graduate-level to better understand general business management functions.\u003c/p\u003e \r\n\r\n\u003cp\u003eEach course offers a personalised learning plan that highlights the areas you need to focus on to give you a competitive edge within your company or in an MBA program.\u003c/p\u003e\r\n\r\n\u003cp\u003eGuided by experts in the field, you will use real world examples, interactive exercises and the support of your peers to learn the fundamentals of: \r\n\u003cul\u003e\u003cli\u003eAccounting\u003c/li\u003e\r\n\u003cli\u003eFinance\u003c/li\u003e\r\n\u003cli\u003eMaths\u003c/li\u003e\r\n\u003cli\u003eData Analysis\u003c/li\u003e\u003c/ul\u003e\r\n\u003c/p\u003e\r\n\r\n\u003cp\u003eThis Professional Certificate program is targeted at:\r\n\u003cul\u003e\u003cli\u003eProfessionals preparing to or already working in a business environment who want to enhance their quantitative business skills. \u003c/li\u003e\r\n\u003cli\u003eLearners who are ready to embark on or considering an MBA program and want to identify and remove any gaps in their quantitative and analytical skills to ensure they are prepared to succeed in the program. \u003c/li\u003e\u003c/ul\u003e\r\n\u003c/p\u003e\r\n\r\n\u003cp\u003eThe “PreMBA Essentials for Professionals” Professional Certificate program will prepare you for success in an MBA at an institution such as Imperial Business School by helping you gain the fundamental skills you need to advance your studies. Imperial College Business School offers a world-class Global Online MBA program and we invite you to visit their website to learn more. \u003c/p\u003e"])</script><script>self.__next_f.push([1,"416:T6ec,\u003cp\u003eData Science and Data Analytics skills are in high demand and R is the programming language of choice for many data professionals. This Applied Data Science with R program emphasizes a hands-on approach to developing job-ready skills for analyzing and visualizing data using R.\u003c/p\u003e \r\n\r\n\u003cp\u003eYou will start the program by learning the fundamentals of R language, including common data types and structures, and utilize it for basic programming and data manipulation tasks.\u003c/p\u003e\r\n\r\n\u003cp\u003eAs you progress in the program, you will learn about relational database concepts and gain a foundational knowledge of the SQL language. You will access and analyze data in databases using R and SQL through Jupyter notebooks.\u003c/p\u003e\r\n\r\n\u003cp\u003eYou will learn various data analysis techniques – from cleaning and refining data to developing, evaluating, and tuning , data science models. You will also learn how to tell a compelling story with data by creating graphs, visualizations, dashboards and interactive data applications.\u003c/p\u003e\r\n\r\n\u003cp\u003eIn each course you will complete hands-on labs and projects to help you gain practical experience with data manipulation, analysis and visualization using a variety of datasets. You will work with tools like R Studio, Jupyter Notebooks, Watson Studio and related R libraries for data science, including dplyr, Tidyverse, Tidymodels, R Shiny, ggplot2, Leaflet, and rvest.\u003c/p\u003e\r\n\r\n\u003cp\u003eBy the end of the program you will be able to apply the data science skills and techniques that you have accumulated and show case those skills in the R Data Science Capstone Project. involving a real-world dataset, and inspired by a real business challenge. This project will culminate in a presentation for reporting the results of data analysis with stakeholders.\u003c/p\u003e417:Tfd2,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThis updated view of Lean Six Sigma (LSS) provides the best understanding of how Lean, Six Sigma, Quality Management, theories and tools are to be applied to different aspects of Global Supply Chains.\u003c/p\u003e\r\n \r\n\u003cp\u003eIn addition, the Lean Six Sigma Certification Courses provide insight on initiatives and six sigma methods that continue to drive efficiency in sustainably operated supply chain processes. The real-world examples provide a modern interpretation of how initiatives, including sustainability and technology adaptation, drive current Global Value Chains and their envisioned process and continuous improvement.\u003c/p\u003e\r\n\r\n\u003cp\u003eIt is recommended the three-course program be taken in sequence. It guides learners through concepts and foundations of Quality, along with the use of both the basics, as well as the advanced, improvement tools and techniques to show the integration of lean to six sigma.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe program begins with CLSSYB01: Introduction to Lean Six Sigma for Sustainable and Resilient Supply Chains, guiding learners through the basic Six Sigma Methodology and foundations of Six Sigma and Supply Chain Management. CLSSYB01 provides an understanding of various topics starting with an introduction and overview of Six Sigma. The course will advance into DMAIC (Define, Measure, Analyze, Improve and Control) and its related six sigma tools. Learners will gain an understanding of the “Lean” in Six Sigma before moving into the roles and responsibilities of management and stakeholders within Lean Six Sigma in Supply Chain.\u003c/p\u003e\r\n\r\n\u003cp\u003eAfter completing CLSSYB01, learners are encouraged to continue with CLSSYB02 and CLSSYB03 in order to obtain the Lean Six Sigma Certificate and gain further understanding on how to implement Lean Six Sigma Projects for optimization of supply chain and strong sustainability performance.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe Lean Six Sigma for Sustainable Supply Chain Program includes a second content course: CLSSYB02: Lean Six Sigma Program and Project Management, which expands upon the content of CLSSYB01.\u003c/p\u003e\r\n\r\n\u003cp\u003eWithin CLSSYB02, the learner is taken through the applied use of the tools and metrics in the LSS program framework. This applied approach includes the basics of project management and lays out real-world methods to apply these tools at the basic level. This program prepares the learner with the necessary knowledge to be a team member (Yellow Belt) of LSS projects seeking synergies, product quality improvement, reduced variability, and shorter lead times.\u003c/p\u003e\r\n \r\n\u003cp\u003eThe final course is a certification exam. CLSSYB03: ISCEA CLSSYB Exam, allows learners to display their knowledge acquired in the previous courses of the ISCEA Lean Six Sigma for Sustainable Supply Chain Program. If the program is successfully completed, the learner will obtain a credential recognized by the industry that supports improved workforce options for the learner: The Certified Lean Six Sigma Yellow Belt Designation.\u003c/p\u003e \r\n \r\n\u003cp\u003eWhile there are no enforced prerequisites to participate in the program, the Lean Six Sigma for Sustainable Supply Chain Program, on edX by ISCEA, is recommended for pre-career or early career Supply Chain professionals. Previous experience in, or future plans to enter into the areas of merchandising, inventory management, operations management, manufacturing industries, the production process, decision making, or buyer/planner role, complements the expertise gained via completion of the courses. ISCEA envisions learners who are seeking tangible skills, tutorials, and credentials to facilitate their progress into senior roles with more strategic oversight, to be successful candidates for the program. Learners who complete the program can immediately apply the lessons they learn to corporate roles and related value streams. Those focused on corporate process improvement, reduced environmental impact efforts, and customer satisfaction will receive great benefits from this program. There is limited mathematical expectations within the courses of the program.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"418:T40f,\u003cp\u003eThis Healthcare Data Analytics Toolkit MicroMasters program introduces you to the transformative role of data analysis in healthcare. It's designed specifically for those at the beginning of their journey in healthcare data analytics, providing foundational knowledge and skills. Throughout this series, you'll delve into the integration of data analytics in healthcare settings, gaining hands-on experience in analyzing data to draw meaningful conclusions and apply these insights to real-world healthcare challenges.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe introductory series focuses on the basics of data analysis and its application in healthcare settings. You’ll learn the essentials of analyzing data, understanding trends, and making data-informed decisions.\u003c/p\u003e\r\n\r\n\u003cp\u003eWhether you are working in a hospital, leading projects in a healthcare NGO, launching a digital health startup, or investing in cutting-edge healthcare technology, our program provides the foundation for the application of data analysis to solve healthcare's biggest challenges.\u003c/p\u003e419:T82e,"])</script><script>self.__next_f.push([1,"\u003cp\u003eDo you want to inform healthcare decisions by conducting research on the effectiveness, benefits, and potential harms of treatment options? Would you like to help patients choose care that best meets their needs? Perhaps you’d like to learn the basics of clinical research? Or maybe you’d like to use a national health data registry to answer a research question? If any of these questions resonate Comparative Effectiveness Research Training and Instruction – CERTaIN - Professional Certificate Program is right for you!\u003c/p\u003e\r\n\r\n\u003cp\u003eCreated by investigators from The University of Texas MD Anderson Cancer Center and partner institutions, the CERTaIN Professional Certificate program provides a comprehensive overview of core concepts, research methods and data analysis techniques used in comparative effectiveness (CER) and patient-centered outcomes research (PCOR) across five key areas:\r\n\r\n\u003cbr\u003e\u003cbr\u003eCourse 1. Introduction\r\n\u003cbr\u003eCourse 2. Knowledge Synthesis\r\n\u003cbr\u003eCourse 3. Patient-Centered Outcomes Research (PCOR)\r\n\u003cbr\u003eCourse 4. Pragmatic Clinical Trials and Healthcare Delivery Evaluations\r\n\u003cbr\u003eCourse 5. Observational Studies and Registries\u003c/p\u003e\r\n\r\n\u003cp\u003eLearn from expert decision scientists, biostatisticians, oncologists, economists, social scientists, health care policy experts and epidemiologists how to conduct CER/PCOR and see how CER/PCOR methods have been applied in the real world to conduct state of the art research studies.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe CERTaIN Professional Certificate Program is intended for anyone interested in CER/PCOR methods. This program is comprised of 5 courses and includes a combined total of almost 50 lectures in CER/PCOR topics. Each course consists of a series of lectures delivered by content experts and each lecture is segmented into short videos, followed by a quiz to assess your understanding of the material.\u003c/p\u003e\r\n\r\n\u003cp\u003eThe CERTaIN Professional Certificate Program is supported by grant number R25HS023214 from the Agency for Healthcare Research and Quality. \u003c/p\u003e\r\n\r\n\u003cp\u003eThe CERTaIN Professional Certificate Program was created by investigators.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"41a:T494,\u003cp\u003eLearn the core techniques of text analytics and natural language processing (NLP) while discovering the cognitive science that makes it possible in this certificate Text Analytics with Python. On the practical side, you’ll learn how to actually do an analysis in Python: creating pipelines for text classification and text similarity using machine learning. These pipelines are automated workflows that go all the way from data collection to visualization. On the scientific side, you’ll learn what it means to understand language computationally. Artificial intelligence and humans don’t view text documents in the same way. Sometimes deep learning sees patterns that are invisible to us. But often deep learning misses the obvious. We have to understand the limits of a computational approach to language together with the ethical requirements that guide how we choose what data to use and how we protect the privacy of individuals.\u003c/p\u003e\r\n\r\n\u003cp\u003eAlong the way, you’ll explore real-world case studies using pandas, numpy, scikit-learn, tensorflow, matplotlib, seaborn, gensim, and spacy within jupyter notebooks to gain useful insights from unstructured data.\u003c/p\u003e41b:T539,\u003cp\u003eA l’issue de ce cours, vous devriez être en mesure de : \u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eExpliquer les concepts de base, les définitions et les principes comptables dans le cadre intégré des SFP. \u003c/li\u003e\n\u003cli\u003eClasser les flux et les encours de base du secteur des administrations publiques selon le Manuel de statistiques de finances publiques 2014. \u003c/li\u003e\n\u003cli\u003eAppliquer les principes généraux pour classer une institution dans le secteur public et dans les sous-secteurs pertinents, tels que les administrations publiques et les sociétés publiques. \u003c/li\u003e\n\u003cli\u003eEnregistrer les flux et les encours budgétaires associés aux activités des établissements du secteur public, en suivant les directives et les classifications du MSFP 2014. \u003c/li\u003e\n\u003cli\u003eExpliquer comment sont calculés les principaux agrégats et soldes analytiques des SFP, et ce qu’ils montrent de l’im"])</script><script>self.