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!

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.

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).

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.

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.

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.

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.

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.

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.

I was baffled after the first lesson. There is no explanation or answers given.

I had a great experience. It was lot more in-depth than I originally anticipated. In the tech world, Machine Learning is a buzz word and Bayesian based algorithms / models are the key and this introduces one to the fundamentals of Bayesian statistics. I was totally hooked on to this and the quizzes with real world examples really helped understand and apply the concepts. This course definitely requires maths background to be able to complete. Course provides lot of helpful materials and a pace that can be adopted based on your time and ability. Really looking forward for another deep dive in the near future.

This was a fantastic introduction to Bayesian statistics. Professor Lee is an excellent lecturer, with a comfortable, almost conversational style that I found easy to follow and stay focused on. The course itself is very well organized, introducing key concepts and then immediately providing examples that helped me internalize the concepts they pertained to. Quizzes were low pressure, straightforward applications of the lectures that served the purpose of allowing me to immediately apply what I had just learned.

Great intro to Bayesian Statistics. The math gets complex but the professor illustrates with examples to help with understanding. The exercises are generally similar to the examples in the lectures and honestly not as hard as they could've been. The course is only 4 weeks and moves pretty fast. Although I scored well, I may take the course again to help make sure all the details and concepts fully sank in.

I'm hungry for a deeper dive into the topic. I hope there is a follow up course in the future.

Extremely useful course. The way concepts are taught is amazing. However, if you are like me, you will have problems following the lectures at the speed at which the professor proceeds. It's a minor 'subjective' issue. The second issue is that sometimes, the equations in the quizzes may appear in the form of "cryptic codes", for the lack of better words, and you'll know it if you face it. A change of browser solves the problem, for me a shift from Chrome to Safari did the trick! Hope this helps.

A good introduction to the concepts conveyed by revealing the equations and expressions on a whiteboard. Minimal work with data and programming - much less of this than other Coursera classes on the same topics. Also unlike other Coursera classes on the same topic, the quiz answers/hints are useful and contain the relevant equations or R commands - not merely "correct" or "you should not have chosen this answer." I found this very helpful for self learning and confirming solution approach.

Good introduction to the Bayesian approach to inference.

As an introduction, it doesn't go very deep on some interesting arguments and it leaves out Hierarchical Modeling and estimations through Monte Carlo Markov Chain, but it would have been unfeasible in such a short time.

Finally, I would like to point out that mathematical strictness doesn't mean that the course is too technical: you have just to go through some calculations and review some concepts in order to fully understand them.

Professor Herbert Lee is world-class. The masterful and thoroughly outstanding presentation, organization and content of this activity are among the best of the best in any subject at any institution, whether on campus or otherwise -- more remarkably so for any senior undergraduate to graduate level mathematics activity, and most especially so in the broad field of Bayesian analysis. In summary: Extremely well-done and hats off to Professor Lee. I am thoroughly impressed.

As a long time frequentist, I occasionally run into problems that are very awkward to fit into the frequentist paradigm. I was aware at a high level that the Bayesian approach could be applied more naturally. Unfortunately, I was unable to "get it" simply be reading a book on the subject. This course made it very approachable. Professor Lee showed us the difficult math (tough integrals) behind it and how we can apply the results of that math in Excel or R

This course has been highly useful to understand how hypothesis testing works, starting from experimental design using prior distributions and assumptions to posterior statistics based on data. In my college courses it was always assumed that the parameters for the distribution were fixed, so, having a way to correct them through the information hidden in the data allows to overcome those assumptions and have a clearer perspective of the data behavior.

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).

This course strikes a perfect balance between not being too simple or too slow on one hand, and offering an easily accessible introduction to many central topic of Bayesian statistics on the other.

I think that good knowledge of basic probability theory and one-variable calculus is necessary for getting the maximum out of this course. This, however, is strictly due to the probabilistic underpinnings of the Bayesian theory.

I really enjoyed working through this course. It is a great introduction to Bayesian statistics. People with a little probability and statistics background can easily follow this course. I personally prefer to have more assignments for this course to better learn the concepts. Professor Lee is a great instructor, and he speaks slowly. The length of each video is short, and I like it a lot because you can finish it quickly.

A very well-organized course. Not a hard one, but one with sufficient quizzes to make sure you understand every concept by solving problems.

Another thing I like about this course, is that I had to actively write a lot of codes in Python and Matlab when doing the exercises(due to my familiarity with these two), although the course teaches a little bit R and Excel programming. This is a very effective way of teaching.

great course for those who have an understanding of the frequentist approach and would like to dip their toes in the bayesian approach. pace is right and the content is interesting throughout. Given the basic math requirements, many derivations are omitted (especially towards the end of the course, which might feel a bit rushed) but I feel the course gives the tools to explore should one want to fill the gaps in.