This course is far different from others in the series. Mathematical formulas and other concepts are introduced without any prior background. Even if the concept is understood the application part of it still remains a mystery on where to apply it, the course could have been more elaborate explaining these concepts in-depth rather than introducing without any prior background. Words such as prior families are used without introducing them properly.

Disappointing drop in quality compared to previous courses in the specialisation. Lectures are just a verbatim copy of the accompanying book, with no additional context, and course assignments/quizzes expect you to know material not covered in the course (e.g. while working on a quiz, I would go back to the textbook, CTRL+F on key terms from the quiz questions, only for them not to be anywhere in the course material).

This course was by far the hardest one of the series and I felt lost numerous times. The video lectures are brief and in my opinion bring more questions than answers. I am not sure about other students but I feel that this course needed 1- much more R-exercises. 2- many more examples per lecture for example, it could be better explored the lessons learned with multiple question quizzes.

I think that some of the lectures in this unit are not constructed as well and clear as in previous units. This makes it harder to learn. I needed way more time than it is specified in the course to process and understand the course material. However, in the previous units I did not experience such issues

This course clearly has come ambition to cover important topics on bayesian statistics, however, probably due to time limit, the lecturers have to skim through the contents without further, sometimes necessary explanations. As a result, the lectures are difficult to follow.

The professors know what they are doing but not good at making the concepts plain to the students who don't have the strong background. Most of the times I would just ask myself why they did this and that but later they don't provide enough explanations.

The instructors are not interactive at all, they are reading directly, it's very boring specially for first week, the instructor overlook most important issues and doesn't highlight them, however the reading material is useful.

Way too hard compared to the others courses, and very unclear. Plus since not a lot of people finish the course, you have to wait a long time to see your peer review exam approved.

Although the course is high quality, unless the other units, this one is way too difficult. The fact that it wasn't Mine who performed the whole course impacts significantly

I would've saved a lot of time by knowing the R commands used in this course. It took so long to figure out things and I I didn't like the course because of that.

Mismatch between assessment and course content. After not being able to pass the assessment, I've fallen behind on the course and I'm too busy to catch up.

After Week 2, there are large gaps between previous material and the futher teaching material which makes confusing and a bit hard to comprehend.

Good introduction to Bayesian concepts, but the course would benefit of some rethought of design of exercises.

The materials and response from the organization are unavailable for a while and never get an answer

good stuff but extremely hard to follow, not engaging at all. lecturer reads off the slides.

A bit complicated compared to the other courses as part of the specialization

Poor lectures. Please look at the feedbacks on this given in the forums

Too Fast. Video is too short and spend a lot of time in the summary.

Too difficult to catch up each week.

Very theoretical and unstructured

About two years ago I completed Dr. Mine's course "Data Analysis and Statistical Inference" and was quite impressed by it. I always hoped that there'd be a follow up on bayesian statistics, so I was really excited when I heard that a course on this topic had finally been created. However while attending the course I became more and more disappointed. Dr. Mine does a nice job explaining things, other teachers in this course aren't as talented. Most slides / videos are quite useless for teaching because they skip over important steps without giving appropriate explanations. Also I was quite disappointed that this course pretty much only focuses on conjugate priors. MCMC is only skimmed over and the introduction to MCMC is more than questionable - instead of showing a simple example, MCMC is squeezed into the topic of bayesian model selection. Another point is R - this course doesn't really teach bayesian stats with R. It teaches how to call one-liners like bayes_inference (from package statsr) or bas.lm (from package BAS) instead of lm. This is totally disappointing. I wish this course would skim over conjugate prior methods and then focus on MCMC sampling methods by teaching how to build interesting and practically useful models using JAGS/STAN/PyMC/whatever. For anyone interested in bayesian stats I'd recommend reading "Doing Bayesian Data Analysis - Using R, JAGS, and STAN" and "Probabilistic Programming and Bayesian Methods for Hackers". These books are actually cheaper than this course.

The first three courses in this Duke series were superbly well done. I have taken numerous courses from Harvard and Johns Hopkins, and none of them compare in quality of execution of the first three Duke courses in this series.

And then there was Bayesian Statistics: much of the "instruction" in this course was truly awful. The quality of the slides and video and so on was still excellent, but the "teaching" was horrible. Vast amounts of totally unexplained jargon and very extensive equations were thrown at the students with the apparent assumption that the course was a review for postdoctoral statistics students. When material is beyond the scope of what perspective students can reasonably be expected to understand, faculty members should be honest enough to just say so rather than pretending to teach the subject matter.

I appreciate very much what the Duke faculty achieved in the first three courses, but the treatment of Bayesian statistics that I have just suffered through was shameful.

The first three classes in this certification were excellent; this course was anything but that. There seems to be a significant disconnect between the first three courses (probability, inference, linear regression) and the fourth course (bayesian). I do not have a strong statistics background but I felt the first three classes in the certification challenged me, while providing an adequate level of support and thorough / articulate examples; the pace was perfect. Yet, with the fourth course I believe that either: 1) there needs to be a bridge course that prepares you for the bayesian course, or 2) the material needs to be taught at a slower pace with more specific and well presented examples / frameworks to work from. Although I was able to complete the course, I will now have to find an alternative source to learn from in order to really understand bayesian stats.

The worst course in the series.

It progresses at a hurricane speed, thus as usefull as the Maria. I have barely made and it was not a pleasant experience. In fact I drowned at the week 4. The only reason I did not drop the course is because I've already paid for the previous courses of the specialization and I need to complete specialization for the certificate.

I think only people who had bayesian stats before and take this course as a refresher might find it pleasant. Or people with very good knowledge of probability theory. For others it is just a waste of time, because you will not learn to sail during a hurricane.

I have checked the syllabus of the other course on Bayesian Stats offered on coursera and it covers the same material in 8 weeks(2 courses), so that course would probably be a better choice if you are considering taking this course individually.