__next_f.push([1,"pact du secteur des administrations publiques sur l’économie. \u003c/li\u003e\n\u003cli\u003eDévelopper un plan de migration pour adopter la méthodologie du MSFP 2014, et compiler et diffuser les SFP en suivant les directives internationales. \u003c/li\u003e\n\u003cli\u003eReconnaître la valeur de compter sur des SFP complètes, cohérentes et comparables au niveau international, et l'utilisation des indicateurs clés des SFP dans la conception, le suivi et l'évaluation de la politique budgétaire.\u003c/li\u003e\n\u003c/ul\u003e41c:T5b9,\u003cp\u003e\u003cspan lang=\"FR-FR\"\u003eÀ la fin du cours, vous devriez être capable de :\u003c/span\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan lang=\"FR-FR\"\u003eDécrire le cadre du \u003c/span\u003eSystème de comptabilité nationale, comprendre la séquence des comptes et leurs interrelations, et identifier les principaux indicateurs macroéconomiques.\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"FR-FR\"\u003eExpliquer les principaux concepts\u003c/span\u003e des comptes nationaux, les règles de comptabilisation et les méthodes.\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"FR-FR\"\u003eDéfinir les composantes du produit intérieur brut (PIB) à partir de l'approche\u003c/span\u003e de la production. Appliquer les concepts, les règles de comptabilisation, les méthodes et les sources de données nécessaires pour estimer le PIB à partir de l'approche de la production.\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"FR-FR\"\u003eDéfinir les composantes du PIB à partir de l'approche des dépenses. \u003c/span\u003eAppliquer les concepts, les règles de comptabilisation, les méthodes et les sources de données nécessaires pour estimer le PIB à partir de l'approche des dépenses.\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"FR-FR\"\u003eDéfinir les composantes du PIB à partir de l'approche \u003c/span\u003edes revenus. Appliquer les concepts, les règles de comptabilisation, les méthodes et les sources de données nécessaires pour estimer le PIB à partir de l'approche des revenus.\u003c/li\u003e\n\u003cli\u003e\u003cspan lang=\"FR-FR\"\u003eDéfinir et expliquer comment \u003c/span\u003eétablir les estimations en volume du PIB du point de vue de la production et des dépenses.\u003c/li\u003e\n\u003c/ul\u003e41d:T479,\u003cp\u003eCe cours, présenté par le Département des statistiques, couvre les principes fon"])</script><script>self.__next_f.push([1,"damentaux nécessaires à l'élaboration des comptes internationaux. Le cours présente le cadre statistique conceptuel pour la balance des paiements et la PEG – tel que présenté dans le Manuel de statistiques de la balance des paiements et de la position extérieure globale, sixième édition (MBP6), harmonisé avec d'autres cadres statistiques macroéconomiques. Vous découvrirez les soldes des comptes courants, de capital et financiers, et comment ils reflètent l'interaction de votre économie avec le reste du monde. Sont couverts les concepts de base, les définitions et les classifications, ainsi que les principales règles de comptabilisation (y compris l'évaluation et le moment de l'enregistrement) qui sont pertinentes pour l'établissement des comptes internationaux. Le cours aborde également les catégories fonctionnelles, y compris l'investissement direct. La nécessité d'intégrer la balance des paiements à la PEG pour compiler des statistiques complètes et comparables au niveau international sera également discutée.\u003c/p\u003e41e:T424,\u003cp\u003eÀ la fin de ce cours, les participants devraient être capables de :\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eExpliquer le cadre de la balance des paiements et de la PEG, et les indicateurs pertinents, tels que les soldes des comptes courants, de capital et financier.\u003c/li\u003e\n\u003cli\u003eAppliquer les concepts et définitions fondamentaux pertinents pour les comptes internationaux tels que la résidence, le territoire économique, le centre prédominant d'intérêt économique et les règles de comptabilisation.\u003c/li\u003e\n\u003cli\u003eClasser les transactions et positions transfrontalières selon le MBP6.\u003c/li\u003e\n\u003cli\u003eEnregistrer les principales composantes de la balance des paiements et de la PEG.\u003c/li\u003e\n\u003cli\u003eExpliquer le cadre de l'investissement direct et son rôle dans l'économie, y compris en tant que source de financement.\u003c/li\u003e\n\u003cli\u003eComparer les différentes catégories fonctionnelles et comprendre l'interprétation économique de chaque catégorie.\u003c/li\u003e\n\u003cli\u003eReconnaître la nécessité d'intégrer la balance des pa"])</script><script>self.__next_f.push([1,"iements et la PEG pour produire des données logiques et cohérentes.\u003c/li\u003e\n\u003c/ul\u003e41f:T813,"])</script><script>self.__next_f.push([1,"\u003cp\u003eL'analyse de données quantitatives est devenue aujourd'hui une pratique incontournable dans tous les métiers liés aux sciences sociales. Ces analyses sont utilisées pour comprendre des phénomènes économiques et financiers, décrire la nature de la relation entre des personnes, des objets ou des événements, ou encore anticiper les conséquences d’une décision.\u003c/p\u003e\n\u003cp\u003eCe cours vous permettra d’acquérir les premiers concepts nécessaires pour construire rigoureusement des modèles économétriques. Il est constitué de leçons construites à partir de cas pratiques originaux interrogeant la vie quotidienne. Mentionnons parmi d’autres:\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eComment réussir une campagne de financement collaboratif ?\u003c/li\u003e\n\u003cli\u003eQuels sont les déterminants des écarts salariaux entre les hommes et les femmes ?\u003c/li\u003e\n\u003cli\u003eExiste-t-il encore un lien entre l’inflation et le chômage ?\u003c/li\u003e\n\u003cli\u003ePeut-on prédire un changement de niveau de risque sur les marchés financiers ?\u003c/li\u003e\n\u003cli\u003eExiste-t-il un lien entre le niveau de revenu et le sentiment d’être en bonne santé ?\u003c/li\u003e\n\u003cli\u003eLe salaire est-il la seule motivation économique des travailleurs ?\u003c/li\u003e\n\u003cli\u003eLe prix de vente des œuvres de Picasso correspond-il à sa valeur de catalogue ?\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eCe cours contient huit leçons. Il permet tout d’abord de comprendre l’étendue et les limites de l’utilisation de l’économétrie en sciences sociales. La formation se poursuit en étudiant le modèle linéaire, tout en insistant sur la manière dont la construction d’un tel modèle permet d'identifier et de quantifier diverses relations entre les données mesurées. Il élabore également les propriétés statistiques de l'estimateur de moindres carrés, permettant ensuite de montrer comment il est possible de vérifier ou de tester des hypothèses économiques en pratique. Des leçons particulières sont consacrées à l’analyse de données fréquemment utilisées dans la pratique de l'économétrie, telles que les variables catégorielles et les séries temporelles.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"420:T9b7,"])</script><script>self.__next_f.push([1,"\u003cp\u003eLes données sont omniprésentes dans nos vies modernes, et leur quantité est faramineuse. Elles peuvent nous être très utiles dans tous les domaines de l’activité humaine comme la santé, le commerce et l’environnement, pour n’en nommer que quelques-uns. Mais comment tirer profit de ces données sans s’y perdre? Le cours Science des données présente les concepts essentiels permettant de collecter les données, de les traiter statistiquement, de les vérifier, de les visualiser, de les structurer et de les analyser. Nous allons décrire certains algorithmes d'apprentissage automatique, et leurs applications aux données.\u003c/p\u003e\n\u003cp\u003eCe cours s'adresse principalement aux professionnels du secteur et aux universitaires ayant des connaissances de base en mathématiques et en programmation (idéalement Python). Les étudiants diplômés en sciences et en ingénierie (principalement ceux qui ne sont pas encore familiarisés avec la science des données) peuvent trouver ce contenu instructif et convaincant. Le contenu de ce cours sera également d’une grande utilité pour quiconque utilise ou s’intéresse à la science des données, de quelque manière que ce soit.\u003c/p\u003e\n\u003cp\u003eNous estimons qu’il faut environ 8 semaines pour suivre ce cours. Celui-ci est divisé en segments pertinents que vous pouvez regarder à votre propre rythme. Des quiz récapitulatifs sont prévus à la fin de chaque segment pour évaluer votre compréhension du contenu. Vous pourrez également mettre la main à la pâte en réalisant des exercices pratiques qui vous permettront de vous familiariser avec les principaux savoir-faire issus de la science des données.\u003c/p\u003e\n\u003cp\u003eCe cours a été développé par des experts du domaine du département d’informatique et de recherche opérationnelle (DIRO) de l’université de Montréal.\u003c/p\u003e\n\u003cp\u003eL’Université de Montréal rayonne depuis de nombreuses années par la qualité de son enseignement et la diversité de son offre, ce qui en fait une des meilleures universités francophones au monde. Que vous choisissiez ce cours à titre personnel ou dans une perspective de carrière en science, le point de départ est ici. La matière parcourue vous permettra de vous préparer avant de commencer un programme d’études en science des données ou toute autre étude liée à ce domaine.\u003c/p\u003e\n\u003cp\u003eNous vous souhaitons la bienvenue dans ce cours en sciences des données, une des pierres angulaires de l'intelligence artificielle.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"421:Ta2e,"])</script><script>self.__next_f.push([1,"\u003cp\u003eBienvenue au cours VIARENA, «Vision artificielle et exploitation intelligente des ressources naturelles». La vision artificielle est l'art et la science de rendre les ordinateurs capables d'interpréter intelligemment des images. Il existe une multitude d'applications de la vision artificielle réalisables dans nos entreprises, particulièrement les PME et nos communautés. \u003c/p\u003e\n\u003cp\u003eLa vision artificielle est particulièrement accessible et facile à mettre en oeuvre. Malheureusement, c'est un secret trop bien gardé de l'intelligence artificielle. En effet, les données sont abondantes et faciles à récolter, les logiciels sont offerts en logiciel libre, il existe de gros modèles préentraînés et l'infonuagique démocratise l'accès aux infrastructures de calcul.\u003c/p\u003e\n\u003cp\u003eCe cours pratique fait le pari que vous pourrez appliquer la vision artificielle à une foule de problèmes sans en maîtriser les détails mathématiques. VIARENA propose une approche pratique basée sur le code pour vous aider à gagner en confiance pendant que vous apprenez des concepts clés.\u003c/p\u003e\n\u003cp\u003eLe cours VIARENA utilise le langage de programmation Python qui possède le plus riche écosystème en IA. Une trentaine de courts laboratoires, sous la forme de carnets IPython interactifs, utilisent la bibliothèque Keras de Google, une interface de programmation d'applications de haut niveau qui démocratise l'apprentissage profond.\u003c/p\u003e\n\u003cp\u003eLes informaticiens apprécieront les laboratoires avec Google Colab qui leur permettront de travailler directement avec le code sans avoir à installer de logiciels. Toutefois, ce ne sont pas des exercices de programmation, tout le code est là et fonctionnel. Les exercices portent plutôt sur l'appropriation et la compréhension du code.\u003c/p\u003e\n\u003cp\u003eIdentifiez une niche commerciale en survolant une foule d'applications pratiques. VIARENA montre un grand nombre d'exemples et d'applications pour illustrer les possibilités et stimuler votre créativité. Des applications comme la surveillance d'un troupeau, la récolte robotisée, l'inspection visuelle ou sonore d'éoliennes, le diagnostic d'une maladie de plante, le tri automatique de matières résiduelles, l'identification de minéraux, l'inventaire forestier, la prédiction de sécheresse, le comptage de poissons, etc.\u003c/p\u003e\n\u003cp\u003eRécoltez des données avec un drone. Entraînez des réseaux de neurones profonds. Déployez une application sur un téléphone intelligent ou un site Web.\u003c/p\u003e\n\u003cp\u003eL'objectif de VIARENA est: « Faire que l'IA soit aussi québécoise que le sirop d'érable et la motoneige. »\u003c/p\u003e"])</script><script>self.__next_f.push([1,"422:T1b35,"])</script><script>self.__next_f.push([1,"\u003cp\u003eLe cours VIARENA a été conçu pour deux clientèles principales qui correspondent à deux parcours pédagogiques:\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eProfil « coureur des bois » - Les entrepreneurs, dirigeants et visionnaires pour les aider à identifier les opportunités et à comprendre les applications potentielles de la vision artificielle.