Data Science: A Primer for Economists

Gomez-Ruano, Gerardo (2020): Data Science: A Primer for Economists.

Abstract

The last years have seen an explosion in the demand for data science skills. In this paper, I introduce the reader to the term, point out the technological jumps that allowed the rise of its methods, and give an overview of the most common ones. I close by pointing out the strengths and weaknesses of the corresponding tools as well as their complementarities with economic analysis.

• Item Type: MPRA Paper
• Language: English
• Keywords: Data Science; Statistics; Quantitative Methods; Labor Market; Technological Change; Numerical Methods; Econometric Methods
• Subjects: A - General Economics and Teaching > A1 - General Economics > A12 - Relation of Economics to Other Disciplines; C - Mathematical and Quantitative Methods > C0 - General > C01 - Econometrics; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C55 - Large Data Sets: Modeling and Analysis; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C87 - Econometric Software; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C88 - Other Computer Software; C - Mathematical and Quantitative Methods > C9 - Design of Experiments > C99 - Other; J - Labor and Demographic Economics > J2 - Demand and Supply of Labor > J24 - Human Capital ; Skills ; Occupational Choice ; Labor Productivity; O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O31 - Innovation and Invention: Processes and Incentives; Y - Miscellaneous Categories > Y2 - Introductory Material > Y20 - Introductory Material

References

Hayashi Chikio. 1998. What is Data Science? Fundamental Concepts and a Heuristic Example. In: Hayashi Chikio, Yajima K., Bock HH., Ohsumi N., Tanaka Y., Baba Y. (eds) Data Science, Classification, and Related Methods. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Tokyo

Manyika James. 2008. Interview with Hal Varian. Transcript in McKinsey & Company.2009. Hal Varian on how the Web challenges managers. https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/hal-varian-on-how-the-web-challenges-managers (accessed September 14, 2020)