The course is in general well structured. The professor used a lot of mathematical equations to explain the contents. I have no problem understanding them. Everything goes smoothly, until the last section: Bayesian Linear Regression (BLE). In the last section, the professor skipped all the mathematics aspects and rushed the content with R / Excel examples. This is not what I expected. Overall, I will rate the course 4 stars.

This course is definitely not for someone new to statistics. Some of reasoning steps were missed in the lecture. I guess the reason is that the professor assumed the students already had such knowledge. I took statistics courses 15 years ago and forgot a lot of knowledge about it, so the course is quite difficult for me though I passed all the quizzes. I am taking it for the second time and it makes more sense to me now.

This course has an extremely useful start. But when we reach the second half of the course, it becomes quite hard to follow. What's missing in the second half of the course is a good example for each topic. These examples are provided in Quizzes which are extremely useful but still, it's the instructor's job to explain them adequetely in the course first. Overall, I liked the course and would recommend it.

The lectures were good but rattled-along at quite a speed, even with pausing and "rewinding" I still found it difficult to follow, esp towards the end. I think a short explanation at the start of the video explaining what was going to be covered, what its role was and where it fitted into the big picture might have helped (background is UK maths A-level 45yrs ago and a career on the fringes of science)

I feel like I have a much better understanding of Bayesian statistics after taking this course. I learned a lot, even though it didn't take very long to get through all of the class material. My only criticism is that the 4th week seems pretty scattered. It covers a lot of different topics in not a lot of detail. Ideally, this material should be broken up into 2 weeks and covered in greater depth.

Very good course, I may have been at a bit of a disadvantage because I came from a behavioural sciences background rather than a full statistics or math background. It was interesting though, and I think I acquired the requisite skills to conduct a Bayesian analysis in future. However, at some points in the class it does become very formula heavy, which I did find tough to grasp at some points.

The course helps in developing a quite sound grasp of the Bayesian approach to the world. The assignments are feasible and help in gaining a deeper understanding of each subject. However there is a caveat: You definitely need to review your math skills before starting this course (esp. calculus, arithmetics and combinatorics) otherwise you'll be struggling with the particularities !

Excellent introduction to Bayesian statistics. More proofs would have been nice (perhaps an optional advanced material section?). The later half of the course increases quite a bit in difficulty and could use 1-2 more examples + applications. Professor did a great job and the quizzes thoroughly tested my knowledge. Overall, I would definitely recommend this course.

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".

Very good course, with less emphasis in the videos and more on exercises and critical thinking, the way I like and learn the best. I particularly liked that the lecturer writes on a transparent vertical surface standing between him and the camera, very convenient. For those not so familiar with mathematics, this might hurt a bit but the payoff is super positive.

Really enjoyed the course! Thank you. I would have given a higher rating if: 1) the instructor had spend more time on the intuition underpinning different derivations, 2) provided more context, 3) discussed more examples from practice. However, I am definitely continuing on to "Bayesian Statistics: Techniques and Models"! Thank you once more, team UCSC!

I enjoyed the course. I was looking for a way to improve my knowledge of statistics and bayesian maths. I mainly used excel for the calculations. I would appreciate an additional tutorial on plotting mass and PDF function etc against Theta and real world variables to explore impact of parameters on distribution shape on prior and posterior results.

Very good course, but you will probably need to supplement it with a beginners Bayesian statistics book. The pace is fast, with quite a bit of solving of formulas without much time spent on the underlying theory. I had to step away from the course for a month or so and read up on the basics before much of the content in this course made sense to me.

Exceptionally interesting class. Professor was knowledgeable and engaging. The key insight was to approach the "quizzes" as homework, a learning process. Some of the lectures were of variable audiovisual quality and the pacing of some sections was not uniform, but overall, a triumph. More from this professor please! Fun.

Good refresher course. Like a number of Coursera courses, it moves from basics through to more advanced topics quite quickly at times and necessarily skips over some of the more tedious but important distributional derivations. Would like to have seen more practical examples of Bayesian regression and its applications

Herbert Lee is great at explaining the mathematics behind Bayesian statistics. However, I think the course can improve greatly by also focusing more on context and the intuition behind the mathematics. I often found that I was able to pass all quizzes, while I did not 100% understand why I was doing what I was doing.

Great discussion into the ideas. The quizzes are relevant to the lectures as well and pretty straightforward, you don't need to go outside of the lecture itself to be able to do the quizzes. the only thing would be it would be good if the lectures notes were in print as Prof. Lee's writing is sometimes hard to read.

As a grad student myself, I liked the way this course was presented in short video format and in only 4 weeks. Definitely there are much more to learn about Bayesian Statistics and one can go way deeper, but this course gives the required basic Bayesian knowledge to someone who wants to get familiar in a short time.

As a person who had a rudimentary knowledge of Bayesian statistics, I found this course very helpful and I think I will come back to review different lectures on this course. But being short and concise makes it harder for individuals with no prior education to understand all materials completely.

An excellent course which focused on important concepts. I dont know if I could have done it without some background in probability. I would have liked more help with last honors quiz, which was frustrating. I wonder if coursera would include tutors that could be paid by learners to help?

The course itself is well structured and covers a lot of material.

There are points in the course where the order of reading material and videos needs to be switched. Also, it would help to update some videos with a little more explanation. It appears as if the lecturer is skipping steps.

I liked it as introduction to Baysian statistics. With the material provided it was quite easily possible to answer the questions. I would have preferred that the videos of the course contained all the material and that it would not have been required to have read through material.

The course was well explained and there were several exercises pushing the learner to understand the logic behind the mathematical concept. I think it is a suitable class for people with already a certain level of statistics knowledge, even though all concepts are well explained.

I think that, besides lesson 11 and 12, everything was very well explained. I was a bit confused with lessons 11 and 12 since I am not new to econometrics. Perhaps I found it confusing the theory background related to the lessons themselves. Just my opinion, very good course.

The course was very well designed, I got to learn about a lot of new things in statistics that I had to understand. But for a Data Analyst working on large data sets and primarily working on ML this course is far too basic. Also, some of the concepts can be explained better.