In Winter of 2022, I taught a for-credit, no-preqs course called “Introduction to Computational Social Science” at Colby College. It was some of the most fun I’ve had in grad school. The course built upon content and experiences I had organizing the Summer Institute for Computational Social Science at UCLA in 2019, 2020, and 2021. Those workshops were primarily pitched at PhD students with strong research skills but little experience with CSS/data science methods. In contrast, this class attempted to give undergraduates (many of them freshman) some intution for social science AND research AND computational methods.
Over the course of four weeks, we divided our time up relatively equally between four activities. In the mornings, I gave lectures on unsupervised ML, NLP, and network science. We also had a journal club where students took turns presenting CSS papers and critiquing them. In the afternoons, we alternated between hands-on activities applying algorithms to real data or working on two group projects. I’m extremely proud of the ground students were able to cover over four weeks.
Below I’ve posted the syllabus and some (rough) thoughts about my approaches to teaching. I’ll continue to update those posts as my thoughts evolve.