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 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.

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.

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.

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.

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).

Good intro to Bayesian Statistics. Covers the basic concepts. Workload is reasonable and quizzes/exercises are helpful. Could include more exercises and additional backgroung/future reading materials.

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

There should be more focus on understanding the equations. The R and excel videos were incredibly blurry.

A good MATHEMATICAL introduction to Bayesian Statistics. I read some of the negative reviews and they claim to have many formulas, well, that was exactly what I was looking because after watching some PyCon Videos about Bayesian Statistics I understood the code to solve the problem but not really why that code works or how.

This course may be frustrating for those with no prior introduction to Bayesian statistics, I recommend to take this course after seeing some videos from the Scipy, PyData and PyCon conferences regarding this topic.

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.

This course introduces the key difference between the Frequentist approach and the Bayesian approach on both discrete and continuous data. The instructor is capable of connecting the dots between the intuition of the theories, the mathematical formulation, and the real-life application. One thing I would suggest is to provide external links to each unfamiliar terminologies mentioned in the videos.

This is a math course. It has good quality, it is rigorous and educational. It presents the mathematical framework of the Bayesian statistics. I like it when the math of the subject is explained well, as done in this course, rather than "I don't want to get in to the math", or "it is beyond the scope of this course", which you often see in online courses.

This is an excellent course for beginners attempting to understand the Bayesian framework—one of the best I've seen on Coursera. I would only suggest the professor use Cmd + C and Cmd + V for R lectures. It pains me to watch him driving the cursor to click to copy-paste.

This was a great course. My only critique is that it is a bit rushed during the last week. But the first three weeks are excellent. Please more of this and please combine the three Bayesian Statistics courses into a specialization.

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.

Overall, an informative and useful introduction to Bayesian statistics. I would have preferred if the examples in the lessons in which prior and posterior were introduced went beyond the "coin flipping" binomial examples. I've taken a number of stats classes, and while coin flipping is to stats as "hello world" is to computer programming, I feel that students would grasp the importance of Bayesian inference much better if more realistic examples were used. Related to this, while the theory is important and understanding how to integrate the likelihoods is useful for grasping key concepts, nobody in practice really does this. You're going to use a programming language like R or some kind of stats package. More focus on these would be good.

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.

Found the pace of the course to be a little uneven - sometimes jumps from basic introductions (good) to somewhat advanced concepts rather quickly. The sound quality was also a bit uneven, but improved with the later videos. Please wear dark clothing so that writing on the see-through board is readable - again, this improved with later videos.

Biggest suggestion for improvement is to provide downloadable lecture notes - having to take notes while the lecture is in process is distracting, and takes us back to the bad old days of "talk and chalk".

All of that said, the class was a very useful introduction, even though the application I have in mind requires discrete Bayes rather than continuous. I will be taking a look at the second course in this series.

I get lost a bit too often.

The teacher sometimes explains easy concepts and omits the difficult ones (e.g. exponential distribution is explained as "for example if you are waiting for a bus that comes every ten minutes" and then he tells you how to compute expected value and moves on, but he does not say WHAT IT MEANS - is it the probability that I will meet an oncoming bus? is it probability of waiting ten minutes for the bus? is it the average waiting time? is it average number of buses that come every hour? - but there is detailed explanation of what A squared means in lesson two (!))

The teacher often makes me confused as to where he got the numbers he is plugging in the formula or what answer the formula gives.

But I take it as a challenge and I intend to finish the course despite all of that. Sometimes it is fun to decipher the mystic equations. And maybe it is me, maybe I was not born to be a statistician. Maybe there are people that find this stuff easy and understand it right away.

I really like the quizes. They are HARD.

One last thing: Wearing white shirt and using white marker makes it impossible to read what he writes. But I take it is part of the challenge ;-)

This is was a really difficult course. I took a basic statistics course in college but was not prepared for the calculus and the theoretical way this course was explained. If you are looking for a stats course that explains through real-world examples rather than theory - this ain't it. The only reason I gave it 2 and not a 1 star is because I can assume that those with a deeper statistical background would probably not face the challenges I did.

When doing the quiz for Lesson 2, Week 1, I first failed, then I used the hints provided to do the appropriate calculations, however the numbers I obtain are considered incorrect, and I cankot pass the quiz although I checked the calculations million times and I know I am correct. There should be a demonstration of how the correct answer is obtained, simpke hints are not enough. Although I was super excited about learning Bayesian statistics, now I am forced to quit as it looks I will never be able to complete the course. The course ended up being useless and frustrating. This is truly unfortunate

The course is more of remembering rather than understanding. Many of the formulas and distributions are used without proper derivations. I was determined to complete the second course of this series, but now I don't think I would.