Online courses directory (5)

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Starts : 2017-09-26
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edX Free Closed [?] English Data Analysis & Statistics Economics & Finance EdX MITx Social Sciences

This course is part of the MITx MicroMasters program in Data, Economics, and Development Policy (DEDP). To audit this course, click “Enroll Now” in the green button at the top of this page.

To enroll in the MicroMasters track or to learn more about this program and how it integrates with MIT’s new blended Master’s degree, go to MITx’s MicroMasters portal.

This statistics and data analysis course will introduce you to the essential notions of probability and statistics. We 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. We will illustrate these concepts with applications drawn from real world examples and frontier research. Finally, we will provide instruction for how to use the statistical package R and opportunities for students to perform self-directed empirical analyses.

This course is designed for anyone who wants to learn how to work with data and communicate data-driven findings effectively, but it is challenging. Students who are uncomfortable with basic calculus and algebra might struggle with the pace of the class.

Starts : 2017-09-26
No votes
edX Free Closed [?] English Data Analysis & Statistics Economics & Finance EdX MITx Social Sciences

This course is part of the MITx MicroMasters program in Data, Economics, and Development Policy (DEDP). To audit this course, click “Enroll Now” in the green button at the top of this page.

To enroll in the MicroMasters track or to learn more about this program and how it integrates with MIT’s new blended Master’s degree, go to MITx’s MicroMasters portal

A randomized evaluation, also known as a randomized controlled trial (RCT), field experiment or field trial, is a type of impact evaluation that uses random assignment to allocate resources, run programs, or apply policies as part of the study design.

This course will provide step-by-step training on how to design and conduct an RCT. You will learn about why and when to conduct RCTs and the key components of a well-designed RCT.

In addition, this course will provide insights on how to implement your RCT in the field, including questionnaire design, piloting, quality control, data collection and management. The course will also go over common practices to ensure research transparency.

No previous economics or statistics background is required. However, economic and statistics concepts and vocabulary will be used and some familiarity is advised.

Starts : 2017-09-28
No votes
edX Free Closed [?] English CaltechX Economics & Finance EdX

This 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.

I teach the same class at Caltech, as an advanced 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.

Starts : 2017-10-01
No votes
edX Free Closed [?] English Business & Management Data Analysis & Statistics EdX Engineering TUMx

Learn how to analyse 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.

You 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.

You will learn how to implement statistical process control using control charts and quality management tools, including the 8 Disciplines and Failure Modes and Effects Analysis to reduce risk and manage process deviations.

To complement the lectures, learners are provided with 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.

Upon successful completion of this program, learners will earn the Technical University of Munich Lean Six Sigma Yellow Belt Certification, confirming mastery of the fundamentals of Lean Six Sigma to a Yellow Belt level, based on the American Society of Quality's Body of Knowledge for the Certified Six Sigma Yellow Belt. 

Starts : 2017-09-28
No votes
edX Free Closed [?] English Data Analysis & Statistics EdX Math UC San DiegoX

The job of a data scientist is to glean knowledge from complex and noisy datasets.

Reasoning about uncertainty is inherent in the analysis of noisy data. Probability and Statistics provide the mathematical foundation for such reasoning.

In this course, part of the Data Science MicroMasters program, you will learn the foundations of probability and statistics. You will learn both the mathematical theory, and get a hands-on experience of applying this theory to actual data using Jupyter notebooks.

Concepts covered included: random variables, dependence, correlation, regression, PCA, entropy and MDL. 

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