Rigorous introduction to health data science using current applications in biomedical research, epidemiology, and health policy. Use modern statistical software to conduct reproducible data exploration, visualization, and analysis. 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. This course focuses on the R computing language. No statistical or computing background is necessary. 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.
Prerequisites
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
