Statistics · Columbia University
Four problem sets throughout the semester, sometimes the last problem on the problem sets would be more difficult than the exam questions and would take quite a bit of time (like 2 hours). 2 midterms and one final that were all a bit crunched for time. If you can learn the concepts well, then the exams are not that bad, but if you have difficult learning everything on your own then the exams will be pretty rough.
Statistical Inference - estimators, hypothesis testing, confidence intervals, categorical data analysis, Bayesian inference, linear regression
Her quizzes and exams are kind of meta, there would always be a couple questions above the difficulty level of the in-class and homework problems. Midterm median was 80%, final median was 50%. However, I think she curved generously. I scored 97.5-100% on all homeworks, 100% on the midterm, 90% average on the quizzes, and a 50% on the final (rip) and recieved a final grade of 96 (A). My advice would be to skip the lectures, look at the slides concurrently with the textbook, and complete all the practice + homework problems.
I have learned a relatively comprehensive foundation in statistics.
Lots of basic knowledge about statistics! This course covered a lot.
There is not a lot of workload for this class; however everything is self-studying because the professor does not explain any of the concepts properly in class. No curve, harsh grading with no partial credit on exams. Very harsh grading and unreasonable teaching.
The course covered not only statistical inference but also a lot of linear regression content. Some of the people in the class who were concurrently taking linear regressions said that this course covered more within linear regressions than the linear regressions course did. Lectures were not really good, and at a certain point in the semester I just stopped going and learned everything from the slides on my own. I think there were usually <20 people going to the lectures midway through the semester even though more than double were signed up for the course. The course is really at a learn-at-your-own pace and do-it-yourself style.
sampling methods, Normal curve, correlation, probability, testing hypothesis, sample proportion, sample mean, chi-square, etc
MLE, Confidence Intervals, Mainly proofs of different distribution, Linear Regression introduction
The content starts off easy, then get's exponentionally more difficult starting week 4-5. She isn't the strongest lecturer, but her slides are really good. You were allowed 3 double sided cheatsheets for quizzes, and 4 double sided cheatsheets for the midterm and final, which I found generous.