Data Science for Epidemiologic Research

This course introduces data science approaches for epidemiological research. These methods include generalized and penalized regression, approaches for high-dimensional exposures, prediction models, etc. Each week we will have a lecture session and a coding session. Although other courses in the School and other Departments might also present some of the methods covered here, the emphasis of this course will be on applications in epidemiology specifically and the appropriateness, assumptions, strength, limitations and interpretation of results within the epidemiology framework. While the core concepts are broadly applicable across epidemiology, in-class applications and labs will be specifically rooted in environmental epidemiology, e.g., air pollution and climate and health. Students are encouraged to bring their own research interests and domain expertise to class discussions, using the provided environmental examples as a template for application in their respective fields.