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ECON-6280: Econometric Methods for Big Data
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< meta property = "og:description" content = "Examine advanced econometric and statistical methods for the analysis of high-dimensional data, otherwise known as "Big Data." In this setting, detailed information for each unit of observation informs machine learning techniques such as classification and regression trees; rECandom forests; penalized regressions; and boosted estimation. These prediction methods are then utilized to improve causal modeling, with applications in the study of healthcare demand and supply modeling, and behavior of consumers and businesses. Â" >
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Econometric Methods for Big Data
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ECON-6280
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Examine advanced econometric and statistical methods for the analysis of high-dimensional data, otherwise known as "Big Data." In this setting, detailed information for each unit of observation informs machine learning techniques such as classification and regression trees; rECandom forests; penalized regressions; and boosted estimation. These prediction methods are then utilized to improve causal modeling, with applications in the study of healthcare demand and supply modeling, and behavior of consumers and businesses. Â
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3 credits
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Prereqs:
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none
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Econometric Methods-big Data (3c)
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Econometric Methods-big Data (3c)
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Econometric Methods-big Data (3c)
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Seats Taken: 0/19
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Econometric Methods-big Data (3c)
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Econometric Methods-big Data (3c)
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Econometric Methods-big Data (3c)
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Econometric Methods-big Data (3c)
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Seats Taken: 5/9
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