Use text, network, and bibliometric data to investigate social questions with transparent computational methods.
This cross-disciplinary program introduces computational approaches to social inquiry. Learners explore appropriate text or network datasets, develop an analysis pipeline, and consider the relationship between computational patterns and social explanations. Research ethics, privacy, representativeness, and algorithmic bias are part of the methodological discussion.
Proposed learning outcomes for this program example:
The sequence below illustrates how this program’s content can be organized. Topics, pacing, and project depth are adapted for each offering. This is not an archived record of a specific cohort’s weekly syllabus.
Build a research workflow from Python data preparation to machine-learning evaluation and an applied project.
Use systematic review methods and R to synthesize research evidence and interpret quantitative findings.
Turn public datasets into reproducible analyses using R, research design, regression, and introductory measurement methods.