For me Chinese, it 's not easy to understand the quiz.

Clear and consise introduction to Bayesian statistics

Good and simple introduction for Bayesian statistics.

well instructed basic course of Bayesian statistics.

More examples are required as well as more practice

Loved the theory & analytical part of the course.

It is a nice introductory course on Baysian s

深入浅出，结合理论推导、实际应用与直观理解，挺好的一门课，让我对贝叶斯推断有了极大的兴趣

Pretty good overview of Bayesian statistics.

from week 3, the learning curve become steep

The normal distribution part lacks detail.

Good overview of Bayesian statistics.

Nice and easy introduction course.

Liked it and can recommend it.

A good course - recommended.

Need more material on R.

Very good introduction!

I am working in the field of epidemiological, medical research. Overall I would recommend taking this course. It needs to be pointed out, however, that if you are outside of the field of mathematics this specific course entails a lot of research (using google etc) that needs to be undertaken to understand the course material. Maybe in the future the course directors can compile a summary of all important formulae etc so that professionals from sectors other than mathematics can follow more easily and can focus much on this particular course on Bayesian statistics and not so much on conducting additional research to understand the basic course material. Furthermore, alongside a summary formula sheet it would be good to have all explanations included, what the parameters (alpha, beta etc) stand for with regards to the specific context. Thank you very much for this course!

This course provides a good overview to Bayesian statistics, but a larger dose of explanations of would be very useful. Mr Lee discusses, in the beginning, the differences between frequentist and bayesian paradigm. I feel that this would be beneficial in the other parts of the course, too. I feel that many of the lectures simply go too fast. After lectures full of Math, it would be useful to present lectures analyzing what had just been taught, in order to better grasp the content. And in general, this happens through the whole course - most lectures are basically math, without much time for grasping the intuition and underlying logic. For example: in the final part, under linear regression, it might be be difficult to grasp what a bayesian predictive interval means. All in all, I recommend this MOOC, but you might find hard to fully grasp it.

Plus Points of the course -

It starts with a context of where and why bayesian statistics comes into play. Good real world examples and questions are posed to drive home this point at the start of the course.

Where it could have been more helpful -

1) Somewhere in between the course gets lost in math expressions and distributions drifting away from real world implications. This would be ok for someone looking for pure math/stats. However it would become less relevant for someone coming from data science/business side. More real world use cases could have been there. (2) Better guidance on which other streams of data science/business can have application of this knowledge would be helpful (3) More comprehensive set of resources (pdf ones) would be great

The topic of the course is very interesting and the subject warrants it. Yet, especially the coverage in the last week of the course appears to be shallow and too many concepts are pushed down as valid or true without a lot of theoretical justification. Besides, some of the interesting conclusions are part of the quizzes rather than an integral part of the lectures. I also think that a course like this should allow the students to receive more written material in the form of PDF files that would cover all the matters being explored. What is made available is fragmented and does not cover all the topics in an organic fashion. I believe the course could be improved substantially.

I don't have background in math and statistics, in the first week of the lecture i can catch up with the lesson, but coming into week 3 and 4 it's really hard to me to understand what's happening, since the lecture / videos only talking about the formulas and only taught us how to use the formula. Actually for person like me who want to know Bayesian Statistics application in the real world and also fundamentals of it it's quite not recommended to took this lecture, honestly. However in the general understanding this lecture quite can help me how Bayesian thinking works what is the connection between likelihood, prior, how to choose prior, etc.

Liked the course, but it was a little easy (took four days total to do the material for the whole course). Many questions were left unanswered (such as how dependent the credibility intervals are on the choice of prior distribution and the assumed distribution of the data), and it didn't touch on later topics that are interesting (MCMC sampling). Again, good beginning course, but I was looking for more in depth study.

-1 star: Some discussions and derivations do not have adequate context and background. I expected more thorough explanation on concepts and more advanced topics. There are also a few minor typos that confused me. It is only a helpful introductory level course on Bayesian without depth.

-1 star: quizzes are not challenging enough and they only require plugging in numbers into equation. Not a good way to study

Nice introduction to Bayesian concepts. Presentation sometimes focused on the details of the calculations and could gain from more perspective. Sections relating to Normal variables - variance unknown and Linear Regression could be more explicit. Useful overall as an introduction, but require to get additional external material to get to the bottom of it.