While I found the course to be fairly easy, it definitely covered a lot of stuff, which sometimes felt a bit overwhelming and like we were rushing through concepts. The overwhelm was helped by the fact that we really didn't cover anything in great detail, although it would have been nice to get more of a background on some of the concepts. This term the overall grade was comprised of 12 tutorial assignments, 4 R assignments, 2 midterms, and a final. I found the tutorial assignments to be perfectly sufficient for understanding the material, and the R assignments were often good refreshers on material covered a few weeks earlier. The questions on the exams were exactly like those on tutorial assignments, so I felt that the midterms and final were very fair.

The course covered probabilities/properties of probability, random variables, various continuous and discrete probability distributions, and hypothesis testing, among other things. I had previously used some of the material in other courses, just taking certain facts for granted, so it was nice to get a bit of an idea where those things came from. I'm sure at least some of the material will be useful in future courses, given that it has already been used in courses I've taken in the past.

Dr. Yu often didn't go into much more detail than what was in the notes during lectures, and often seemed to not know how to answer questions about the material, but she usually got the point across with the examples. She did frequently make small errors, and then she would get kind of flustered and need to take a moment to recompose herself, which I sometimes felt detracted from my ability to follow the lecture. She was very organized in terms of actually running the course though.

Do the tutorial assignments before going to the tutorial. That way you'll know if you don't understand something and need to ask your TA for help with something specific when you walk in. And, if your TA allows it, always check your answers before submitting the assignment.