Sep 21, 2017
Great course. Difficult to apprehend sometimes as the Frequentist paradigm is learned first but once you get it, it is really amazing to see the believe update in action with data.
Apr 10, 2018
I like this course a lot. Explanations are clear and much of the (unnecessarily heavyweight) maths is glossed over. I particularly liked the sections on Bayesian model selection.
автор: Stanley R C•
Jan 30, 2018
The instructors have great expertise, but this course is pretty difficult for a Bayesian newbie. Additional study guides would be helpful (especially week 4).
автор: Lalu P L•
Jun 02, 2019
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.
автор: Malolan S•
Sep 10, 2019
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.
автор: Ángela D C•
Jun 12, 2018
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!
автор: KALYESUBULA M•
Jun 03, 2017
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.
автор: Adam A•
Aug 25, 2017
An interesting and challenging course, would be better with more real examples and explanation as some of the material felt rushed
автор: Marwa A E K M A Z•
Jan 07, 2020
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
автор: Hanyu Z•
Dec 08, 2016
The material is good. However, there is no support from the instructors to answer our questions in the discussion forum.
автор: Niels R•
Jul 06, 2019
This course through the material too fast. The content should have been spread out over two courses in my opinion.
автор: Emmanouil K•
Aug 16, 2017
This is a very interesting topic. Lectures in weeks 3 and 4 could use some work.
автор: Vicken A•
Dec 29, 2016
Bayesian stats is a broad topic. Learners would benefit from more material.
автор: Raja F Z•
May 23, 2020
this Course very informative and bears an applied approach for learning.
автор: Jaime R•
Nov 08, 2018
Theorethical backdrop is a bit excessive on an R focused course
автор: Liew H P•
Jan 17, 2019
This course is challenging and well-presented!
автор: José M C•
Mar 22, 2017
Good content but sometimes it gets confusing.
Nov 15, 2017
Harder than former courses but great!
автор: George G R•
May 06, 2017
The classes are good.
автор: sohini m•
Oct 27, 2017
It was nice
автор: Haixu L•
Jan 19, 2018
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.
автор: Sander t C•
Jun 22, 2020
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.
автор: Jeff M•
May 09, 2019
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.
May 31, 2019
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.
автор: Bo L•
Dec 08, 2017
This course is different from the first 3 courses in this specialization. I only recommend this course to people who have sound knowledge in calculus and some background knowledge in Bayesian Statistics. Personally, the pace of the videos is fast and the instructors use very technical terms. Although the course is not intended to give in-depth explanation into Baysian statistics, how the content is set up tend to be confusing.
автор: Thomas J H•
Aug 07, 2017
This course has a much steeper learning curve than the first three, and goes from theory to examples in action rather than vice versa. I think the Professors involved are super-smart and more than just qualified, but the teaching method is a noted departure from the first three courses in this series. Think this would work better as two courses. Slow things down a bit, and give more R exercises and examples.
автор: Andreas Z•
Mar 27, 2018
This introduction to Bayesian statistics familiarises you with the fundamental concepts. The difficulty is that the material covered is non-trivial and probably cannot be squeezed into the time allocated. Is is very difficult to follow the lectures and not getting lost. Thus, you need to take lot of time and maybe complement this course additional ones in order to understand the material and profit from it.