Content area

Abstract

Should one teach coding in a required introductory statistics and data science class for non-major students? Many professors advise against it, considering it a distraction from the important and challenging statistical topics that need to be covered. By contrast, other professors argue that the ability to interact flexibly with data will inspire students with a lasting love of the subject and a continued commitment to the material beyond the introductory course. With the release of large language models that write code, we saw an opportunity for a middle ground, which we tried in Fall 2023 in a required introductory data science course in our school’s full-time MBA program. We taught students how to write English prompts to the artificial intelligence tool GitHub Copilot that could be turned into R code and executed. In this short article, we report on our experience using this new approach.

Details

Title
Generative AI for Data Science 101: Coding Without Learning to Code
Author
Bien, Jacob 1 ; Mukherjee, Gourab 1 

 Data Sciences and Operations, Marshall School of Business, University of Southern California, Los Angeles, CA 
Volume
33
Issue
2
Pages
129-142
Publication year
2025
Publication date
2025
Publisher
Taylor & Francis Ltd.
Place of publication
Alexandria
Country of publication
United Kingdom
e-ISSN
2693-9169
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
ProQuest document ID
3184854149
Document URL
https://www.proquest.com/scholarly-journals/generative-ai-data-science-101-coding-without/docview/3184854149/se-2?accountid=208611
Copyright
© 2025 The Author(s). Published with license by Taylor & Francis Group, LLC. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-07-15