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ECON-4280 - 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; random 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." >
2023-02-10 04:08:22 +00:00
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2023-02-10 04:02:50 +00:00
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Econometric Methods for Big Data
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ECON-4280
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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; random 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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4 credits
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Prereqs:
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one of:
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< a class = "course-pill" href = "ECON-1200.html" > ECON-1200 Introductory Economics< / a >
< a class = "course-pill" href = "IHSS-1200.html" > IHSS-1200 Principles of Economics< / a >
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< a class = "course-pill" href = "MATH-1010.html" > MATH-1010 Calculus I< / a >
< a class = "course-pill" href = "MATH-1500.html" > MATH-1500 Calculus for Architecture, Management, and HASS< / a >
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one of:
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< a class = "course-pill" href = "ENGR-2600.html" > ENGR-2600 Modeling and Analysis of Uncertainty< / a >
< a class = "course-pill" href = "MGMT-2100.html" > MGMT-2100 Statistical Methods< / a >
< a class = "course-pill" href = "MATP-4600.html" > MATP-4600 Probability Theory and Applications< / a >
< a class = "course-pill" href = "PSYC-2310.html" > PSYC-2310 Research Methods and Statistics I< / a >
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Past Term Data
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Detailed View
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< th class = "spring season-label" > Spring< / th >
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< th class = "fall season-label" > Fall< / th >
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< th class = "summer2 midsum-label" > (Session 1)< / th >
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< th class = "year" > 2023< / th >
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Econometric Methods-big Data (4c)
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< li > Huaming Peng< / li >
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Seats Taken: 7/19
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Econometric Methods-big Data (4c)
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< ul class = "prof-list" >
< li > Huaming Peng< / li >
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< span class = "course-capacity" >
Seats Taken: 0/19
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< th class = "year" > 2022< / th >
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Econometric Methods-big Data (4c)
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< ul class = "prof-list" >
< li > Jason Ung Huh< / li >
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< span class = "course-capacity" >
Seats Taken: 9/19
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< td colspan = "2" class = "term summer not-offered" >
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Econometric Methods-big Data (4c)
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< li > Huaming Peng< / li >
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< span class = "course-capacity" >
Seats Taken: 12/19
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< th class = "year" > 2021< / th >
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Econometric Methods-big Data (4c)
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< li > Jason Ung Huh< / li >
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< span class = "course-capacity" >
Seats Taken: 11/19
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Econometric Methods-big Data (4c)
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< ul class = "prof-list" >
< li > Huaming Peng< / li >
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< span class = "course-capacity" >
Seats Taken: 11/19
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Econometric Methods-big Data (4c)
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< ul class = "prof-list" >
< li > Huaming Peng< / li >
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< span class = "course-capacity" >
Seats Taken: 10/19
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Econometric Methods-big Data (4c)
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< ul class = "prof-list" >
< li > Huaming Peng< / li >
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< span class = "course-capacity" >
Seats Taken: 6/19
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< th class = "year" > 2018< / th >
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