Excellent course! This covered a large amount of material, but it was well organized, with a good number of problems to solve. Matthew Heiner does an excellent job with the lectures and explains things well. Coming from the frequentist worldview, I found this course to be a definite challenge, but well worth the time.

The second course of the great series. The knowledge and skills gained in this course allow to actually do statistical analysis on scientific data. The course is very clear, systematic and well presented. Thank you!

Just finishing this class now......it is very good. Much better than the first one in this series. The videos and examples are better explained, and you leave with a solid understanding of Bayesian Analysis. When I signed up for this class I really wanted to know how I could use tools like MCMC to perform real analysis, and I feel like I got what I signed up for. Well done!

Really interesting course. The coding session are useful and can be use cases for lots of various situations.

Brilliant course! Very well organized and with useful study cases.Suggestion: It would be nice to have the same examples in Python using, e.g. Stan or PyMC.

One of the best designed courses. The material and videos are very precise and informative. The quiz questions and assignment are very enjoyable. Thank you !

Really good intermediate introduction to bayesian analysis. I really liked how hands-on the course is. The last project was very useful as one will likely to face challenges and try to solve them especially if you use a rich dataset.

The course provided a lot of very helpful tools. However, I believe it was a bit too fast paced. Furthermore, there were certain topics which were not explained clearly -- for example, the discussion of the Metropolis-Hastings Algorithm and Gibbs Sampling was extremely confusing.

really like the content.

the R material in this actually changes my view towards R, so thanks.

Informative but the pace is slow

Good course, but the peer review process for the Capstone project in Week 5 is broken. Based on submissions to the course Forum in which multiple students have submitted their work on time but not received a grade due to lack of peer reviewers, this has been going on .

This course is taught in a way that not useful for real world applications.

Not enough depth.

This course helped me to get some experience at building Bayesian models and how they are applied.

Muy recomendable para los investigadores y profesionales que quieren desarrollar productos y procesos nuevos.

Fairly good introduction to basic Bayesian statistical models and JAGS, the package to fit those models.

one of best stats courses I had

Amazing, super cool!

Great materials and well organized lecture structure. But in the meanwhile, it requires quite a lot preliminary knowledge.

A very good practical and theoretical course

This course is excellent! The material is very very interesting, the videos are of high quality and the quizzes and project really helps you getting it together. I really enjoyed it!!!

Very good course giving a good practical kickoff to a very interesting and exciting topic of Bayesian statistics.

This course was fantastic. It combined detailed learning materials with frequent and comprehensive assessments. While managing to cover everything from the basics of MCMC through to the use of a number of different bayesian models. My only issue with the course was that the learning materials encouraged copy-pasting code and often didn't properly explain the choice of priors and other details about the chosen models.

Excellent course, with deep explanation of difficult topics in Bayesian statistics and Marcov chain applications. Good quizzes and enough time to complete them. Recommend to all interested in probability theory.

This course fills an essential gap in learning Bayesian statistics, and provides concrete assistance in moving from theory to actual model writing in R and jags. Worth every penny, and then some more. However, the course requires a fairly high level of comfort with both general Bayesian statistics and the R language. I think it would benefit from a brief introductory lecture on jags syntax, as well as some additional worked problem examples.