The course covers the fundamentals of classical topics in regression and experimental design that are used to solve a wide range of data science problems. Course topics include estimation, Gauss-Markov Theorem, inference, prediction, diagnostics, model selection, and factorial and block design. Recent advances in linear models including regularization, nonparametric regression, and causal analysis will be covered. The course utilizes statistical programming language such as R, SAS, and/or Python. Prerequisite: Graduate Standing. (F;S;SS)