Statistics · Columbia University
Homework in R (Not weekly- Due about every 3rd class, but again, soft deadlines, especially if he felt the class was struggling on particular concepts. We only actually got through 7 of them, and 8 was posted but not graded over reading week.) Midterm 1: Half T/F, Half short answer problems. No direct coding problems (almost entirely conceptual with a few minor computations in short answer). Average around 85. Midterm 2: This one was all short answer/ short computations. I think average was still around 85. Final: A LOT of T/F ( around 75%) and only 2 short answer. If you've done every HW yourself and read the book well, you should understand the concepts enough to do fine on T/F.
Analyzing contingency tables, logistic regression, multiple logistic regression, multinomial models, and other regression methods that apply to categorical data
He gives a little less than average workload. What is nice is that he is very lenient on submission time, and I have turned things in almost a week late. His homework assignments are not unnecessarily hard. Quite a bit of it will use R, but it will never be anything too difficult that you cannot ChatGPT. With prof. Neath, it is definitely more about learning the concepts that some random R syntax. Just like his in class examples, his homework is usually meant to illustrate something. His grading is also quite nice. I took him for another course (Bayesian Stats), and iirc, everyone got a B or an A, and it was split roughly 50/50. His tests could be hard, but the curve works out so that everyone gets a solid grade. If you go to class and do the homeworks, it should not be anything you haven't seen on the assessments.
Lot about stats, regression, model fitting and assesment
Very interesting directions in research of stats.
HW that can just raise your final grade, 2 midterms and a final, everything is fair and not made to trick you
some advanced statistics skills including probability, percent error, mean, standard deviation, integrals, etc
Many useful hypothesis testing. SUPER useful in real life! Love this course so much, Professor Neath is excellent.
Problem Sets - 40% of the grade (all involve coding to some extent) Midterm - 20% Final - 40%
Problem Sets (20% of grade) - taken from the book, and usually given every two weeks. There were 7 problem sets for the semester. Midterms (20% each) - can be challenging, but no real big surprises. Final (40%) - it was okay. There is a curve applied to the overall grade.