Generative AI for Data Science 101: Coding Without Learning to Code
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| Veröffentlicht in: | Journal of Statistics and Data Science Education vol. 33, no. 2 (2025), p. 129 |
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Taylor & Francis Ltd.
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| 024 | 7 | |a 10.1080/26939169.2024.2432397 |2 doi | |
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| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Bien, Jacob |u Data Sciences and Operations, Marshall School of Business, University of Southern California, Los Angeles, CA | |
| 245 | 1 | |a Generative AI for Data Science 101: Coding Without Learning to Code | |
| 260 | |b Taylor & Francis Ltd. |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
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| 700 | 1 | |a Mukherjee, Gourab |u Data Sciences and Operations, Marshall School of Business, University of Southern California, Los Angeles, CA | |
| 773 | 0 | |t Journal of Statistics and Data Science Education |g vol. 33, no. 2 (2025), p. 129 | |
| 786 | 0 | |d ProQuest |t Research Library | |
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| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3184854149/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |