The course could have been more comprehensive and less verbose. It had so much content in a tiny course. Content should be less and more comprehensive.

A bit more depth in explaining conjugacy in priors and posteriors will be very helpful. A possible way would be to have more example illustrations.

Week 3 was too much information too soon, but week 4 was great again like the other courses in this specialisation. Learned so much, thanks!

Learnt a lot. Though the subject material was hard to grasp first hand, it is good that instructor was readily available to help us through.

An interesting and challenging course, would be better with more real examples and explanation as some of the material felt rushed

It's a good one, but not as previous courses. Week 3 isn't well explained as other weeks. Hope it can be further improved

The material is good. However, there is no support from the instructors to answer our questions in the discussion forum.

This course through the material too fast. The content should have been spread out over two courses in my opinion.

This is a very interesting topic. Lectures in weeks 3 and 4 could use some work.

Bayesian stats is a broad topic. Learners would benefit from more material.

this Course very informative and bears an applied approach for learning.

Theorethical backdrop is a bit excessive on an R focused course

The material was interesting, yet required more time.

This course is challenging and well-presented!

Good content but sometimes it gets confusing.

Harder than former courses but great!

The classes are good.

It was nice

I don't have much new to add here - like many others, I found the course to be a sharp departure in teaching style and workload from the previous 3 courses, and found unanswered threads on the forums from one and two years ago. Students have been leaving feedback in this vein for years as well but it does not seem to have prompted any adjustments. The last two weeks of reading are especially intense and feel very crammed in, with the videos not explaining it with the care that the first few courses do.With all that said, it is not impossible to get through this course (clearly, as many of us have finished it), but you're left on your own for much of it. On the bright side the course project is not a huge jump up in difficulty as the readings may suggest, but is pretty much in line in terms of difficulty compared to the previous three projects.

The material is interesting. However some of the points are not presented in a way that I can understand.

The course is less coherent than the previous ones.

This course gave me an impression that the materials are not well organized. Basically, the course organizers present a lot of concepts and materials to you without background introductions. I know there are a lot to cover in 5 weeks. The organizers should think this through about how to present a lot of information in a short period of time. Maybe put the less important information in a lecture notes or something could be better.

This course was way harder than the three that came before. It feels as if courses 1 to 3 did not prepare me for this one at all. The lecturers throw in a lot of formulas that they just expect us to understand with ease. Whereas the first three courses explained everything in great detail, even the simplest things, this course assumes you immediately understand everything they throw at you. The quizzes also ask for small details mentioned during 2 seconds of one of the many videos. Still, the course is doable if you push through and apply what you learn in the Rstudio-assignments.

I wanted to rate this 3.5 but because I could not will lean towards 3 stars. The main reasons being that the course could have been organized in such as way as to introduce Bayesian statistics, rather than bombard the student with hefty math formulas. Instead, it could have reduced the material by half and focused on understanding WHY and WHEN Bayesian can be helpful for certain cases. An emphasis on providing more practical examples could have helped me more clearly understand the mechanisms of priors, model averaging, etc.

Overall I think there are better options available for learning bayesian statistics. The pacing and structure of the course both felt off to me, spending too much time on some things (conjugacy in particular) and breezing past many other things too quickly (particularly numerical methods). I also thought that it would have been more helpful to learn to perform many of the analyses from scratch so that they could be better understood, rather than relying so heavily on the accompanying statsR package.

The classes in this course are less illustrative and spend less time in each topic in comparison with the Frequentist courses of the Statistics specialization. From my perspective, this block could be a whole specialization on its own. I really enjoyed learning about Bayesian statistics and I am thinking of taking a more detailed course only on this. Especially because this curse didn't help me understand Bayesian Statistics as well as it helped me understand Frequentist Statistics.

It seems like this course contains good information, but there's a huge gap in the material as taught by some of the instructors. It seems like one of the instructors in particular assumes you're already familiar with material that's not covered in the rest of the course. These parts of the lectures rehearse math and code in a very formulaic way which conveys almost no intuition or understanding of the subject matter. However, the labs a pretty good.