\u003c/li\u003e\n\u003cli\u003eProfil « patenteux » - Les praticiens, informaticiens, programmeurs, codeurs, ingénieurs, techniciens, scientifiques et développeurs de logiciels qui désirent s'initier d'une façon pratique à la vision par ordinateur. Pour eux, les laboratoires exigent la connaissance d'un langage de programmation et si possible de Python\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eObjectifs finaux du cours\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCOUREUR DES BOIS :\u003c/p\u003e\n\u003cp\u003e- Faire des liens entre les connaissances acquises en vision artificielle et des applications dans la vie réelle.\u003c/p\u003e\n\u003cp\u003ePATENTEUX:.\u003c/p\u003e\n\u003cp\u003e- Réaliser une application en vision artificielle depuis l'acquisition et le prétraitement des images, en passant par la création, l'entraînement et l'évaluation d'un réseau convolutif, puis finalement son déploiement sur la Toile ou sous la forme d'une application mobile.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectifs du module 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCOUREUR DES BOIS \u0026amp; PATENTEUX:\u003c/p\u003e\n\u003cp\u003e- Comprendre que la vision artificielle est accessible et prête à l'emploi;\u003c/p\u003e\n\u003cp\u003e- Comprendre l'importance des données;\u003c/p\u003e\n\u003cp\u003e- Énumérer quelques applications de la vision artificielle;\u003c/p\u003e\n\u003cp\u003e- Connaître certaines limites de l'IA.\u003c/p\u003e\n\u003cp\u003eCOUREUR DES BOIS :\u003c/p\u003e\n\u003cp\u003e- Connaître les éléments-clés d'une stratégie pour bien débuter en IA.\u003c/p\u003e\n\u003cp\u003ePATENTEUX:\u003c/p\u003e\n\u003cp\u003e- Distinguer et définir intelligence artificielle et science des données.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectifs du module 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCe module a pour objectif la mise à jour de vos connaissances par un survol de la science des données et de l'apprentissage automatique. Si vous êtes déjà familier de ces sujets, nous vous invitons à passer directement au module suivant.\u003c/p\u003e\n\u003cp\u003eCOUREUR DES BOIS \u0026amp; PATENTEUX:\u003c/p\u003e\n\u003cp\u003e- Comprendre comment les ordinateurs peuvent apprendre à partir de données;\u003c/p\u003e\n\u003cp\u003e- Saisir l’importance des données, de leur acquisition et de leur prétraitement.\u003c/p\u003e\n\u003cp\u003eCOUREUR DES BOIS :\u003c/p\u003e\n\u003cp\u003e- Comprendre « intuitivement » la science des données, et l’apprentissage automatique.\u003c/p\u003e\n\u003cp\u003ePATENTEUX:\u003c/p\u003e\n\u003cp\u003e- Lire, manipuler, visualiser, calculer des statistiques, analyser des relations dans un jeu de données;\u003c/p\u003e\n\u003cp\u003e- Déterminer la classe-cible et les prédicteurs, séparer les données d'entraînement et de test, choisir un algorithme d’apprentissage classique, entraîner et évaluer un modèle.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectifs du module 3\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCOUREUR DES BOIS:\u003c/p\u003e\n\u003cp\u003e- Comprendre « intuitivement » le fonctionnement d'un neurone, l'apprentissage d'un perceptron, l'apprentissage d'un réseau de neurones et l'apprentissage profond.\u003c/p\u003e\n\u003cp\u003ePATENTEUX:\u003c/p\u003e\n\u003cp\u003e- Comprendre « pratiquement » le fonctionnement d'un neurone, l'apprentissage d'un perceptron, l'apprentissage d'un réseau de neurones, et l'apprentissage profond;\u003c/p\u003e\n\u003cp\u003e- Appliquer le perceptron multicouche à la reconnaissance visuelle de chiffre manuscrits et d'objets simples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectifs du module 4\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCOUREUR DES BOIS \u0026amp; PATENTEUX:\u003c/p\u003e\n\u003cp\u003e- Saisir le potentiel et énumérer quelques applications de la vision artificielle en agriculture.\u003c/p\u003e\n\u003cp\u003eCOUREUR DES BOIS :\u003c/p\u003e\n\u003cp\u003e- Comprendre « intuitivement » le fonctionnement d'un réseau convolutif.\u003c/p\u003e\n\u003cp\u003ePATENTEUX:\u003c/p\u003e\n\u003cp\u003e- Comprendre « pratiquement » le fonctionnement d'un réseau convolutif;\u003c/p\u003e\n\u003cp\u003e- Appliquer un réseau convolutif à la reconnaissance et à la classification d'images de chiffres manuscrits et d'objets simples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectifs du module 5\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCOUREUR DES BOIS \u0026amp; PATENTEUX:\u003c/p\u003e\n\u003cp\u003e- Connaître différentes sources de données et leurs méthodes d’acquisition;\u003c/p\u003e\n\u003cp\u003e- Connaître l’utilisation des caméras, LiDAR, des capteurs et des objets connectés;\u003c/p\u003e\n\u003cp\u003e- Comprendre l’intérêt des images satellitaires et recueillies par des drones;\u003c/p\u003e\n\u003cp\u003e- Savoir que la source essentielle des biais est dans les données;\u003c/p\u003e\n\u003cp\u003e- Connaître différentes sources de données synthétiques;\u003c/p\u003e\n\u003cp\u003e- Saisir le potentiel et énumérer quelques applications de la vision artificielle dans les pêches et l’aquaculture.\u003c/p\u003e\n\u003cp\u003eCOUREUR DES BOIS :\u003c/p\u003e\n\u003cp\u003e- Comprendre qu'avoir des quantités d'images de qualité est au coeur des applications de la vision artificielle;\u003c/p\u003e\n\u003cp\u003e- Saisir le défi de l’annotation des images.\u003c/p\u003e\n\u003cp\u003ePATENTEUX:\u003c/p\u003e\n\u003cp\u003e- Comprendre que les données doivent être de qualité et en quantité suffisante;\u003c/p\u003e\n\u003cp\u003e- Comprendre les rudiments du fonctionnement des caméras, des LiDAR, des capteurs et objets connectés;\u003c/p\u003e\n\u003cp\u003e- Moissonner des données sur la Toile;\u003c/p\u003e\n\u003cp\u003e- Connaître les principales fonctions d’un drone;\u003c/p\u003e\n\u003cp\u003e- S'initier au traitement d’images satellitaires;\u003c/p\u003e\n\u003cp\u003e- S'initier au traitement des sons;\u003c/p\u003e\n\u003cp\u003e- Annoter des images.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectifs du module 6\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCOUREUR DES BOIS \u0026amp; PATENTEUX:\u003c/p\u003e\n\u003cp\u003e- Comprendre le défi du « mur des données massives »;\u003c/p\u003e\n\u003cp\u003e- Connaître des moyens de franchir le « mur des données massives »;\u003c/p\u003e\n\u003cp\u003e- Saisir le potentiel et énumérer quelques applications de la vision artificielle en foresterie.\u003c/p\u003e\n\u003cp\u003eCOUREUR DES BOIS :\u003c/p\u003e\n\u003cp\u003e- Connaître les principes et les avantages de l’apprentissage par transfert et de l’amplification de données.\u003c/p\u003e\n\u003cp\u003ePATENTEUX:\u003c/p\u003e\n\u003cp\u003e- Appliquer l’apprentissage par transfert et l’amplification de données à la reconnaissance d’images.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectifs du module 7\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCOUREUR DES BOIS \u0026amp; PATENTEUX:\u003c/p\u003e\n\u003cp\u003e- Comprendre les différents choix techniques pour le déploiement d’une application en vision artificielle;\u003c/p\u003e\n\u003cp\u003e- Saisir le potentiel et énumérer quelques applications de la vision artificielle en environnement et faune.\u003c/p\u003e\n\u003cp\u003eCOUREUR DES BOIS :\u003c/p\u003e\n\u003cp\u003e- Comprendre la stratégie de la traversée du gouffre;\u003c/p\u003e\n\u003cp\u003e- Connaître les grands principes de la gestion de projet d’innovation;\u003c/p\u003e\n\u003cp\u003e- Saisir l’importance de la créativité;\u003c/p\u003e\n\u003cp\u003ePATENTEUX:\u003c/p\u003e\n\u003cp\u003e- Déployer une application en vision artificielle sur la Toile;\u003c/p\u003e\n\u003cp\u003e- Réaliser une application mobile en vision artificielle.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectifs du module 8\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCOUREUR DES BOIS \u0026amp; PATENTEUX:\u003c/p\u003e\n\u003cp\u003e- Être conscient de la surenchère et de l’effet de mode;\u003c/p\u003e\n\u003cp\u003e- Saisir le potentiel et énumérer quelques applications de la vision artificielle dans l’énergie et les mines.\u003c/p\u003e\n\u003cp\u003eCOUREUR DES BOIS :\u003c/p\u003e\n\u003cp\u003e- Connaître quelques ressources utiles pour aller plus loin dans son projet d'affaires.\u003c/p\u003e\n\u003cp\u003ePATENTEUX:\u003c/p\u003e\n\u003cp\u003e- Se familiariser avec la détection d’objets;\u003c/p\u003e\n\u003cp\u003e- Connaître quelques bonnes suggestions de livres et de cours pratiques.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"423:T9c5,"])</script><script>self.__next_f.push([1,"\u003cp\u003eVous voulez apprendre l'algèbre linéaire, un précieux outil complémentaire à vos connaissances acquises durant vos études en économie, ingénierie, physique, ou statistique? Ou simplement pour la beauté de la matière? Alors ce cours est fait pour vous! Outre remplir le rôle d'outil dans les différentes branches mentionnées ci-dessus (permettant la résolution de problèmes concrets), l'algèbre linéaire, qui capture l'essence des mathématiques -à savoir, l'algèbre et la géométrie- vous introduira au monde plus abstrait des mathématiques.\u003c/p\u003e\n\u003cp\u003eProposé comme complément de cours aux ingénieurs de première année à l'Ecole Polytechnique Fédérale de Lausanne, ce MOOC (composé de trois parties) n'en est pas moins un cours à part entière et peut être considéré comme une base solide d'algèbre linéaire pour tout étudiant intéressé par l'apprentissage de cette matière.\u003c/p\u003e\n\u003cp\u003eBien que les vidéos constituent le coeur du cours, des exercices de type QCM (Questions à choix multiples) ainsi que des séries au format PDF seront disponibles chaque semaine, ainsi que des corrigés appropriés. Plus précisément, les séries d'exercices seront accompagnées d'un corrigé au format PDF et certains problèmes bénéficieront d'une correction détaillée en vidéo, dans laquelle l'un des enseignants présentera la solution, étape par étape. Finalement, chaque vidéo de cours sera suivie d'un quiz, dont le but est de tester le degré d’assimilation des connaissances acquises.\u003c/p\u003e\n\u003cp\u003eLe cours est organisé en dix chapitres dans lesquels une approche très détaillée des concepts théoriques est proposée, ainsi que de multiples exemples illustratifs :\u003c/p\u003e\n\u003cp\u003e1) Systèmes d'équations linéaires.\u003c/p\u003e\n\u003cp\u003e2) Algèbre matricielle.\u003c/p\u003e\n\u003cp\u003e3) Espaces vectoriels.\u003c/p\u003e\n\u003cp\u003e4) Bases et dimensions.\u003c/p\u003e\n\u003cp\u003e5) Applications linéaires. \u003c/p\u003e\n\u003cp\u003e6) Matrices et applications linéaires.\u003c/p\u003e\n\u003cp\u003e7) Déterminants.\u003c/p\u003e\n\u003cp\u003e8) Vecteurs propres, valeurs propres, diagonalisation.\u003c/p\u003e\n\u003cp\u003e9) Produits scalaires et espaces euclidiens.\u003c/p\u003e\n\u003cp\u003e10) Matrices orthogonales et matrices symétriques.\u003c/p\u003e\n\u003cp\u003eCette première partie du cours sera dévouée à l'étude des quatre premiers chapitres cités plus haut. Aucune connaissance particulière n’est requise pour comprendre les concepts abordés dans ce MOOC, mais il est conseillé de travailler régulièrement et de manière assidue, de façon à ne pas prendre de retard lors de l'apprentissage de la matière.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"424:T5d8,\u003cp\u003e\u003cspan lang=\"FR-FR\"\u003eCe cours, présenté par le Département de la statistique du Fonds monétaire international (FMI), détaille\u003c/span\u003e aux participants les concepts de base, les définitions et la méthodologie qui permettent de compiler les indicateurs de solidité financière (ISF). Développés par le FMI à la fin des années 1990, ils sont actuellement compilés et transmis au FMI par plus de 150 pays. Ils sont fréquemment employés par les chercheurs, les experts et les décideurs politiques du monde entier pour surveiller la stabilité du système financier dans son ensemble d’un point de vue macroprudentiel. Les équipes du FMI s’en servent également pour leurs analyses de stabilité financière et leurs activités de surveillance en la matière. Ce cours porte sur l’histoire des ISF et leur application dans le domaine de la surveillance et des analyses macroprudentielles. Il détaille le cadre conceptuel des ISF, le processus de collecte des données et les méthodologies d’agrégation et de consolidation qui en sont le soubassement. Il sera également question de l’application des ISF centraux et complémentaires dans les analyses macroprudentielles. Le Guide d’établissement des indicateurs de solidité financière, dont la révision a été effectuée en 2019, constitue une référence importante tout au long de ce cours. Ce guide constitue l’autorité ultime en matière de concepts et méthodes pour les ISF et le fondement de ce cours.