Applied linear models with a focus on practical applications. Topics include least squares estimation, model diagnostics, variable selection, regularization, mixed-effect models, splines, additive models, an introduction to GLMs, and missing data. Matrix formulations are used. Data analysis in R and Python and effective written communication is emphasized.