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As a faculty member
As a faculty member
Most recently, I have been teaching an advanced quantitative methods course (SOC 212B and 212C) for PhD students. We cover causal and descriptive estimands, nonparametric identification with Directed Acyclic Graphs, and estimation by statistical and data science approaches. The website has all materials.
I teach a related undergraduate course, Social Data Science, which is welcoming to all students regardless of coding and statistical background.
Below are links to course materials for courses I taught in the past.
Causal Inference (undergraduate, co-taught with Sam Wang).
Fall 2023. causal3900.github.io/fa23
Studying Social Inequality with Data Science (undergraduate)
Spring 2024. info3370.github.io
Spring 2023. info3370.github.io/sp23
Causal Inference in Observational Settings (PhD seminar)
As a PhD student
As a PhD student
My teaching experience is in a statistics sequence aimed at Ph.D. students in sociology, cross-listed for undergraduates.
I was a teaching assistant in the full two-semester sequence under Brandon Stewart in the 2016–2017 academic year. I returned to TA the second course of the sequence in spring 2018 under Matthew J. Salganik. In each course, I led a 2-hour section reviewing materials from the course every other week. (link to sample handout on generalized linear models) (links to sample slides on random variables, likelihood inference, binary outcome models, duration models, and missing data)
Course evaluations have been universally positive.
Course evaluations have been universally positive.
"Ian did a particularly great job tying class material to social science research."
"He was good at deconstructing theoretical concepts into real world examples that we could appreciate and understand."
"Ian is great at providing intuitive examples and explanations of complex topics."
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