\u003c/p\u003e425:T4c7,\u003cp\u003e\u003cspan lang=\"FR-FR\"\u003eÀ la fin de ce cours, les participants devraient être en mesure :\u003c/span\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cspan lang=\"FR-FR\"\u003eDe définir les concepts essentiels liés aux ISF et leur application dans les domaines de la surveillance et des analyses macroprudentielles.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cspan lang=\"FR-FR\"\u003eDe définir le cadre conceptuel, y compris l’application des concepts de capital réglementaire, de ratio de levier et de liquidité, qui entrent en jeu dans la \u003c/span\u003ecompilation des ISF.\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cspan"])</script><script>self.__next_f.push([1," lang=\"FR-FR\"\u003eDe décrire le processus de préparation des données pour la compilation des ISF et d’appliquer les méthodologies recommandées pour l’agrégation et la consolidation de ces données.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cspan lang=\"FR-FR\"\u003eDe préciser chacun des ISF centraux et complémentaires pour les institutions collectrices de dépôts et d’expliquer l’application de chacun d’entre eux dans les analyses macroprudentielles.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003e\u003cspan lang=\"FR-FR\"\u003eD’identifier les données sources et de préciser les ISF pour les institutions non-collectrices de dépôts ainsi que leur application dans les analyses macroprudentielles.\u003c/span\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e426:T7c3,\u003cp\u003eBienvenue au MOOC intitulé \u003cem\u003eIntroduction à la science des données sociales.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLes données sont partout, et il faudra rapidement savoir comment les analyser pour en dériver des connaissances sur lesquelles nous pourrons prendre des décisions et des actions plus éclairées. Selon la prestigieuse revue \u003cem\u003eHarvard Business Review\u003c/em\u003e , le travail du scientifique de données sera l’emploi le plus « \u003cem\u003esexy »\u003c/em\u003e du 21ème siècle et le développement de l’intelligence numérique devient une composante incontournable du développement professionnel. Depuis quelques années, il apparaît de plus en plus évident qu’il y a une rareté, sur le marché de l’emploi, de professionnels formés comme scientifiques des données.\u003c/p\u003e\n\u003cp\u003eCe cours introductif à la science des données appliquées pour les sciences sociales, du comportement ou de la santé aborde les concepts et les différents outils permettant de débuter un projet en science des données et de faire les premières analyses descriptives. Cette formation permettra aussi de développer les capacités de créer des visualisations intéressantes des données analysées. Ce cours proposera des exercices pratiques pour découvrir les outils de la science des données et de la recherche « ouverte » inspirés des travaux de l’Open Science Framew"])</script><script>self.__next_f.push([1,"ork. Ce cours propose d’utiliser le langage R, mais aussi des outils provenant de l’environnement Python, dont Google Colaboratory (Colab) et Jupyter Notebooks. R est un langage de programmation, en statistique et apprentissage automatique, dont la popularité est grandissante en sciences sociales et de la santé.\u003c/p\u003e\n\u003cp\u003eCe cours gratuit et en ligne et asynchrone a été élaboré par Éric Lacourse (directeur scientifique) grâce au soutien de Praxis (Centre de formation professionnelle de la Faculté des Arts et Sciences), du Centre de pédagogie universitaire (CPU) et des Bibliothèques de l’Université de Montréal.\u003c/p\u003e427:T9ca,"])</script><script>self.__next_f.push([1,"\u003cp\u003eVous voulez apprendre l'algèbre linéaire, un précieux outil complémentaire à vos connaissances acquises durant vos études en économie, ingénierie, physique, ou statistique? Ou simplement pour la beauté de la matière? Alors ce cours est fait pour vous! Outre remplir le rôle d'outil dans les différentes branches mentionnées ci-dessus (permettant la résolution de problèmes concrets), l'algèbre linéaire, qui capture l'essence des mathématiques -à savoir, l'algèbre et la géométrie- vous introduira au monde plus abstrait des mathématiques.\u003c/p\u003e\n\u003cp\u003eProposé comme complément de cours aux ingénieurs de première année à l'Ecole Polytechnique Fédérale de Lausanne, ce MOOC (composé de trois parties) n'en est pas moins un cours à part entière et peut être considéré comme une base solide d'algèbre linéaire pour tout étudiant intéressé par l'apprentissage de cette matière.\u003c/p\u003e\n\u003cp\u003eBien que les vidéos constituent le coeur du cours, des exercices de type QCM (Questions à choix multiples) ainsi que des séries au format PDF seront disponibles chaque semaine, ainsi que des corrigés appropriés. Plus précisément, les séries d'exercices seront accompagnées d'un corrigé au format PDF et certains problèmes bénéficieront d'une correction détaillée en vidéo, dans laquelle l'un des enseignants présentera la solution, étape par étape. Finalement, chaque vidéo de cours sera suivie d'un quiz, dont le but est de tester le degré d’assimilation des connaissances acquises.\u003c/p\u003e\n\u003cp\u003eLe cours est organisé en dix chapitres dans lesquels une approche très détaillée des concepts théoriques est proposée, ainsi que de multiples exemples illustratifs :\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSystèmes d'équations linéaires.\u003c/li\u003e\n\u003cli\u003eAlgèbre matricielle.\u003c/li\u003e\n\u003cli\u003eEspaces vectoriels.\u003c/li\u003e\n\u003cli\u003eBases et dimensions.\u003c/li\u003e\n\u003cli\u003eApplications linéaires. \u003c/li\u003e\n\u003cli\u003eMatrices et applications linéaires.\u003c/li\u003e\n\u003cli\u003eDéterminants.\u003c/li\u003e\n\u003cli\u003eVecteurs propres, valeurs propres, diagonalisation.\u003c/li\u003e\n\u003cli\u003eProduits scalaires et espaces euclidiens.\u003c/li\u003e\n\u003cli\u003eMatrices orthogonales et matrices symétriques.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eCette deuxième partie du cours sera dévouée à l'étude des chapitres 5 à 8 cités plus haut. Une bonne connaissance de la matière enseignée dans le MOOC \u003cem\u003eAlgèbre Linéaire (Partie 1)\u003c/em\u003e est requise. Aussi, il est conseillé de travailler régulièrement et de manière assidue, de façon à ne pas prendre de retard lors de l'apprentissage de la matière.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"428:T4de,\u003cp\u003eA l’issue de ce cours, vous devriez être en mesure de : \u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003eReconnaître le rôle, la portée, les utilisations et les cadres d’élaboration des comptes nationaux trimestriels et des indicateurs de l’activité économique à haute fréquence. \u003c/li\u003e\n\u003cli\u003ePasser en revue les sources de données disponibles pour établir les CNT et les IAEHF. \u003c/li\u003e\n\u003cli\u003eExpliquer l'utilisation des mesures en volume et la relation fondamentale entre la valeur, la quantité et le prix — en développant la manière de détecter et de traiter des problèmes tels que la nécessité de mettre à jour les pondérations et de reconnaître la perte d'additivité pour les estimations en volume chaînées. \u003c/li\u003e\n\u003cli\u003eAppliquer des techniques de base pour établir des séries d’étalonnage et combler les lacunes / les écarts dans les données. \u003c/li\u003e\n\u003cli\u003eAppliquer les techniques de base de désaisonnalisation aux séries chronologiques, sur la base des bonnes pratiques de correction des variations saisonnières. \u003c/li\u003e\n\u003cli\u003eDécrire une politique de révision équilibrée en tenant compte de la manière dont une base de données en temps réel peut être utilisée pour évaluer la fiabilité des estimations des CNT et des IAEHF.\u003c/li\u003e\n\u003c/ul\u003e429:T9f9,"])</script><script>self.__next_f.push([1,"\u003cp\u003eVous voulez apprendre l'algèbre linéaire, un précieux outil complémentaire à vos connaissances acquises durant vos études en économie, ingénierie, physique, ou statistique? Ou simplement pour la beauté de la matière? Alors ce cours est fait pour vous! Outre remplir le rôle d'outil dans les différentes branches mentionnées ci-dessus (permettant la résolution de problèmes concrets), l'algèbre linéaire, qui capture l'essence des mathématiques -à savoir, l'algèbre et la géométrie- vous introduira au monde plus abstrait des mathématiques.\u003c/p\u003e\n\u003cp\u003eProposé comme complément de cours aux ingénieurs de première année à l'Ecole Polytechnique Fédérale de Lausanne, ce MOOC (composé de trois parties) n'en est pas moins un cours à part entière et peut être considéré comme une base solide d'algèbre linéaire pour tout étudiant intéressé par l'apprentissage de cette matière.\u003c/p\u003e\n\u003cp\u003eBien que les vidéos constituent le coeur du cours, des exercices de type QCM (Questions à choix multiples) ainsi que des séries au format PDF seront disponibles chaque semaine, ainsi que des corrigés appropriés. Plus précisément, les séries d'exercices seront accompagnées d'un corrigé au format PDF et certains problèmes bénéficieront d'une correction détaillée en vidéo, dans laquelle l'un des enseignants présentera la solution, étape par étape. Finalement, chaque vidéo de cours sera suivie d'un quiz, dont le but est de tester le degré d’assimilation des connaissances acquises.\u003c/p\u003e\n\u003cp\u003eLe cours est organisé en dix chapitres dans lesquels une approche très détaillée des concepts théoriques est proposée, ainsi que de multiples exemples illustratifs :\u003c/p\u003e\n\u003col\u003e\n\u003cli\u003eSystèmes d'équations linéaires.\u003c/li\u003e\n\u003cli\u003eAlgèbre matricielle.\u003c/li\u003e\n\u003cli\u003eEspaces vectoriels.\u003c/li\u003e\n\u003cli\u003eBases et dimensions.\u003c/li\u003e\n\u003cli\u003eApplications linéaires. \u003c/li\u003e\n\u003cli\u003eMatrices et applications linéaires.\u003c/li\u003e\n\u003cli\u003eDéterminants.\u003c/li\u003e\n\u003cli\u003eVecteurs propres, valeurs propres, diagonalisation.\u003c/li\u003e\n\u003cli\u003eProduits scalaires et espaces euclidiens.\u003c/li\u003e\n\u003cli\u003eMatrices orthogonales et matrices symétriques.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eCette troisième (et dernière) partie du cours sera dévouée à l'étude des chapitres 9 et 10 cités plus haut. Une bonne connaissance de la matière enseignée dans les MOOCs _Algèbre Linéaire (Partie 1) _et _Algébre Linéaire (Partie 2) _est requise. Aussi, il est conseillé de travailler régulièrement et de manière assidue, de façon à ne pas prendre de retard lors de l'apprentissage de la matière.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"42a:T489,\u003cp\u003eA la fin du cours, l'étudiant sera capable\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003ede définir les concepts théoriques introduits en cours et d'en donner des exemples illustratifs ;\u003c/li\u003e\n\u003cli\u003ede reconnaître un produit scalaire et maîtriser les propriétés associées à un tel objet (e.g. Inégalité de Cauchy-Schwarz, inégalité du triangle) ;\u003c/li\u003e\n\u003cli\u003ede et maîtriser les notions de bases liées à l'orthogonalité (e.g. familles/bases orthogonales, familles/bases orthonormales, orthogonal d'un sous-espace, Théorème de Pythagore) ;\u003c/li\u003e\n\u003cli\u003ede construire une base orthonormée d'un sous-espace vectoriel d'un espace euclidien à l'aide du procédé de Gram-Schmidt ;\u003c/li\u003e\n\u003cli\u003ede calculer la meilleure approximation quadratique d'un vecteur ;\u003c/li\u003e\n\u003cli\u003ede calculer la solution au sens des moindres carrés d'un système linéaire ;\u003c/li\u003e\n\u003cli\u003ede calculer la factorisation QR d'une matrice donnée, lorsque cela est possible ;\u003c/li\u003e\n\u003cli\u003ede diagonaliser orthogonalement une matrice symétrique donnée ;\u003c/li\u003e\n\u003cli\u003ede déterminer les axes principaux d'une forme quadratique donnée ;\u003c/li\u003e\n\u003cli\u003ede calculer la décomposition en valeurs singulières d'une matrice donnée.\u003c/li\u003e\n\u003c/ul\u003e42b:T883,"])</script><script>self.__next_f.push([1,"\u003cp\u003e\u003cspan lang=\"FR\"\u003eDans ce cours en ligne, vous découvrirez les notions théoriques sur lesquelles repose la conception du modèle d'estimation de l’écart de taxe sur la valeur ajoutée (TVA) du Programme d’analyse des écarts\u003c/span\u003e\u003cspan lang=\"FR\"\u003e\u003c/span\u003e\u003cspan lang=\"FR\"\u003e du Fonds monétaire international à l’intention des administrations fiscales ; en outre, vous apprendrez à utiliser ce modèle afin de produire vos propres estimations de l’écart de TVA. Vous vous familiariserez avec sa structure générale et le mode d'interaction de ses composantes. Vous apprendrez quelles sont les données nécessaires au modèle ainsi que la façon de les établir et vous verrez comment le modèle les utilise pour calculer la TVA potentielle, qui est comparée à la TVA effective pour déterminer l’écart de TVA.