Grade distributions are collected using data from the UCLA Registrar’s Office.
This is by far one of my favorite Stats classes so far. Professor Gould is an incredible person and instructor. He was able to explain everything incredibly well and was tremendously active on Campuswire and answering emails.
I personally enjoyed his use of participation in the class, it helped me from going insane to have some personal interaction with others during the Spring 2020, COVID-19 quarter. However, if you are anxious about these type of zoom breakout room activities, perhaps this class isn't for you because they happen almost weekly. (Prof gould would put us into breakouts and we'd explore the dataset he gave to us and do regression on it and stuff).
The homeworks were pretty easy and didn't require a lot of R knowledge. It is a lot of repetition in terms of the looking at model adequacy and running lm() tests on data.
There was not a final exam but rather a final project where you and a few other people (i was in a group of 2) investigate a dataset on your own and right a research paper (about 5 pages) on the topic. The project was easy and fun if you choose a fun topic that you're actually interested in. Grades were never a worry in the class, and was strongly right-skewed throughout the course. Even still, Prof gould curved the class on top of this distribution to ensure no one got lower than a C. I can't wait to take another course with him!
For Stats 101A with Professor Gould, I feel as if I learned quite a bit about regression while not having to worry about my grade too often during the class. Professor Gould provided us with real-world examples in which the skills we acquired in this class could be used. I found this topic fascinating from the start of the quarter, and Professor Gould only increased my love for regression.
While some lectures were more of the standard fare (i.e. him lecturing, us taking notes), there were a few interactive activities to ensure we were understanding the material. Moreover, he would sometimes ask us to collaborate and work in R toward solving a problem he posed, giving us greater practice and thus greater understanding of regression. The homework assignments were appropriately assigned for the most part, with none of them taking an egregiously absurd amount of time. The midterm was an extremely fair assessment of the class up to that point; there were no tricks or convoluted questions.
At the end of the quarter, things became hectic with the coronavirus, but Professor Gould adapted accordingly and made the grading scheme for the final and the class more lenient as a result. This professor genuinely cared about students learning the material and ensuring that everything made sense while also grading favorably on exams.
TL;DR: Professor Gould is a wonderful professor with whom I would take another class; he cares about the students understanding the material while also making grades reasonable. However, if you are a stats major/minor, you should not just be caring about the grade because this material is extremely relevant to potential future jobs, and I believe Professor Gould established a great foundation upon which I can expand.
Given the extenuating circumstances that the coronavirus has put UCLA in this quarter, I could not have been more grateful to have as understanding and accommodating of a professor as Dr Gould. He was very lenient with grading on the final project/exam, telling us to complete as much of the final project as possible and that we'd only be graded on those problems.
The structure of this class was very manageable to begin with, and he focused a lot on solidifying our intuition of the concepts. We have weekly homework assignments where you do modeling in R, but none of them took more than a few hours. There was only one midterm, which was scheduled before multiple linear regression so we were only tested on simple linear regression concepts that are fairly straightforward. From the beginning of quarter, the final was just going to be a take-home project that ended up being like just another homework assignment.
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