Econometrics Pedagogy and Cloud Computing: Training the Next Generation of Economists and Data Scientists

Handel Danielle V. (), Anson Ho (), Kim Huynh, David Jacho-Chávez and Rea Carson H. ()
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Handel Danielle V.: Department of Economics, Emory University, Rich Building 306, 1602 Fishburne Dr., Atlanta, GA, 30322-2240, USA
Rea Carson H.: Department of Economics, Emory University, Rich Building 306, 1602 Fishburne Dr., Atlanta, GA, 30322-2240, USA

Journal of Econometric Methods, 2021, vol. 10, issue 1, 89-102

Abstract: This paper describes how cloud computing tools widely used in the instruction of data scientists can be introduced and taught to economics students as part of their curriculum. The demonstration centers around a workflow where the instructor creates a virtual server and the students only need Internet access and a web browser to complete in-class tutorials, assignments, or exams. Given how prevalent cloud computing platforms are becoming for data science, introducing these techniques into students’ econometrics training would prepare them to be more competitive when job hunting, while making instructors and administrators re-think what a computer laboratory means on campus.

Keywords: virtual online learning; Jupyter; GitHub; Python; R; Stata (search for similar items in EconPapers)
JEL-codes: A11 A22 A23 C87 C88 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1515/jem-2020-0012

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