\u003c/span\u003e\u003cspan lang=\"FR\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eLe cours en ligne comprend cinq composantes principales, qualifiées de modules. Le premier module commence par donner un aperçu général du concept d’écart fiscal, puis expose la théorie sur laquelle s’appuie la conception du modèle d'estimation de l’écart de TVA. Le deuxième module explique comment les divers systèmes de TVA doivent être intégrés au modèle. Le troisième module donne des indications sur l’établissement des différentes mesures de la TVA effective en vue de leur intégration au modèle et explique pourquoi elles sont nécessaires. Le quatrième module est consacré à l’élaboration des données statistiques dont on a besoin pour calculer l’assiette de la TVA potentielle et indique comment elle est intégrée au modèle. Le dernier module montre comment exécuter le modèle pour obtenir les résultats et, surtout, comment les analyser et les interpréter.\u003cspan lang=\"FR\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eEn bref, le cours est conçu pour permettre aux pays d’effectuer régulièrement et sur la même base des estimations de l’écart de TVA en se servant du modèle d'estimation de l'écart de TVA du programme RA-GAP du FMI, qui est bien établi.\u003cspan lang=\"FR\"\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eCe cours en ligne est offert par le FMI avec le soutien financier du gouvernement japonais.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"42c:T622,\u003cp\u003eAs the world becomes more data driven, future-focused professionals need to develop the quantitative skills to inform corporate decision-making and managerial strategy. Guided by experts from the London School of Economics and Political Science (LSE), this online certificate course empowers you with the knowledge and practical tools to understand, interpret, and communicate data relevant to your role and organization. \u003c/p\u003e\n\u003cp\u003eDevelop an understanding of how data-driven models can improve your ability to make decisions in a fast-paced world. Over the course of eight weeks, you’ll participate in a capstone project and apply the techniques and concepts covered to extract business insights from a real data set. You’ll also gain experience using Tableau —– a leading business intelligence and data analytics software —– to visualize and report on insights extracted from data sets. \u003c/p\u003e\n\u003cp\u003eThis LSE course will equip managers and analysts in a variety of industries with the skills to make data-driven decisions. Marketing and sales analysts will gain the ability to extract and interpret key business insights for competitive advantage. Similarly, finance, HR, and business analysts will develop data analysis skills that can be directly applied in their role and organization. There are no formal prerequisites for this course, but some numerical literacy is advantageous, as well as a basic working knowledge of Microsoft Excel. You’ll be granted a student license to download and use Tableau free of charge, for the duration of the course.\u003c/p\u003e42d: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,"42e:T610,\u003cp\u003eAs the increase in data changes the way in which organizations operate, professionals with a hybrid blend of analytical skills and domain-specific expertise are required to propel data-driven business decisions. In the Rice University Data Analysis and Visualization online short course, you’ll develop the skills and knowledge to effectively analyze, visualize, and communicate data insights within your organization. \u003c/p\u003e\n\u003cp\u003eGuided by industry experts, you’ll discover how to navigate technical tools such as Tableau, SQL, and VBA. Learn to harness the fundamentals of data analysis such as advanced Excel functions, databases, and practical statistics. Through data-driven insights and effective visualization techniques, you’ll learn to communicate effectively with stakeholders through data storytelling.\u003c/p\u003e\n\u003cp\u003eThis course is designed for professionals in data-driven roles, for decision makers who need to leverage data effectively, and for those interested in moving into these spaces. Functional analysts will further their ability to inform specific functions and business decisions, while managers will benefit from the ability to use analytics tools in their decision making. Any professional interested in taking the first steps toward an analytical role will also benefit from a new understanding of data analysis and visualization.\u003c/p\u003e\n\u003cp\u003eA basic proficiency in Excel is recommended, and while there are no prerequisites for this course, it’s recommended that students have a basic understanding of SQL before registering.\u003c/p\u003e42f:T68f,\u003cp\u003eThe transformative power of data and analytics has never been more important than it is today. Companies increasingly need to deliver insights on customer experience and business trends to remain competitive in the market.\u003c/p\u003e\n\u003cp\u003eThe Business Analytics online short course from the University of Cape Town (UCT) aims to equip you with advanced data analysis techniques to support business decision making and critical thinking in times of ambiguity and uncertainty.\u003c/p\u003e\n\u003cp\u003eTh"])</script><script>self.__next_f.push([1,"is course focuses on practical business application by showing you how to harness data visualization and storytelling to interpret number sets, identify patterns, and uncover business trends critical to driving growth in your organization.\u003c/p\u003e\n\u003cp\u003eBy practically engaging with various analytical tools (such as SQL and Tableau, as well as Python on an introductory level), this course will empower you to tap into the power of business intelligence to transform your data and deliver actionable insights.\u003c/p\u003e\n\u003cp\u003eAs this is an advanced course, it’s ideal for practicing analysts who want to update their knowledge, grow their careers, and improve their ability to impact business decisions with data insights. It’s also designed for those who aspire to become data scientists and for managers and senior decision makers who want to learn how to leverage data to make informed choices and add more value to their organizations. While there are no prerequisites for this course, it’s recommended that learners have a basic understanding of statistics and analytics before registering. Notably, students will engage with and run code via an IDE notebook but won’t have to write code themselves.\u003c/p\u003e430:T66f,\u003cp\u003eIn a world that’s increasingly data-driven, organizations need professionals who can extract meaningful insights from data to make better business decisions. \u003c/p\u003e\n\u003cp\u003eOn the Data Science with Python online short course from the University of Cape Town (UCT), you’ll have the opportunity to develop practical data science and analysis skills for use in everyday business scenarios. Over the course of eight weeks, you’ll cover widely applicable Python libraries and learn how these methods can be, and are, used in day-to-day business situations. \u003c/p\u003e\n\u003cp\u003eGain an introduction into statistical learning, which will provide a foundation on the mechanics of machine learning. You’ll explore supervised learning using tree-based models and neural networks, as well as unsupervised learning using K-means and hierarchical clustering. "])</script><script>self.__next_f.push([1,"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\u003e431:T8b1,"])</script><script>self.__next_f.push([1,"\u003cp\u003eThe US healthcare system wastes billions of dollars annually as a result of administrative inefficiencies.1 In light of this, there’s an increasing demand for clinicians and health-services managers who understand the operational and economic impact of their everyday business decisions.2\u003c/p\u003e\n\u003cp\u003eThe eight-week Healthcare Management online program from the Yale School of Management Executive Education will give you a grounding in the business principles and organizational practices that underpin the modern healthcare system. \u003c/p\u003e\n\u003cp\u003eThroughout the program, you’ll gain practical health administration and operational skills to help you manage finances, maximize throughput, optimize patient care, and ultimately become a more effective leader. \u003c/p\u003e\n\u003cp\u003eWith a unique, integrated curriculum and specific industry case studies that shed light on the US healthcare system, you’ll find actionable insights applicable to your own context. Sharpen your healthcare-management skills, gain the tools to drive change in your organization, and earn a certificate of participation online from the Yale SOM Executive Education. \u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1\u003ca href=\"https://jamanetwork.com/journals/jama/article-abstract/2752664?guestAccessKey=bf8f9802-be69-4224-a67f-42bf2c53e027\u0026utm_source=For_The_Media\u0026utm_medium=referral\u0026utm_campaign=ftm_links\u0026utm_content=tfl\u0026utm_term=100719\" target=\"_blank\"\u003e JAMA Network\u003c/a\u003e (Oct, 2019).\u003cbr /\u003e\n2\u003ca href=\"https://www.bls.gov/ooh/management/medical-and-health-services-managers.htm\" target=\"_blank\"\u003e Bureau of Labor Statistics\u003c/a\u003e (Jun, 2021).\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis program is aimed at both medical and non-medical professionals who are looking to gain a fundamental understanding of business and organizational principles, and their application in healthcare.\u003c/p\u003e\n\u003cp\u003eIt’s ideal for medical professionals who currently manage or aim to run their own private practices, and who require the financial and managerial skills to do so, or for those who are looking to transition into a more administrative or senior role within healthcare. Physicians, registered nurses, surgeons, and allied health professionals will also benefit from this introduction to the essentials of health-services administration.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"432: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\u003e433:T9fd,"])</script><script>self.__next_f.push([1,"\u003cp\u003eChanging tenant expectations, sustainability concerns, and where people choose to live due to work-from-home protocols are reshaping the commercial and residential real estate markets. It requires data and quantitative analysis skills to predict the full impact of this on the property sector, and investment and development decisions. \u003c/p\u003e\n\u003cp\u003eThe Real Estate Financial Analytics online short course from the MIT School of Architecture and Planning (MIT SA+P) will enable you to transform uncertainty (‘unknown unknowns’) into risk (‘known unknowns’). You’ll gain the technical skills and tools to assess the viability of residential and commercial real estate investments using rigorous analysis techniques, simulation modeling, and financial modeling. This six-week program introduces real estate price dynamics and teaches you to quantitatively model them using Microsoft Excel, in order to understand some of the challenges of real estate price indexing. It also explores the lure and limitations of forecasting and how to make more accurate predictions using Monte Carlo simulation. \u003c/p\u003e\n\u003cp\u003eIn addition, you’ll explore how flexibility in the context of uncertainty can add value to investments, as well as assess the quantitative and qualitative value of real estate investment decision flexibility using simulation modeling. With guidance from MIT academics and industry experts, you’ll discover the potential for new financial engineering tools based on real estate price indexing.\u003c/p\u003e\n\u003cp\u003eThis program is designed to help equip finance and built environment professionals with the knowledge and techniques to navigate real estate investment decisions successfully. Whether for personal investment management or in a professional capacity, this course aims to provide you with the skills and tools to make better recommendations and projections, identify high-return investments, and build an enduringly strong portfolio.\u003c/p\u003e\n\u003cp\u003eBusiness leaders, financial executives, real estate entrepreneurs, developers, and fund managers and advisers will build on their current skill set, and gain insight into the latest investment trends and structures. Analysts — especially those looking to hone in-demand, specialist real estate expertise — will benefit from the expert thought leadership in areas such as property price dynamics and valuation in the capital market. Due to the technical nature of this course, it’s best suited to those with existing knowledge of finance or economic analysis and experience using Microsoft Excel.\u003c/p\u003e"])</script><script>self.__next_f.push([1,"434:Ta49,"])</script><script>self.__next_f.push([1,"\u003cp\u003eDuration: 6 weeks (excluding orientation)\u003c/p\u003e\n\u003cp\u003eEffective data visualisation and storytelling has the power to transform complex data into impactful insights that guide informed decision-making. This crucial skill enables businesses to spot patterns, make strategic decisions, and predict future trends, helping them to build data-driven growth and success.