So, I really wanted to LOVE this class, but instead I found that I merely liked it, and want to use this review as a way to explain why. WHAT I LIKE ABOUT THE CLASS: The material is sufficient for the topic at hand, and is structured in an appropriate way. If you work through everything you'll have a decent grasp of exactly what the class is meant to be about. It's also pretty well paced. WHAT I DIDN'T LIKE ABOUT THE CLASS: Dr. Lee usually rushes through or skips discussions what concepts mean before formalizing them mathematically. As a result it's very easy to make progress through the class without a good feeling that you actually "get" what Bayesian statistics is really about. Too many of these videos are him chopping wood through the mathematical jingo, when the material DESPERATELY needed a 3-5 minute introductory video about what concepts actually mean or how to think about them. I remember telling my girlfriend during the middle of the class that I found it frustrating because I was progressing through it quickly, and getting the quizzes right, but lacked a good intuition for how to think about Bayesian statistics. So Dr. Lee......work on those presentation skills! Think deeply about how to communicate the essentials of the concepts in each lesson, and THEN start pounding away on the whiteboard!

This course gives an introduction to the theoretical basics of Bayesian statistics. Before taking this class, I had a very confused view of the whole Frequentist vs Bayesian "debate". I understand now that Bayesian statistics is really about attaching uncertainties to beliefs and producing a clear definition of this uncertainty (especially through the notion of credible intervals).

The course really focusses on theory. I recommend knowing a bit of basic stats concepts before taking the class, such as Bayes' Theorem, basic discrete and continuous distributions, and confidence intervals. If you are not experienced with these, be aware that you will likely need to read-up on them throughout the course. R is used, but the usage is so simple that you should not shy away due to a lack of R experience.

I really have no complaints about the course. After completing it, you should understand the differences between Bayesian and Frequentist approaches. You will also understand a lot of terminology that gets thrown around in data science these days (priors, posteriors, credible intervals).

I felt like I just did a lot of calculations. The course was better in the beginning, as I felt the professor actually explained what and why were were doing what we were doing. By the middle of the course, however, I felt that the professor just jotted down equations and went really quickly. I don't actually understand why I was doing the calculations that I was doing.

I don't find that the lectures do a good job of relating the material to real world usage. To much focus on equations and too little on the why.

If you already know everything about the topic and just forgot some little things or you are very strong in calculus, this may be a nice refresher. Otherwise, not very useful. Really dense and little explanation. I liked the Youtube MIT course on Probability (it includes Bayesian Statistics) much more, since it has good explanation of the concepts.

The instructor doesn't do a good job at teaching. He throws so many formulas at you without explaining any of them. The course is purely based on memorization not understanding the concepts. I have been using other online classes to be able to understand this class.

Herbert Lee is teaching by seeing books and write lots of equations doesn't explain how theory and equations related to real world applications. Its more like class room lessons , not like something that can be applied to real world scenarios.

Almost no intuition is given. I really got bored while watching the formulas to be written on the board without giving real meaning behind them. I would not have taken this course I was aware of these.

Very good overview to the area. Efficient and clear lectures - emphasis on the quizzes that required just a proper amount of focus and time from my personal point of view.

Very concise and helpful for an intro to Bayesian statistics. Good level of difficulty to encourage learning. This well prepares further study of more advanced topics such as MCMC and more.

a really good course!

though sometimes the questions in quizes aren't clear enough,or not explaind else where,and sometime you could miss the big picture.

could also be good if you could add some python scripts,and maybe more reading material about the topics.

A well-rounded introductory course in Bayesian Statistics.

Great introduction for beginners

The bite-sized arrangement of individual videos are very conducive for learning and self internalisation.

Thanks it was nice learning from wonderfu instructors.

very helpful for Statistic analysis

Good and clear course. I was impressed by the quality of the material.

Great course for beginners as well as those that need a refresher on the basics!

Very Useful Course

Great introductory course. It was challenging but doable for someone who has not take college level mathematics or statistics in a few years.

I found the course very well made and beautifully presented. The material is systematic, the more advanced topics based on the previously learned information without gaps and any need to study additional sources. The examples and the tests provide additional insights. Thank you, prof. Herbert Lee, for this great course!

Was able to do the course with Python instead of R, though it got a bit complicated on the last topic (regression).

The course if very informative but some previous knowledge and understanding of basic statistics (e.g., distributions) is recommended to fully grasp the concepts delivered through the course.

Great introduction to Bayesian statistics. Very helpful for me, especially for understanding some of the times when priors might be useful, and how they can aid me.

best course for the beginners who want to get started on bayesian inference

A great introduction to Bayesian Statistics. For some of the Quiz questions, it would have been helpful to get an error warning in case one might had accidentally used a comma instead of a dot notation.