Data, A I, and the People's Health

(3 Credits)

Motivated by social science theory, this course introduces data science concepts related to health equity. Topics include challenges across the data lifecycle (e.g., sampling, analysis, reporting), statistical sampling methods, representation techniques, and frameworks for measuring health disparities. Students will examine algorithmic fairness, predictive model implications, and the effects of data variance across social and geographic contexts. Practical exercises involve community-engaged research, identifying equity concerns in quantitative data, and addressing statistical issues to mitigate health inequities where applicable.