Prerequisites: MET CS 544 or MET CS 550 or consent of instructor. You will learn the foundations of statistical machine learning, regression, and classification, and explore the key components of statistical models, including how to construct, interpret, and evaluate them. Topics include data description and visualization, statistical inference, one- and two-sample tests for means and proportions, simple and multiple linear regression, multinomial and logistic regression, analysis of variance (ANOVA), and regression diagnostics. For each topic, you will examine the methodology, underlying assumptions, interpretation of results, and model assessment. The course includes a programming component using R or Python, providing hands-on experience that reinforces theoretical concepts. Methods are presented through real-world examples to help you understand when and how to apply different statistical techniques effectively.

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