Must have taken PPHA 30537 Data and Programming for Public Policy I-Python and PPHA 30538 Data and Programming for Public Policy II-Python or obtain instructor consent to enroll. The objective of the Data Science sequence is to train students to be successful and autonomous applied economists and data scientists in government and industry. In the first two courses of the sequence, students learned programming, as well as how to handle, summarize, and visualize modern datasets. The objective of this course is to train students to be insightful users of modern machine learning methods. The class covers regularization methods for regression and classification, as well as large-scale approaches to inference and testing. In order to have greater flexibility when analyzing datasets, both frequentist and Bayesian methods are investigated. Students may request to waive the prerequisites by providing the instructor with evidence of equivalent programming experience.