Generative AI for Data Science 101: Coding Without Learning to Code

• Published in: Journal of statistics and data science education Vol. 33; no. 2; pp. 129 - 142
• Main Authors: Bien, Jacob, Mukherjee, Gourab
• Format: Journal Article
• Language: English
• Published: Alexandria Taylor & Francis Ltd 03.04.2025; Taylor & Francis Group
• Subjects: Artificial Intelligence; Chatbots; Coding; Computer Software; Data science; Generalized linear models; Generative artificial intelligence; GitHub Copilot; Housing; Introduction to data science; Introductory Courses; Language; Large language models; Learning; MBA programs & graduates; Nonmajors; Regression (Statistics); Regression analysis; Science Curriculum; Science education; Science Instruction; Software; Statistical Data; Statistics Education; Students; Syntax; Teaching; Teaching Models
• ISSN: 2693-9169; 2693-9169
• DOI: 10.1080/26939169.2024.2432397

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.