par Essam S•
The instructor is very good, more examples need to be added, there are mistakes in the evaluation
par Tim S•
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.
par Jun I•
Great course which covers from fundamental probability theory with good examples for better understandings.
par Mauricio F•
It was a great course. Good combination between theory and practice.
par 상은 김•
Helpful to understand data sciences basic thories
par Cora M•
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.
par Ke M•
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.