About this course: This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach. In particular, the Bayesian approach allows for better accounting of uncertainty, results that have more intuitive and interpretable meaning, and more explicit statements of assumptions. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. For computing, you have the choice of using Microsoft Excel or the open-source, freely available statistical package R, with equivalent content for both options. The lectures provide some of the basic mathematical development as well as explanations of philosophy and interpretation. Completion of this course will give you an understanding of the concepts of the Bayesian approach, understanding the key differences between Bayesian and Frequentist approaches, and the ability to do basic data analyses.
Who is this class for: This course is for people interested in learning an alternative to the Frequentist approach that is typically taught in statistics classes. The course covers both concepts and basic computing, and so it is applicable both to people doing data analysis as well as people who read the analysis of others, such as decision makers. This course expects that learners have previous exposure to statistics at the introductory level or higher, and previous exposure to calculus. In both cases, the expectation is that concepts have been seen previously, possibly many years earlier, but that the details may have been forgotten.
Taught by: Herbert Lee, Professor
Applied Mathematics and Statistics
| Level | Intermediate |
| Commitment | Four weeks of study, two-five hours/week depending on your familiarity with mathematical statistics. |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
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Great lectures. The explanation of each topic is extremely clear and avoids excessive mathematical burden. Lectures are short and concise. Quizzes or at least Module Honors could be a bit more challenging, though. It's a great course, anyway. I'll be looking forward to enroll in the next course of the sequence, entitled "Bayesian Statistics: Techniques and Models".
Exceptional course on probabilities and statistics from a Bayesian point of view. I would recommend this course to anyone wishing to learn more about probabilities and statistics.
This is a good course. The instructor offers additional material that help with the understanding of the material, along with enough quizzes to help with practical use.
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Great for understanding the fundamentals of Bayesian analysis!