Rigorous introduction to health data science with applications in biomedical research, epidemiology, and health policy. Conduct reproducible data exploration, visualization, and analysis with R. Interpret and translate results for interdisciplinary researchers. Critically evaluate data-based claims, decisions, and policies. Includes exploratory data analysis, visualization, basics of probability and inference, predictive modeling, and classification. No statistical or computing background is necessary. Not open to students with credit for 100-level STA course, STA 210, or STA 300-level or above. This is a half-credit course, must complete STA 198L-1 AND 198L-2 to get curriculum codes.
Not open to students who have taken a 100-level Statistical Science course, Statistical Science 210, or a Statistical Science course numbered 300 or above