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This course will teach from a textbook written by a prominent economist with leading expertise in data science and machine learning. Students will be presented with statistical techniques to process big data for making business and economics decisions. Topics may include statistical uncertainty, regression, classification and factor analysis, experimentations and controls, frameworks for causal inference. We will also explore the relations between nonparametric econometrics, machine learning and artificial intelligence. The statistical package R will be used to illustrate concepts and theory. Prerequisites: Econ 102A or equivalent and Econ 102B.