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ERIC Number: EJ1400128
Record Type: Journal
Publication Date: 2023
Pages: 15
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0022-0485
EISSN: EISSN-2152-4068
Available Date: N/A
Integrating Data Science into an Econometrics Course with a Kaggle Competition
Journal of Economic Education, v54 n4 p364-378 2023
As vast amounts of data have become available in business in recent years, the demand for data scientists has been rising. The author of this article provides a tutorial on how one entry-level machine learning competition from Kaggle, an online community for data scientists, can be integrated into an undergraduate econometrics course as an engaging activity using only linear regression. Other techniques in this tutorial include log-linear and quadratic models and interactions of explanatory variables, which are common functional forms in econometrics. The competition allows students to use real-world data, build a predictive model, submit their model online to be evaluated instantaneously based on accuracy, and keep improving their model. R and Python codes are provided to make it possible for readers to replicate.
Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A
Author Affiliations: N/A