2020 Fall PHDS-1 (LU)
BSTA001
In Population Health Data Science I (PHDS-I) students will spend the semester learning the fundamentals of probability theory, univariate statistics, statistical computing, and machine learning. A mix of traditional and experiential learning will focus on how to build an analysis pipeline to answer pressing questions in population health. In-class examples and projects will use real data sets. Examples include: comparing cardiovascular interventions in clinical trials, evaluating the incidence of influenza in the United States, and visualizing international health expenditures and burdens. Students will propose a small data-driven project focused in population health, and use their newly-acquired data science skills to collect, analyze, and present their work.
tom mcandrew
Assistant Professor
I am a computational scientist with a methodological focus on developing ensemble forecasting algorithms and extracting statistical information from unstructured human judgment data. The areas of application that interest me most are building tools to combine forecasting and predictive models in the health sciences.