In the final math module, we will focus on how linear algebra, ODEs, statistics, and signals & systems techniques can be used to interrogate data from biological and engineering experiments. The lecture topics include: Jacobi, Gauss-Seidel, and SOR iterative solvers for large linear systems; Gauss-Newton iterations (nonlinear least-squares); the ANOVA table, multi- factor regression, and intro to the general linear model (GLM); data deconvolution; Monte Carlo, bootstrap, and kernel density estimation. Prior exposure to linear algebra (BE 601 equivalent), basic probability and statistics (BE 200 equivalent), and working knowledge of a programming language (Matlab, Python, etc.) is highly recommended.