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

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.

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.

I don't think the course material covers what we are asked to do in the hw sometimes combining several topics into one question when we are only given one or two basic examples of how to solve a problem for a topic. It's a little disappointing in an online course that I have to spend a majority of my time learning from videos on Youtube, khan academy and other statistics courses on coursera. I hope there will be some more material added to the course because I think the teaching is good, but the lessons in application is minimal.

1) There is a BIG gap between the learning/content of video tutorial and the knowledge assessment in weekly assignments, especially, when it comes to application of R.

2) There is absolutely no help/guidance on how to correct the mistakes in the assignment, the ones which get graded automatically.

3) The suggested time duration for the assignments Vs Reality was also way off for me.

Overall, a suboptimal experience.

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).

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

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

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

I rate the academic content at maximum. Pretty straightforward and foundational lessons for statistics. I had to dock the 1 star though because of requiring a buggy platform such as proctorU for the for credit version. Either proctorU has to improve a whole lot or this program should drop them and find a better online proctoring platform. Or perhaps offer onsite testing services such as prometric of pearson vue. Honestly, proctorU has a way to go to make the experience horror-free for the student to take the most important part of the course, ie final exam. Well lets just say I had a very bad experience with it and I might have lost points because of it,( did not answer one question and ended my exam 20 min early just to end the horror and walk off my frustration). ProctorU platform is just barely functional. Their proctor might have just used fb messenger or zoom along with their pc screen viewer and their mouse and kb grabber, it probably would have been better... but i dont know just sharing my one and only experience of it, which was quite negative. Despite this I'd still recommend the course for anyone who want's a path into data science, acads are solid and a good foundation into the field, imo.

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 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.

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.

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.

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

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!

Excellent and enjoyable - clear and straightforward explanations by the lecturer, clear examples to support understanding. Quizzes and programming assignments consolidate learning effectively. Lab is useful, especially in week 6, for learning how to run basic simulations.

This is a very good course. A lot of detailed and practical content in the lecture. If we can have a section to bring R programming and the Math together that would be even better. And may be we can use R function call instead of round decimals in the lab answers.

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.

Very well presented material with invaluable and illuminating labs, quizes, and programming assignments. I felt like the pace and assignment difficulty were perfect.