\u003c/p\u003e\n\u003cp\u003eUnearth the power of data with the \u003cstrong\u003eApplied Data Visualisation and Analysis for Business\u003c/strong\u003e online certificate course from the London School of Economics and Political Science (LSE). Over six weeks, you'll learn the art of converting raw data into captivating visual narratives using Tableau. You’ll also be armed with a suite of econometric and statistical modelling tools in Microsoft Excel. Guided by experts, you’ll gain a deep understanding of the technical details that support effective data visualisations and the expertise needed to thrive in a data-centric environment. \u003c/p\u003e\n\u003cp\u003eThis course is intended for business professionals seeking to boost their technical data visualisation and analysis skill set and grow their ability to influence decision-makers through data storytelling. It's also appropriate for those wishing to enhance their mastery of data visualisation using Tableau and data modelling using Excel. \u003c/p\u003e\n\u003cp\u003eThose interested in the course should ideally be familiar with Tableau basics (the interface, connecting to datasets, and creating basic visualisations) and introductory statistical concepts. There will, however, be a light refresher on these concepts in the course material.\u003c/p\u003e\n\u003cp\u003eIf you don’t have these skills yet, or feel you could benefit from a more-in depth refresher, the LSE \u003ca href=\"https://www.getsmarter.com/products/lse-data-analysis-for-management-online-certificate-course\" target=\"_blank\"\u003eData Analysis for Management\u003c/a\u003e online certificate course can provide you with Tableau fundamentals.\u003c/p\u003e\n\u003cp\u003e\u003cimg alt=\"\" src=\"https://www.getsmarter.com/disk/public/tVYZwNpzsTmzB9pbhBDXkJM1/cpd_accreditation_logo.png\" /\u003e \u003c/p\u003e\n\u003cp\u003eThis Applied Data Visualisation and Analysis for Business online certificate course is certified by the United Kingdom CPD Certification Service, and may be applicable to individuals who are members of, or are associated with, UK-based professional bodies. The course has an estimated 54 hours of learning.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: should you wish to claim CPD activity, the onus is on you. The London School of Economics and Political Science (LSE) and GetSmarter accept no responsibility, and cannot be held responsible, for the claiming or validation of hours or points.\u003c/em\u003e\u003c/p\u003e"])</script><script>self.__next_f.push([1,"435:T684,\u003cp\u003eWith more and more businesses relying on statistical analysis to drive decision-making, the ability to extract meaning from data has become a highly valued skill. Data analysts play a key role in helping business leaders make informed choices, distinguish between effective and ineffective practices, cut costs, and solve key problems — they are, therefore, an integral part of any successful organizational team.\u003c/p\u003e\n\u003cp\u003eThe Data Analysis online short course from the University of Cape Town (UCT) will introduce you to the fundamentals of data analysis and the broader potential of analytics in business. Over the course of eight modules, you’ll learn how data is collected, stored, organized, analysed, and interpreted, and you’ll be given the chance to practice data analysis techniques on real-world data sets. Ultimately, you’ll walk away with practical skills that you can apply immediately in your organization to address specific business needs in the areas of finance, sales, marketing, operational management, and human resource management.\u003c/p\u003e\n\u003cp\u003eAnyone interested in moving into the realms of data science and analytics will find this UCT online short course useful. The content is geared towards those eager to update their skill sets to remain relevant, as well as established professionals looking to unlock new opportunities within or outside their current organizations. Data analysts, and others who already work with data, can add to existing certifications by learning how to better leverage numbers to achieve results and drive growth. Students will need access to Microsoft Excel to complete the practical components of the course.\u003c/p\u003e436:T499,In today's dynamic business landscape, data-driven decision-making is key. That's why we've designed a skills-based learning program that not only equips you with the latest analytics tools and techniques but also connects you with industry micro-credentials that are in high demand.\r\nBecome a data analyst ready to analyze and to visualize data for strategic de"])</script><script>self.__next_f.push([1,"cision making \r\nStudy contemporary data analyst skills from content derived from leading, global organizations\r\nStudy consumers’ buying habits to help businesses make more strategic decisions about how they market their products and services. \r\nMaster the process of gleaning insights from data to inform better business decisions\r\nLearn in-demand job skills from first day of instruction and bring them into the workplace\r\nThe Masters of Science in Business Analytics is designed to prepare learners for data analyst careers. A data analyst finds a solution to a problem or provides an answer to a question. This career tasks a data analyst to gather, purify, and analyze data sets. Contemporary data analysts work in a variety of fields, including government, business, finance, law enforcement, and science.437:T67f,- #78 National Universities - U.S. News \u0026 World Report, 2019\n- #5 Best U.S. Cities for Jobs - Fortune, 2018\n- Create meaningful change in America’s vibrant political, historical \u0026 cultural capital city\n-An undergraduate degree in Statistics teaches students the principles of statistical theory and methods, including probability theory, data analysis, and statistical inference.\n-Students learn how to design experiments and studies, collect and analyze data, and draw conclusions from statistical analysis.\n-They study statistical modeling and regression analysis, using mathematical and statistical tools to model relationships between variables.\n-Statistics students develop skills in data visualization and communication, including graphing, charting, and presenting data.\n-They learn about the applications of statistics in fields such as biology, economics, psychology, and public health.\n-Students study the principles of machine learning and data mining, using statistical and computational methods to analyze large datasets.\n-Statistics students learn how to use statistical software and computer programming languages to solve statistical problems and analyze data.\n-They also study the history and philosophy o"])</script><script>self.__next_f.push([1,"f statistics, exploring the development of statistical ideas and their relationship to other fields of study.\n-Statistics students learn how to communicate statistical ideas and findings through technical writing and presentations.\n-Finally, an undergraduate degree in Statistics prepares students for careers in various fields such as academia, industry, and government, as well as for further study in graduate and professional programs.438:T7ae,Florida International University is one of the largest public research universities in the US. The FIU Global First Year program prepares you for academic, social, and professional success. Florida International University offers 110+ undergraduate programs. International business, hospitality, engineering, and criminal justice are among FIU’s top fields of study.\n-An undergraduate degree in Mathematics and Statistics teaches students the principles of pure and applied mathematics, including calculus, algebra, and analysis, as well as statistical theory and methods.\n-Students learn about mathematical proof and how to construct rigorous and logical arguments.\n-They study the foundations of probability theory and statistics, including methods of statistical inference and data analysis.\n-Mathematics and Statistics students develop problem-solving skills, critical thinking, and logical reasoning that are useful in a wide range of fields.\n-They learn about the applications of mathematics and statistics in fields such as physics, engineering, finance, and data science.\n-Students study the principles of mathematical modeling and simulation, using mathematical and statistical tools to solve real-world problems.\n-Mathematics and Statistics students learn how to use mathematical and statistical software and computer programming languages to solve mathematical and statistical problems.\n-They also study the history and philosophy of mathematics and statistics, exploring the development of mathematical and statistical ideas and their relationship to other fields of study.\n-Mathematics"])</script><script>self.__next_f.push([1," and Statistics students learn how to communicate mathematical and statistical ideas and findings through technical writing and presentations.\n-Finally, an undergraduate degree in Mathematics and Statistics prepares students for careers in various fields such as academia, industry, and government, as well as for further study in graduate and professional programs.439:T707,UMass Boston, located in America’s most celebrated college city, combines the resources of a major research university and the accessibility of a public institution. With 65+ courses of study and a prestigious Honors College, UMass Boston gives you access to career opportunities, research projects, and a strong alumni network. With 11% of the student body representing 140+ countries and speaking 60+ languages, UMass Boston is truly global.\n-An undergraduate degree in Mathematics teaches students the principles of pure and applied mathematics, including calculus, algebra, and analysis.\n-Students learn about mathematical proof and how to construct rigorous and logical arguments.\n-They study the foundations of geometry, topology, and number theory.\n-Mathematics students develop problem-solving skills, critical thinking, and logical reasoning that are useful in a wide range of fields.\n-They learn about the applications of mathematics in fields such as physics, engineering, finance, and computer science.\n-Students study the principles of probability theory and statistics, including methods of statistical inference and data analysis.\n-Mathematics students learn how to use mathematical software and computer programming languages to solve mathematical problems and simulate real-world scenarios.\n-They also study the history and philosophy of mathematics, exploring the development of mathematical ideas and their relationship to other fields of study.\n-Mathematics students learn how to communicate mathematical ideas and findings through technical writing and presentations.\n-Finally, an undergraduate degree in Mathematics prepares students for careers in "])</script><script>self.__next_f.push([1,"various fields such as academia, industry, and government, as well as for further study in graduate and professional programs.43a:T85d,"])</script><script>self.__next_f.push([1,"Western New England University offers a hands-on and personalized educational experience, with small class sizes and nurturing faculty. The 215-acre campus offers a vibrant community where students can explore cutting-edge research, entrepreneurial prospects, and creative pursuits, all while building a wide professional network. With degree programs in high-demand fields such as engineering, health, pharmaceuticals, and business, Western New England University prepares students to get started on their career journey. Learn from industry professionals as you develop key skills and gain in-depth knowledge that will help you stand out to employers in the US, or anywhere in the world.\n-An undergraduate degree in Mathematical Sciences teaches students the principles of pure and applied mathematics, including calculus, algebra, and analysis.\n-Students learn about the use of mathematical models to solve real-world problems in fields such as engineering, physics, and finance.\n-They study probability theory and statistics, including methods of statistical inference and data analysis.\n-Mathematical Sciences students learn about the foundations of computer science, including algorithms, programming, and data structures.\n-They also study the principles of mathematical logic and the foundations of mathematics, including set theory and topology.\n-Students develop skills in problem-solving, critical thinking, and logical reasoning, which are useful in a wide range of fields.\n-They learn about the use of mathematical software and computer programming languages to solve mathematical problems and simulate real-world scenarios.\n-Mathematical Sciences students study the history and philosophy of mathematics, exploring the development of mathematical ideas and their relationship to other fields of study.\n-They learn how to communicate mathematical ideas and findings through technical writing and presentations.\n-Finally, an undergraduate degree in Mathematical Sciences prepares students for careers in various fields such as academia, industry, and government, as well as for further study in graduate and professional programs."])</script><script>self.