Challenges, techniques, and opportunities presented by data that has one or more of the following characteristics: large, unstructured, high frequency, variable quality. The course will consist of three parts: 1) computational tools for applying standard econometric techniques on large datasets, 2) extracting summary information from unstructured data (e.g. images, text) for use in econometric analysis, 3) application of statistical learning techniques (e.g. classifiers, regression trees, machine learning) and the role of such techniques in causal inference. prereq: APEC 5031 or equivalent; APEC 8221 or equivalent programming experience.