Psychology · Columbia University
Learned a great deal on the fundamentals of statistics as it relates to behavioral scientists. Topics included: basic tenants of statistics, variables of measurement, how to visualize data, varying kinds of null hypothesis statistical testings (bootstrapping, permutations, linear regressions, binomial tests, z-tests, t-tests, rank sum test, chi-square test, ANOVA, etc.), was exposed to the limitations of statistics and computer learning, effect size and power (this is vital for data literacy so I wish that it was covered more), and bayesian statistics was touched on. Get ready to use R!
1-3 assigned readings before every class. Three 10-min presentations (15% each) in front of only your TA. Midterm and final (MCQ ~60 questions; lowest is 20%, highest is 25% of your grade).
Stats formulas, statistical thinking and basic R coding.
I'm sure this man is nice, but he's so boring. Part of that is the class: it's structured in a way that jumps around a lot and doesn't really allow for continuity the way most lectures do, but the other part is him. He co-taught this class with John Morrison (slightly better, but only because he's funny) and I genuinely found myself dozing off at times. He explained things that didn't need explanation (like the process of addition), and didn't expand on things that did (like if it was going to be on the exam). Classes were long slides of images and text and it's very hard to know what's actually important to know with all the history, quotes, names, and theories they bring up. They take attendance (you can miss up to 3 classes), but honestly the slides are mostly pictures or huge chunks of text so it's hard to study outside of class anyway. That said, this isn't a hard class AT ALL, just a slow one. If you show up and listen during class you can get an A-; if you do the readings, an A. The subject matter is so so interesting, but their approach made me second doubt my entire major—but I've realized intro classes in general tend not to be great since they have to cram so much in.
10% attendance, 10% daily quizzes, 45% 3 papers, 15% midterm, 15% final, 5% highest test score super manageable work load. 1 hour a week for the readings, considerably more time when a paper was due but still not bad with any amount of time management skills
I think the most important thing I learned was how to distinguish between different kinds of data analyses and when to use each of them. I learned the importance of analyzing data with validity and also how to navigate R Studio.
Really enjoyed prof baldassano's lectures. you can really tell he is very learned in the subject area but he makes the content really digestible and fairly fun to learn.
I learned a lot about how statistics works, given I knew nothing about it beforehand. I learned basic statistical literacy.
statistical concepts and how to code in R
I learned how to use a wide array of statistical analyses, as well as a basic level of coding in R.