Liked it and can recommend it.

A good course - recommended.

Need more material on R.

Excellent fundamentals.

Very good introduction!

Good, but concise

Very useful!

good

In the course, I liked that there were questions asked during the videos. That makes you think about the content, the professor was just talking about.

Anyway from my point of view, the supplementary material should have covered more of the content of the course. That would have helped me a lot.

Also, I sometimes felt lost when the video started, some introducing words why this topic is now discussed, or an overview about the topics handled in the topic area would have helped me to understand the connections. What would have also helped are overview slides (also in the supplementary material e.g.) Also I had sometimes the feeling, that the answers to the questions of the quizzes were not always included in the videos. For this, I would have been glad to have a extensive supplementary material.

To sum up, I was able to learn a lot, but I could have learnd a lot more with better supplementary material or a clearer structure.

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!

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

The first two weeks are very clear, after that, new notions are thrown without any definition, the calculations are not done, only results are given. I finished the course by brute-forcing the exams because I wanted to finish fast to take another course... No help in the forums too. For me this is a course to avoid except the first two weeks that helped me a lot.

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.

It was pretty intuitive and easy to follow the first couple of weeks, but then the assumed knowledge of beta and gamma distributions and their frequentist usage, stood in the way of me fully grasping the Bayesian part of it. In the end I just copied the examples from the lectures and passed the tests ... without really getting it.

Overall I liked the course but I would have preferred a more formal treatment in many cases - sometimes numbers were plugged into the formulas without first explaining their formal structure more in detail.

I did not like also the fact that the course was implemented in R and Excel (but that's a matter of taste of course).

I didn't think the lectures were very good. The instructor wasn't careful with his notation, which was very confusing, and the initial lectures where he used a pastel green marker on a green background and wearing a pastel green shirt made his blackboard text nearly invisible.

However, the assignments were execellent.

The material is good, but I've found the lectures challenging to understand even having some background in math. It would be good if all the definitions and key facts were stated more prominently in the lectures, as opposed to algebraic transformations which most readers can hopefully do on their own.

This course requires solid grounding in mathematics. No meant of Social Science graduates without proper training in statistics/mathematics. The course was good in the sense that we could how probability distributions are used to model real world problems.

Study material was certainly not adequate.

The course presented fundamental concepts of probability, regression, and Bayesian ways of thinking. However, it lacked in applications of Bayesian approaches beyond the most basic. For example, how do we estimate these models on real data to obtain parameters and make inferences or predictions?

The course curriculum is very good and it provides a very thorough background of the Bayesian approach. However, it is easy to get bogged down with all the mathy derivations. More intuition and scope of applying this knowledge in real world would have been very beneficial.

Maybe I am not the brightest, but as someone with zero knowledge of probability theory and Bayesian inference I found it quit hard to follow the videos and had to reoccur to books. Nevertheless, once I got Bayesian process I started to follow the sentences of the videos