Introduction to Quantitative Social Science, Teaching Assistant to Mark Ratkovic
Course description: (Introductory statistics course for undergraduates majoring in Politics or Sociology) The course introduces basic principles of statistical inference and programming skills for data analysis. The goal is to provide students with the foundation necessary to analyze data in their own research and to become critical consumers of statistical claims made in the news media, in policy reports, and in academic research.
Course evaluations:
“Henry was AMAZING. Super helpful, and constantly sought to engage students in precept. Also super accessible in Office hours”
“Henry was extremely helpful and clarified a lot of confusing content.”
“I thought the precept was generally pretty helpful. Henry is a fun dude, which makes doing programming at 9 pm on a Wednesday not as grim as it could be”
“Precepts saved me in this class. Henry was really committed to helping us understand, from the most basic to the highest level concepts. He taught us a lot and I think that he learned a lot as well.
“Henry is the man: really great preceptor who was willing to tailor precepts to what the students want.”
“Henry was a super helpful preceptor and was willing to answer all of our questions!”
“Henry is great – loved him.”
“Henry is very hardworking and was committed to our learning. He consistently asked for feedback and clearly demonstrated that he cared about our progress.”
“Henry was the person in this class who actually taught me anything.”
“Henry was extremely caring and a passionate teacher!”
“Henry was very receptive to our comments and interests as far as what we wanted to cover in class.”
Advanced Data Analysis for the Social Sciences, Teaching Assistant to Yu Xie
Course description: (Second statistics course for Sociology PhD students) Introduces theories of inference underlying most statistical methods and how new approaches are developed. The first half of the course covers maximum likelihood estimation and generalized linear models. The second half covers a number of topics useful for applied work including missing data, matching for causal inference and, others. The course concludes with a project replicating and extending a piece of work in the scholarly literature.
No course evaluations (graduate-level course)