• Covers the use of data to guide decision-making, with a focus on data-rich and high-dimensional environments as are now commonly encountered in both academic and industry applications. Begins with an introduction to statistical decision theory, including Bayesian perspectives. Covers empirical Bayes methods, including related concepts such as false discovery rates, illustrated with economic applications. Requires knowledge of mathematical statistics and regression analysis, as well as programming experience in R or Python. Students taking the graduate version submit additional assignments.

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