__next_f.push([1,"43b:Ta6f,"])</script><script>self.__next_f.push([1,"Gonzaga University’s humanistic heritage focuses on educating the mind, body, and spirit, and developing personal, academic, and professional growth through critical thought and creative innovation. Gonzaga University’s humanistic heritage focuses on educating the mind, body, and spirit, and developing personal, academic, and professional growth through critical thought and creative innovation.\n-In an undergraduate Applied Mathematics degree, students learn mathematical theories and modeling techniques and apply them to solve real-world problems in various fields. The curriculum typically includes courses in calculus, linear algebra, differential equations, probability theory, statistics, numerical analysis, and optimization. The degree program includes projects, research opportunities, and internships, and students may also study advanced topics in mathematics.\n-Calculus: Students learn calculus, including differentiation and integration, which is used to model real-world problems and analyze changes in physical and mathematical systems.\n-Linear algebra: Students learn linear algebra, including matrix algebra and eigenvalue analysis, which is used to solve systems of linear equations and in data analysis.\n-Differential equations: Students learn differential equations, which are used to model physical phenomena and systems that change over time.\n-Probability theory: Students learn probability theory, including random variables and distributions, which is used in data analysis and modeling of random phenomena.\n-Statistics: Students learn statistical methods and techniques, including regression analysis, hypothesis testing, and sampling theory, which are used in data analysis and empirical research.\n-Numerical analysis: Students learn numerical methods for solving mathematical problems that are difficult or impossible to solve analytically, such as systems of differential equations or optimization problems.\n-Optimization: Students learn optimization techniques, including linear and nonlinear programming, which are used to solve problems and model decision-making in various fields.\n-Mathematical modeling: Students learn how to model real-world problems using mathematical tools and techniques, and how to interpret and communicate the results.\n-Computer programming: Students learn computer programming and algorithm design, which is used to solve mathematical problems and implement numerical methods.\n-Advanced topics: Students may study advanced topics in mathematics, such as abstract algebra, topology, real analysis, and partial differential equations, to gain a deeper understanding of mathematical theories and applications."])</script><script>self.__next_f.push([1,"43c:T636,- #147 National Universities - U.S. News \u0026 World Report, 2019\n- A true campus experience just minutes from the cultural and financial capital of the world\n- 240+ major companies recruit on campus Apple, IBM, AIG, Pepsi and Merrill Lynch\n-An undergraduate degree in Mathematics teaches students the principles of pure and applied mathematics, including calculus, algebra, and analysis.\n-Students learn about mathematical proof and how to construct rigorous and logical arguments.\n-They study the foundations of geometry, topology, and number theory.\n-Mathematics students develop problem-solving skills, critical thinking, and logical reasoning that are useful in a wide range of fields.\n-They learn about the applications of mathematics in fields such as physics, engineering, finance, and computer science.\n-Students study the principles of probability theory and statistics, including methods of statistical inference and data analysis.\n-Mathematics students learn how to use mathematical software and computer programming languages to solve mathematical problems and simulate real-world scenarios.\n-They also study the history and philosophy of mathematics, exploring the development of mathematical ideas and their relationship to other fields of study.\n-Mathematics students learn how to communicate mathematical ideas and findings through technical writing and presentations.\n-Finally, an undergraduate degree in Mathematics prepares students for careers in various fields such as academia, industry, and government, as well as for further study in graduate and professional programs.43d:Ta16,"])</script><script>self.__next_f.push([1,"University of the Pacific's undergraduate programs are offered at the Stockton, California campus and offer world-class instruction in a supportive and challenging atmosphere. University of the Pacific offers 80+ undergraduate majors, small class sizes, a diverse campus experience, and internship opportunities.\n-In an undergraduate Applied Mathematics degree, students learn mathematical theories and modeling techniques and apply them to solve real-world problems in various fields. The curriculum typically includes courses in calculus, linear algebra, differential equations, probability theory, statistics, numerical analysis, and optimization. The degree program includes projects, research opportunities, and internships, and students may also study advanced topics in mathematics.\n-Calculus: Students learn calculus, including differentiation and integration, which is used to model real-world problems and analyze changes in physical and mathematical systems.\n-Linear algebra: Students learn linear algebra, including matrix algebra and eigenvalue analysis, which is used to solve systems of linear equations and in data analysis.\n-Differential equations: Students learn differential equations, which are used to model physical phenomena and systems that change over time.\n-Probability theory: Students learn probability theory, including random variables and distributions, which is used in data analysis and modeling of random phenomena.\n-Statistics: Students learn statistical methods and techniques, including regression analysis, hypothesis testing, and sampling theory, which are used in data analysis and empirical research.\n-Numerical analysis: Students learn numerical methods for solving mathematical problems that are difficult or impossible to solve analytically, such as systems of differential equations or optimization problems.\n-Optimization: Students learn optimization techniques, including linear and nonlinear programming, which are used to solve problems and model decision-making in various fields.\n-Mathematical modeling: Students learn how to model real-world problems using mathematical tools and techniques, and how to interpret and communicate the results.\n-Computer programming: Students learn computer programming and algorithm design, which is used to solve mathematical problems and implement numerical methods.\n-Advanced topics: Students may study advanced topics in mathematics, such as abstract algebra, topology, real analysis, and partial differential equations, to gain a deeper understanding of mathematical theories and applications."])</script><script>self.__next_f.push([1,"43e:T951,"])</script><script>self.__next_f.push([1,"The University of Dayton is a top-tier research university dedicated to academic excellence, community leadership, entrepreneurship, and creating a positive global impact. University of Dayton offers international students 80+ undergraduate degree options. The University of Dayton’s popular and highly ranked programs include engineering, entrepreneurship, business, and aerospace and aviation engineering.\n-In an undergraduate Applied Mathematical Economics degree, students learn mathematical modeling techniques to analyze economic systems and data, including courses in calculus, linear algebra, probability theory, statistics, optimization, game theory, and econometrics. They also apply these tools to real-world economic problems and may study microeconomics, macroeconomics, international trade, and development economics. \n-Mathematical modeling: Students learn mathematical modeling techniques to represent and analyze economic systems and data.\n-Calculus: Students learn calculus, including differentiation and integration, which is used extensively in economic analysis and optimization.\n-Linear algebra: Students learn linear algebra, including matrix algebra and eigenvalue analysis, which is used to solve systems of linear equations and in data analysis.\n-Differential equations: Students learn differential equations, which are used to model dynamic economic systems.\n-Probability theory: Students learn probability theory, including random variables and distributions, which is used in econometric analysis and financial modeling.\n-Statistics: Students learn statistical methods and techniques, including regression analysis, hypothesis testing, and sampling theory, which are used in data analysis and empirical research.\n-Optimization: Students learn optimization techniques, including linear and nonlinear programming, which are used to solve economic problems and model decision-making.\n-Game theory: Students learn game theory, which is used to analyze strategic interactions between individuals or groups in economic systems.\n-Econometrics: Students learn econometric methods, which are used to estimate and test economic models using data.\n-Microeconomics and macroeconomics: Students study microeconomic and macroeconomic concepts, including consumer and producer behavior, market structures, national income accounting, monetary policy, and fiscal policy."])</script><script>self.__next_f.push([1,"43f:T9a9,"])</script><script>self.__next_f.push([1,"- #78 National Universities - U.S. News \u0026 World Report, 2019\n- #5 Best U.S. Cities for Jobs - Fortune, 2018\n- Create meaningful change in America’s vibrant political, historical \u0026 cultural capital city\n-In an undergraduate Applied Mathematics degree, students learn mathematical theories and modeling techniques and apply them to solve real-world problems in various fields. The curriculum typically includes courses in calculus, linear algebra, differential equations, probability theory, statistics, numerical analysis, and optimization. The degree program includes projects, research opportunities, and internships, and students may also study advanced topics in mathematics.\n-Calculus: Students learn calculus, including differentiation and integration, which is used to model real-world problems and analyze changes in physical and mathematical systems.\n-Linear algebra: Students learn linear algebra, including matrix algebra and eigenvalue analysis, which is used to solve systems of linear equations and in data analysis.\n-Differential equations: Students learn differential equations, which are used to model physical phenomena and systems that change over time.\n-Probability theory: Students learn probability theory, including random variables and distributions, which is used in data analysis and modeling of random phenomena.\n-Statistics: Students learn statistical methods and techniques, including regression analysis, hypothesis testing, and sampling theory, which are used in data analysis and empirical research.\n-Numerical analysis: Students learn numerical methods for solving mathematical problems that are difficult or impossible to solve analytically, such as systems of differential equations or optimization problems.\n-Optimization: Students learn optimization techniques, including linear and nonlinear programming, which are used to solve problems and model decision-making in various fields.\n-Mathematical modeling: Students learn how to model real-world problems using mathematical tools and techniques, and how to interpret and communicate the results.\n-Computer programming: Students learn computer programming and algorithm design, which is used to solve mathematical problems and implement numerical methods.\n-Advanced topics: Students may study advanced topics in mathematics, such as abstract algebra, topology, real analysis, and partial differential equations, to gain a deeper understanding of mathematical theories and applications."])</script><script>self.__next_f.push([1,"440:T6d8,Louisiana State University is a Top 100 Public University with a dedicated focus on student involvement, research opportunities, and experiential learning. With more than 330 fields of study and 70 majors, LSU offers students opportunities for hands-on experience working alongside world-class faculty. Top-ranked and popular programs include business, engineering, petroleum engineering, and STEM.\n-An undergraduate degree in Mathematics teaches students the principles of pure and applied mathematics, including calculus, algebra, and analysis.\n-Students learn about mathematical proof and how to construct rigorous and logical arguments.\n-They study the foundations of geometry, topology, and number theory.\n-Mathematics students develop problem-solving skills, critical thinking, and logical reasoning that are useful in a wide range of fields.\n-They learn about the applications of mathematics in fields such as physics, engineering, finance, and computer science.\n-Students study the principles of probability theory and statistics, including methods of statistical inference and data analysis.