A very challenging course. Week 1 was relatively easy. However, starting from week 2, the material was exceedingly hard. I almost gave up but then decided to take a break and come back to it.

It took me a total of 50 hours to complete the course. The main challenge was the mental shift from frequentist (taught in school) to Bayesian paradigm.

Great introduction to Bayesian Statistics.

Prof. Lee uses the right approach with a theoretical introduction that helps to graps the concepts with a right balance of math and intuition. This was my first exposure to the bayesian approach to statistics and after this course I want to learn more, both on the pratical and the theoretical side.

An excellent introduction to Bayesian Analysis with some practical examples and applications. The lessons serve as a solid foundation towards understanding the philosophical underpinnings of the Bayesian approach to decision analysis under uncertainty. Thanks to Prof Herbert Lee for making the easy to understand without sacrificing rigour.

I really enjoyed this course. I thought the concepts were well explained, with a good balance of mathematical formalism and practical insight. I already had a strong background in probability and statistics, and I realize the course would be harder for those who do not--however, even then, I think the challenge is well worth the effort.

This is a great course! Much better (and cheaper) than the course I took in grad school. Full of practical knowledge, and isn't too overwhelming on the mathematics/statistical theory. It's just right. Good for anyone interested in Bayesian statistics, though some background with probability distributions may help climb the learning curve.

Excellent course.

It provides a strong basis for understanding the topics and many examples to see applications.

The topics are introduced gradually, and the quizzes are well organized to provide a step-by-step understanding.

I particularly enjoyed the conceptual and intuitive discussions of all major topics provided by the instructor.

I really enjoyed this course. The lectures were short and clearly explained, and particularly highlighted why Bayesian statistics is different and what is useful about it. I would have like a bit more walk-through on some of the derivations in weeks 3 and 4. More R exercises and further resource recommendations would have been useful.

Outstanding course to understand Bayesian statistics. Teacher is very pedagogical and the course delivery with equations written on the transparent board make everything easy to follow.

As an area for development, I would have like more information on Bayesian linear regression in week 4, through background lecture or dedicated video.

This was a great course! I had NO background in Bayesian statistics other than knowing the Baye's theorem but was able to get through and pass all the quizzes. You need to have some knowledge of probability theory though.mWatching and listening keenly to the videos, and going through the supplemental materials was extremely helpful.

Very clear lectures masterfully delivered by prof. Lee. The quizzes are good, if somewhat on the easy side. Don't be discouraged by the choice of R as the tool for assignments. R is flawed as a programming language, but you won't need to do any programming, only one-liners to evaluate various statistical functions and plot results.

This is the first online course I have ever taken so I don't have anything to compare it to, but this course was excellent! The lectures and materials were very clear and I will be adopting some of Prof. Lee's approach into my own teaching practice. The bar has been set very high for any future online courses that I will take!

Herbert Lee provides a sound introduction to Bayesian statistics while also offering, to the attentive learner, an analysis of the frequentist paradigm (e.g. the pitfalls of making objectivity assumptions and using p-values). The course requires less math background from the learner than it helps building. Highly recommended!

Herbert Lee's Tests are fun (Best!) to learn during the test! Lectures are succinct; Format of writing on the glass towards you and then flipped was right & original. Went on to try Kaggle problems independently. For usable feedback need tiny bit more on Poisson, Gamma, non conjugate intuitively & darker shirts as background.

Good course. This course is quite challenging for people who don't major in math or physics. However, it isn't so difficult to understand as the post half of this course has a lot in common. In my experience, understanding the concept of priors and posterior estimation is the core of this course. Have fun learning this course.

It's a great intro course into Bayesian statistics.

I'm a computer scientist, and data science was my lifelong professional interest. I always wanted to learn more about the Bayesian statistics, and this is an excellent course. It's mathematically rigorous - but not too hard. And the instructor is doing an excellent job.

Provided a nice balance of theory and hands-on examples. Also a good balance of hard enough that I couldn't just casually answer the questions without really working through them and understanding the concepts, but not too hard as to make me feel I couldn't squeeze this into my schedule here and there. Great foundation.

It's a great course, there is a lot of information and it might seem at times overwhelming, but it's organized nicely and prof. Lee has a very comfortable time explaining all the concepts. A few more examples would have made this course easier, but that does not mean it would have been better. It's as good as it gets

This course offers great explanations about Bayesian statistics. Although the course is a bit hard, by understanding each example provided in each lecture, I was able to grasp the basic concepts and ideas about Bayesian statistics. Also, I am now able to use R for Bayesian statistics thanks to this course.

This is a great course for anyone with no prior knowledge of Bayesian statistics. The instructor did a great job explaining the concepts and provided good examples. I also liked the quizzes and activities in R/Excel. I learned a lot from this course! I plan to take a few more courses in Bayesian stats.

Prof Lee derived the formulas in an upbeat way, which helped me learn. I'd suggest putting the actual lectures into pdf for later reference, like is done for supplementary material. Homework assignments were challenging and educational. You might suggest a review of prob distributions as pre-requisite.

Fantastic first course. The only concern I have is with the software choices. I have neither R nor Excel, but was able to easily use google Sheets. It might be worth mentioning to students that this is an option. There is even a stats package that claims parity with one of the listed packages for excel.

The quizzes in the course are very well elaborated and designed to help you learn points and details not explicitly stated in the lectures. The contents and pacing are just about right for me. Perhaps the section on normal inference would need more elaborated as this part was the most difficult for me.

This course provide the theoretical basics for anyone who wants to understand, and hopefully work with, Bayesian stats. Prof. Lee's exposition of the math behind Bayesian stat is precise concise and to the point. If I, with only high school math from age ago, could understand, I believe anyone can.

Very nice course that in my opinion nicely fits between Bolstad and Gelman in difficulty (talking in popular Bayesian Data Analysis books). Herbert Lee does a very good job at building one's intuition and understanding in the general Bayesian inference. Good starting point for moving on with Bayes.

Amazing. Simple, fast, dense, very well taught. I loved the professor, his commentaries and way to explain the contents. Thought the exercises were OK, maybe simpler than I taught but the comments in them helped me a lot to understand the topics. 10/10, a new and better way to teach! Very useful.