My rating applies to the first week, as I'm dropping after my experience with the first assignment. This is not a commentary on Prof. Dougherty, who seems like a teacher I'd really like to have in an in-person setting. It refers instead to the Gilliamesque homework submission and grading system. Before you join the class, be prepared:

All homework is submitted in an ipynb using an R kernel, and homework is autograded. The grader gives zero feedback regarding what was incorrect, not to mention why or what the correct answer is. All you get is the number of cells that didn't pass; when you reload the assignment, there is no indication of what was wrong.

As a math nerd troll, however, it's magnificent—the grading mechanism itself is a probability problem that provides one with hours of fun. By which I mean frustration.

I joined this class as a refresher, because I love probability. I'm dropping this course before that changes.

peer review assignments are useless

Sorry, but I can't learn R by myself. I know how to do all the calculations, just don't know how to put it in the R language.

The instructor is very good, more examples need to be added, there are mistakes in the evaluation

Overall I thought this course was very good. The lectures were clear. I was even more impressed by the work that was put into designing different kinds of assignments. After completing them, I felt like I understood concepts and techniques much better than before.

That said, I have two big criticisms. First, I really did not like the textbook that was provided. It is supposed to be different from a traditional text book, in a way that makes it easier to understand, I guess. But honestly I thought it had the opposite effect. The non-traditional style made it harder to look up information I wanted to review. I ended up searching for other online sources for better explanations of what was going on.

Second, while I think the class is great on its own, it is part of the Statistical Inference Specialization, and it feels like there was a lack of coordination between the people designing this course and those designing the second course in the series. The second course seems to presuppose much more advanced understanding of probability distributions than this course provides. So while I think the course is great on its own, if you are expecting it to prepare you for the second course in the series, it honestly fails to do so.

The professor's instruction is clear and concise, but I wish there were more videos to expand on topics not discussed. The auto-graded assignments are painful since there is no feedback on which problem was wrong (hint: only do one problem at a time and submit to grader. it is painfully slow but this way you know how you did on each question). This course assumes you have basic familiarity with R and can do basic differentiation & integration. I would not recommend this as a first course in probability - this course is best for those who have had some exposure to probability already (E.g., undergraduate level course).

This was a very good course. The material was well thought/planned out such that the readings, lectures, and homeworks built off each other in a constructive manner, which reinforced the material. I highly recommend taking this course as an introduction to probability.

Need to brush up integral calculus for thios course. Something I haven't looked at for 40 years.

Great course which covers from fundamental probability theory with good examples for better understandings.

Very logical arrangement, proper speech rate, crystal clear!

Great intro and very well presented by the prof

It's pretty basic material, but that's not a bad thing. I had no trouble with the content.

It took a month, or something like that, for Coursera to let do the peer grading that's required by the course.

Not enough participants for peer review, not quite enough time spent on curriculum

This class isn't good. The textbook is poorly written and doesn't include any exercises. Nothing is explained. Students are expected to understand advanced concepts with little to no instruction and no support in the discussion forums.

Duke University's Introduction to Probability and Data Analysis with R is a much better course.

I felt this course was challenging, in a good way. I really appreciate the number and depth of the exercises for each module. The only downside is the auto-grading of the homework doesn't tell you which question you got wrong, so that can be frustrating. But overall I feel very lucky to have access to this course for Coursera price, and I plan to finish this specialization, because I really feel it's beneficial for working toward mastery of probability and statistics.

This is very good and well prepared course. The subject is very well presented and explained. Every week there is a programming assignment, peer-rated projects and a quiz. It gives opportunity to think about the the new knowledge and use it in different way. Thank you very much to the instructor!

This course taught me the basics of probability, R programming, and Latex. I am deeply grateful to Prof. Anne Dougherty, UC Boulder, and Coursera for this tough but wonderful experience.

This is a great course on probability. Although I felt like it was too easy and should include more PDFs (such as Beta and Gamma) and random variable transformations.

This is an excellent course to review foundational probability concepts. The instructor speaks clearly and goes through examples thoroughly for each concept.

Marvelous course.......

It is the best course in all coursera. Thanks a lot <3

It was a great course. Good combination between theory and practice.

Helpful to understand data sciences basic thories

Exactly the probability course I was looking for

Good course with sufficient amount of practice.

The course is very good.