\n-Mathematics students learn how to use mathematical software and computer programming languages to solve mathematical problems and simulate real-world scenarios.\n-They also study the history and philosophy of mathematics, exploring the development of mathematical ideas and their relationship to other fields of study.\n-Mathematics students learn how to communicate mathematical ideas and findings through technical writing and presentations.\n-Finally, an undergraduate degree in Mathematics prepares students for careers in various fields such as academia, industry, and government, as well as for further study in graduate and professional programs.441:T6a9,Auburn University prepares you for success with its prestigious academic programs, emphasis on hands-on learning experiences, and family spirit. Auburn University offers more than 150 undergraduate degrees, including top-ranked programs in engineering, business, supply chain ma"])</script><script>self.__next_f.push([1,"nagement, journalism, architecture and design, and fisheries/aquaculture.\n-An undergraduate degree in Mathematics teaches students the principles of pure and applied mathematics, including calculus, algebra, and analysis.\n-Students learn about mathematical proof and how to construct rigorous and logical arguments.\n-They study the foundations of geometry, topology, and number theory.\n-Mathematics students develop problem-solving skills, critical thinking, and logical reasoning that are useful in a wide range of fields.\n-They learn about the applications of mathematics in fields such as physics, engineering, finance, and computer science.\n-Students study the principles of probability theory and statistics, including methods of statistical inference and data analysis.\n-Mathematics students learn how to use mathematical software and computer programming languages to solve mathematical problems and simulate real-world scenarios.\n-They also study the history and philosophy of mathematics, exploring the development of mathematical ideas and their relationship to other fields of study.\n-Mathematics students learn how to communicate mathematical ideas and findings through technical writing and presentations.\n-Finally, an undergraduate degree in Mathematics prepares students for careers in various fields such as academia, industry, and government, as well as for further study in graduate and professional programs.442:T6c5,The University of Illinois Chicago provides a hands-on learning experience in a supportive, diverse environment. Located in downtown Chicago, UIC offers you the opportunity to live, learn, and excel in the third-largest city in the US. UIC’s top-ranked programs include engineering, business, architecture, design, education, health sciences, public health, and public affairs.\n-An undergraduate degree in Mathematics teaches students the principles of pure and applied mathematics, including calculus, algebra, and analysis.\n-Students learn about mathematical proof and how to construct rigorous and logical a"])</script><script>self.__next_f.push([1,"rguments.\n-They study the foundations of geometry, topology, and number theory.\n-Mathematics students develop problem-solving skills, critical thinking, and logical reasoning that are useful in a wide range of fields.\n-They learn about the applications of mathematics in fields such as physics, engineering, finance, and computer science.\n-Students study the principles of probability theory and statistics, including methods of statistical inference and data analysis.\n-Mathematics students learn how to use mathematical software and computer programming languages to solve mathematical problems and simulate real-world scenarios.\n-They also study the history and philosophy of mathematics, exploring the development of mathematical ideas and their relationship to other fields of study.\n-Mathematics students learn how to communicate mathematical ideas and findings through technical writing and presentations.\n-Finally, an undergraduate degree in Mathematics prepares students for careers in various fields such as academia, industry, and government, as well as for further study in graduate and professional programs.443:T66c,The University of Kansas is a top-tier public research university with a commitment to a research-focused academic curriculum and career support. At KU, you can choose from over 190 fields of study, including top-ranked business, engineering, pharmacy, education, and architecture programs.\n-An undergraduate degree in Mathematics teaches students the principles of pure and applied mathematics, including calculus, algebra, and analysis.\n-Students learn about mathematical proof and how to construct rigorous and logical arguments.\n-They study the foundations of geometry, topology, and number theory.\n-Mathematics students develop problem-solving skills, critical thinking, and logical reasoning that are useful in a wide range of fields.\n-They learn about the applications of mathematics in fields such as physics, engineering, finance, and computer science.\n-Students study the principles of probability theory and"])</script><script>self.__next_f.push([1," statistics, including methods of statistical inference and data analysis.\n-Mathematics students learn how to use mathematical software and computer programming languages to solve mathematical problems and simulate real-world scenarios.\n-They also study the history and philosophy of mathematics, exploring the development of mathematical ideas and their relationship to other fields of study.\n-Mathematics students learn how to communicate mathematical ideas and findings through technical writing and presentations.\n-Finally, an undergraduate degree in Mathematics prepares students for careers in various fields such as academia, industry, and government, as well as for further study in graduate and professional programs.444:T4a4,I didn’t want theory — I could do theory all day. I wanted something I \r\ncould use immediately when I walk out the door, and that’s what Maryville’s \r\ncourses provided.” — Felecia W., Maryville Grad The digital world runs on \r\ndata. So can your career. We live in a digital world — one in which \r\nexamples of data science and analysis can be found everywhere. Consider \r\nyour product recommendations from Amazon. Think about how companies in \r\ndifferent industries like Boeing, Walmart, and Disney use data to drive \r\ncritical business decisions, and insurance companies depend on this \r\nanalysis to forecast risk or banks to evaluate loan applications. Now \r\nimagine the career potential if you had the skills to help them do it. Earn \r\nyour bachelor’s in data science online from Maryville University, and \r\nyou’ll do more than study the tools and techniques used to dig deeper into \r\ndata. Graduates can build the skills to explore, analyze, monitor, manage, \r\nand visualize large data sets using the latest technology. Our innovative \r\nprogram also features a business minor, which can help prepare you for top \r\ndata jobs in nearly any field. Get Curriculum Details445:T7ac,Cleveland State University partners with world-class hospitals, Fortune 500 companies, government research centers, "])</script><script>self.__next_f.push([1,"and cultural institutions to give its students academic, research, and job opportunities. With more than 175+ undergraduate degrees to choose from, Cleveland State Global students are prepared to succeed in the global workforce.\n-An undergraduate degree in Information Systems teaches students how to design, develop, and manage information systems that support business processes and decision-making.\n-Students learn about the principles of project management, including planning, scheduling, and budgeting.\n-They study the analysis and design of information systems, using techniques such as data modeling and process mapping.\n-Information Systems students learn about databases, including design, implementation, and management of database systems.\n-They study enterprise resource planning (ERP) systems, which integrate business processes and functions across different departments and areas of an organization.\n-Students learn about the principles of data analytics, including data mining, machine learning, and statistical analysis, and their applications in business decision-making.\n-They also learn about the legal and ethical issues surrounding the use of information systems in organizations, including privacy, security, and intellectual property.\n-An undergraduate degree in Information Systems teaches students how to communicate technical information to non-technical stakeholders, including writing technical reports and giving presentations.\n-Students learn about emerging technologies and their potential impact on organizations and society, preparing them to be innovative and responsible technology professionals.\n-Finally, Information Systems students develop skills in teamwork and collaboration, as well as leadership and management, to prepare them for careers in various fields such as business, healthcare, and government.446:T80d,"])</script><script>self.__next_f.push([1,"The University of Utah is located on a picturesque campus in Salt Lake City. Utah creates global leaders by placing a strong emphasis on entrepreneurship, innovation, and quality of life. The Utah College of Engineering - ranked #61 in Undergraduate Engineering by U.S. News \u0026 World Report (2020) - prepares students to improve the productivity, health, safety, and enjoyment of human life through leading-edge research and tech development.\n-An undergraduate degree in Information Systems teaches students how to design, develop, and manage information systems that support business processes and decision-making.\n-Students learn about the principles of project management, including planning, scheduling, and budgeting.\n-They study the analysis and design of information systems, using techniques such as data modeling and process mapping.\n-Information Systems students learn about databases, including design, implementation, and management of database systems.\n-They study enterprise resource planning (ERP) systems, which integrate business processes and functions across different departments and areas of an organization.\n-Students learn about the principles of data analytics, including data mining, machine learning, and statistical analysis, and their applications in business decision-making.\n-They also learn about the legal and ethical issues surrounding the use of information systems in organizations, including privacy, security, and intellectual property.\n-An undergraduate degree in Information Systems teaches students how to communicate technical information to non-technical stakeholders, including writing technical reports and giving presentations.\n-Students learn about emerging technologies and their potential impact on organizations and society, preparing them to be innovative and responsible technology professionals.\n-Finally, Information Systems students develop skills in teamwork and collaboration, as well as leadership and management, to prepare them for careers in various fields such as business, healthcare, and government."])</script><script>self.__next_f.push([1,"447:T740,- #147 National Universities - U.S. News \u0026 World Report, 2019\n- A true campus experience just minutes from the cultural and financial capital of the world\n- 240+ major companies recruit on campus Apple, IBM, AIG, Pepsi and Merrill Lynch\n-An undergraduate degree in Information Systems teaches students how to design, develop, and manage information systems that support business processes and decision-making.\n-Students learn about the principles of project management, including planning, scheduling, and budgeting.\n-They study the analysis and design of information systems, using techniques such as data modeling and process mapping.\n-Information Systems students learn about databases, including design, implementation, and management of database systems.\n-They study enterprise resource planning (ERP) systems, which integrate business processes and functions across different departments and areas of an organization.\n-Students learn about the principles of data analytics, including data mining, machine learning, and statistical analysis, and their applications in business decision-making.\n-They also learn about the legal and ethical issues surrounding the use of information systems in organizations, including privacy, security, and intellectual property.\n-An undergraduate degree in Information Systems teaches students how to communicate technical information to non-technical stakeholders, including writing technical reports and giving presentations.\n-Students learn about emerging technologies and their potential impact on organizations and society, preparing them to be innovative and responsible technology professionals.\n-Finally, Information Systems students develop skills in teamwork and collaboration, as well as leadership and management, to prepare them for careers in various fields such as business, healthcare, and 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