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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." >
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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.
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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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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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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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