Very Nice

excelent!

THANK YOU

recommend

Completed

excellent

PERFECT!

Awesome.

Superb

Great!

Great!

Good!

good

good

j

This is a great course and I have learned a lot. The teacher is extremely knowledgeable and formulates things very clearly. However, this is really a math course. For me it was hard to stay motivated because the language of the course is mathematics, the teacher juggles with the concepts that my mind was still trying to process and absorb. I was able to finish all exercises, including the honors ones, but when I finished the week 3, I had to redo it completely again and buy a book on Bayesian statistics by John Kruschke which helped me immensely to rethink the basic concepts again. This course could be excellent if it included more reiterations of concepts, was explained in more general language, the pace was slower and most importantly included more practical applications. The typical statistical examples of coin flipping are fun, but too abstract. In the end, I want to know how I can apply Bayesian statistics. A lot of knowledge of mathematics was assumed and I had to look up a lot of concepts myself. The derivations sometimes also went too quick and supplementary materials were quite dense. I think this course is a perfect refresher course for someone who has mathematical background and has taken a Bayesian statistics course some time ago. But for the beginner with some mathematical background (I am familiar with the frequentist statistics, machine learning, calculus) it was too much of a challenge. If it were not a Coursera course, where I can rewind endlessly and work at my own pace, but a regular university course, there will be p=.9 that I would drop out, while my prior for dropping out would be p=.05

I would have given it 5 stars if some of the materials were covered more in depth (e.g. Jeffrey's prior). It seems like someone can dedicate a lot of time learning about how to apply it in different situations and in some instances I had to hunt for more in depth or simpler explanations for specific subjects (such as Jeffrey's prior) in other sources online. Overall the course is helpful and very useful and very well organized and gives a good amount of extra resources to read on but, I think it can become better if, the instructor did not rush through some of the subjects and spent more time explaining (especially towards the end of the course). The discussion forums help in these types of situations but, there will be a lot of searching dedicated to the specifics you are looking for. Overall an update to the course based on feedback of people that completed the course (from discussion forums) seems necessary. Adding an extra 5-10 minutes to some of the video contents can save the student from hours of research on the internet and confusion (sometimes due to the outside source). I'm not saying one should not spend time learning the material further from outside sources. Just saying the explanation might help avoid the confusion caused by looking into other sources.

The course is great and through, however, it lacks intuitional explanations of many concepts. Thus it is hard to follow sometimes. Also, it requires very decent mathematical background while, in my opinion, most of the viewers are rather economists without strong enough base (luckily I'm with actual mathematics BA). One more point is that I find this course rather unfinished because there is so much more about basic Bayesian statistics to say. For example, one of the most important topics for me and reasons to take the course are BVARs. However, they were not even barely mentioned and the course was cut off with simple regressions (without any clear use of prior/posterior ideas described in the majority of the course). Thus, I think that course is an excellent starting point for those who are really good in Statistics and Theory of Probabilities but do not know anything about Bayesian things. And this course should be definitely followed by some other, more applied one.

There are several things in the course that were able to clear up my understanding. The course instructor responds to more questions than I would have expected as well. The course uses a lot of mathematical notation and it helps to take some time with it but once you get the idea of conjugate priors down you can quickly employ them in your own problems. The course covers conjugate priors for several different likelihoods including the normal distribution and the binomial distribution. Although the derivation of the conjugate priors looks daunting as it is written down, the usage of the priors make Bayesian statistics much easier.

This course uses R and Excel but is not a course in either. Most of the computations that are performed for the quizzes are pretty simple and require little skill in R.

I am glad that I have taken the course and would take another if provided by this instructor. I plan to reference the materials provided in the future whenever I need a refresher.

The course is a great introduction into Bayesian statistic analysis. I particularly liked the detailed explanations of where the parameter formulas came from. Also a great thing, in my opinion, was to write the explanations on the glass instead of just displaying the final results. It kind of provided a sense of interactivity and made the material more digestible for a person with not such a strong background in math. It greatly smoothed the learning curve for me and kept interested and motivated to finish the course. In the end the pace accelerated a bit but was still manageable. Four weeks seems a great duration for such a course - not becoming boring and tiring. Honors tests were quite easy, I'd prefer to have a little more challenge. Overall I'd recommend the course for everyone who wants a quick introduction into Bayesian statistics. It provides a solid background for further studies.

What I liked in the course is that it focuses on examples and solving actual problems. The quantity and the quality of the lectures is great, but what I really missed is written lectures where one can always lookup forgotten things or read details etc. Also, one thing that I think might be added easily is a reference to Mathematica and Maple's routines. I'm using Maple and it took some efforts to get on track. And finally, I think that 4 quizes per week is really too much for working people. It's true that the tests weren't that difficult, but it took me about an hour to do each, so I think 30 mins of lectures vs. 4 hours of quizzes is a bit unfair. Of course, my background in statistics is non-existent so it may be that it took me longer than average. But I think the course material could have been spread over say 6 weeks for lighter load on the students. All best to the team!

The course is good as an introductory course to Bayesian statistics. However, there is no much context and no much explanation for many of the calculations. I think the course would greatly benefit from: before each existing video, make a 2 minute video of a real world situation why that approach is helpful (bus arriving every 10 minutes, etc.) so that all students can connect with the content, then the video as it is now, then a 1 minute video linking the formulas with the real world example. As the course stands now, it is difficult to really follow why the formulas have any application and this only appear in the quizzes (where you are being tested, not learning in a non-stressful situation). I think more context and more examples with the formulas would make this course perfect. As of now, it is way too disconnected from real world, although still worth it.

It was a really well taught class and I enjoyed watching it. Unfortunately I seem to lack some basic understanding, since I am not a statistician. Therefore I had problems following the course and had to do quite a bit of research to do on my own to get long. Still, I managed to get 100% correct on all quizzes and all honours quizzes. So it seems, that if you put in enough effort, you can get 100% on the course without understanding many things. This is not to say, that this course is easy, it took a LOT of effort, but it was possible. I will now investigate further to get all of the basics. Maybe I will come back and take this course as a refresher. Other than that I can whole-heartedly recommend this course. The presented material is very well organized and and presented and Professor Lee is a really good teacher.

A great introduction. I feel like I know a lot more about bayesian statistics now. But I do mostly feel like there is quite a bit I don't know, and while I passed, I feel like there is quite a bit more I need to do to really 'get it'. The professor recommended some books in a discussion forum and I'll be going through some of those next I am sure. I also feel, looking back, I should have had some additional math preparation before starting. The calculus was vaguely familiar but with the pace of the lectures, I felt occasionally lost. I would have found it helpful if there was a quick primer on calculus to know and review at the beginning of the course. All in all great course. Loved the presentation method.

This course provides a solid overview of simple Bayesian models and common distributions used in those models. It also provides an initial understanding of conjugate prior distributions and non-informative prior distributions. The R code used in this course is very simple; easy for a beginner, but perhaps a bit simple if you are already familiar with programming in R and doing commonplace frequentist statistical analyses (regressions, ANOVA, etc). Overall, this course is definitely worth taking if you are interested in Bayesian statistics and need a good place to start. There are quite a bit of videos and supplemental materials which allow for a broadened understanding of the materials. Thanks, Dr. Lee!!