Statistics · Columbia University
-MLE/MOM estimators, Forming Confidence Intervals, Hypothesis Testing, simple linear regression
20% HW 40% midterm 40% final There were homeworks once every two weeks which probably take 6-8 hours. The exams are almost identical to the practice exams and overall everything is graded pretty fairly.
Very hard topics...could be lost all the time. Not recommend for those non-math majors. The homeworks are very hard, cannot complete unless you go to office hours. The midterm was easy though, not sure what the final will look like. The professor was OK, for some reason he was late or canceled for several lectures approaching the end.
Honestly nothing. I should not have taken this class, as I already struggled with probability to begin with, but I had tremendous difficulty in this class and have learned my lesson. I am dumfounded as to how I was able to score high on the majority of the homework assignments, let alone the midterm. When I sat down for the midterm, I can truthfully say I knew the answers to 2 out of the 5 total questions, yet somehow pulled out an 85%.
Statistics, estimators, hypothesis testesting, bayesian statistics, linear regression
I wouldn't take this class if I didn't have to, but if you do have to, Johannes is pretty good. He doesn't have the most interesting lectures ever, but they are pretty educational and his lecture notes are kinda bomb. He is pretty straight to the point and doesn't teach much extra material. I think every person in the room was not particularly interested in the material (including the prof), but it is relatively important theoretical background for Statistics. He pretty regularly would end class 30-40 minutes early and even cancelled the last lecture because there was nothing more to teach. In general, he is a pretty nice guy, and the class is very fair. He is very responsive to in class feedback and quite accessible/approachable. If you have to take the class, I would recommend him.
Knowledge about arbitrages, martingales, local martingales, stochastic integrals, and black-scholes model.
-Basics of measure theory, stochastic process, Brownian motion, Ito's formula
how to create inferences from statistics--t-tests, MLE, MSEs, etc.
The fundamental methods of calculus-based statistics.