Quantitative methods for analyzing biomedical data. Data generation and related domain knowledge, data visualization and pre-processing tools, scientific problem formulation and data modeling, quantitative methods selection and application, pipeline programming and coding, and result checking and visualization. The interdisciplinary approach prepares students in math, statistics, biostatistics, computer science, and engineering for careers in biomedical data science. Recommended prerequisites: Multivariate calculus, linear algebra, undergraduate-level probability, undergraduate-level statistics, and R programming.
Department of Mathematics