Computational Social Science

3 credits · Graduate

This is the 1st semester of a two-semester course that provides a rigorous introduction to methods and tools in advanced data analytics for social science doctoral students. The goal of the course is to provide students with a strong foundation of knowledge of core methods, thereby preparing them to contribute to research teams, to conduct their own research, and to enroll in more advanced courses. The course will cover research reproducibility (fall), machine learning (fall), natural language processing (spring), and causal inference (spring). In contrast to other courses currently offered on campus, this course’s intended audience is applied researchers, typically social science doctoral students in their 2nd or 3rd yr of graduate school.