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).

Great course! I'm an average person on maths and I can say this course is challenging but not overwhelming; you'll be just fine. It may require some basic previous knowledge of R if you want to work the problems using it, but having all excercises done in Excel as wells makes it really easy. So even if you have no idea of R, you still can use Excel instead. I'll take the 2nd course of this series just becuase I liked this one a lot

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.

excellent course. I would suggest to add some more references/suggested readings and add one ' blackboard' lecture on the regression part (currently the course jumps into R/Excell with no theory given and the supplement material does not give enough infirmation to code your own functions). Nevertheless, the feedbacks provided at the exams are amazing and i managaed to get all the information I wanted from there.

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.

Short but sweet. This course is a good introduction to the subject. I particularly liked the instructor and the design of the tests, which are really complementary to the learning material and are really helpful to put in practice the somewhat abstract theory. The supplementary material is also well done. It would be nice to have a course book to follow though as referring to videos is not always easy.

This is a wonderful course in Statistics that I would highly recommend to everyone who wants to take a learning path into the world of Bayesian inference and refresh their knowledge of numerous statistics concepts involved. The lectures provide excellent in-detail explanations, and additional reading material fill in the gaps if some of the concepts or derivations weren't shown in the lecture in full.

Excellent course. For such broad discipline I'm sure it was difficult to choose most important material to fit 4-week course, yet professor did it perfectly. I'd love to see this course in Python, but I guess I can't have everything ;) I'd also love see some examples of using probabilistic programming packages, like Stan or PyMC3 in more real-life problems - I would give 6/5 stars for it!

This is my first course on Coursera and I am delighted by the construction, how it was led by the instructor and what I learned. Quizzez are great, I spent on some quite a bit of time, but I feel they really checked if I understand the concepts and calculations. The questions during the video are also an excellent idea to check if you follow. All in all I am very happy I took this course!

I really enjoyed this course, the videos are fairly short with focus on exercises and there is a nice narrative throughout the course. Sometimes I needed to watch videos again because explanations were too fast for me to follow in real time, but I definitely enjoyed presentation style of Prof. Herbert Lee. Will be following the course up with "Techniques and Models" to learn about MCMC.

Followed the course in order to fill a gap I had in statistics knowledge, as I'm very interested in machine learning - deep learning, and always came upon things as MLE without really knowing well what they were talking all about. Really a very good course to get an understanding! Well explained, though maybe you'll need to brush up your Algebra and Calculus a bit to be able to follow...

It's a fantastic course, which guides me to know what is Bayesian statistics. Before joining this course, I try my best to learn Bayesian Statistics but it's failed. However, I really grasped some key points and knowledge of Bayesian Statistics and I will join the following course about Bayesian Statistics to get more. Thanks for the professor. I am appreciated for it.

Great course that introduces the fundamentals of Bayesian Statistics. Useful for becoming familiar enough with the ideas to use in basic analysis provided you have some experience with frequentist statistical methods. For my studies, this course allowed me access to the Bayesian statistical material that is often encountered in phylogenetic analysis in bioinformatics.

I think this was a very helpful course, for me personally I learn better with "real" examples, so i think if there were more of those earlier on, that would have been more helpful. I also use Python, and would prefer to use Python, so it would be nice if there were instructions on that in addition to R/Excel. Spent a lot of time translating between R and Python.

I strongly recommend this course to those who are interested in learning theoretical concepts that build Machine Learning statistics especially Bayesian. The course content was well organized and the professor presented the concepts in a very engaging way. Relevant and appropriate examples and in-video quizzes were very helpful in understanding the theory.

A great course to learn not only Bayesian Statistics, but covers statistics in general to a great degree. The best part is the exercise, which are almost perfect to learn the course material. After doing tens of MOOCs everywhere, I find this course unique in terms of pushing students to apply the concepts. I loved this course and enjoyed learning with it.

This is one of the best online courses I took. This is coming from an ex-Professor who taught 13 years. The material basic and is brief, but to the point and very well organized and presented. Having some background in statistics helps as some important details are skimped. In a such a short time, I learned well the concept of about Bayesian statistics.

Excellent course. Professor Lee did a first-rate job of giving the intuition for Bayesian methods and building the foundations for further study of the topic. The course is short and to the point, but that is a feature and not a bug. It will prepare you to take further courses in Bayesian statistics or to study the topic on your own. Highly recommended.

I really enjoyed this course!

Lectures were clear and given at a good pace. Thank you for the effort at putting in comments for all the questions on the quizzes. This really cemented my understanding and this has been the first time I have really gotten through Bayes theorem, which has been my downfall in previous statistics classes.

Good job Dr Lee!

Out of 15 online courses I have taken over the last 3 years, this is the best. Professor Lee presents rather difficult material in a clear, detailed, style. The lesson quizzes are remarkably useful; it seems real care has been taken in aligning the questions with the key points in the lectures, and in furthering one's understanding of the same.

I found this course to be really useful. It did progress through the math a bit quickly for my liking, but it was paced very appropriately and the discussion forums were helpful. Excellent examples are contained and I loved how both R and Excel modules were leveraged. Looking forward to seeing more Bayesian courses on Coursera in the future.

Great course for intro to Bayesian. Before deciding to learn bayesian, I expect to choose a course which could explain concept in a simple way but, at the same time, having enough practice. This course matches my need. After taking this course, I would recommend it to anyone who want to learn some bayesian for further machine learning studies.

Really loved this course. I am relatively new to Statistics but very familiar with the rest of the mathematical tools used in this class (Integration, sets, etc). After finishing the class, I was immediately able to apply Bayesian Inference to my job. Things were explained well, and made sense after re-watching once or twice. Excellent course!