Imaxe de Portada

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

Gardado en:
Publicado en:Journal of Statistics and Data Science Education vol. 33, no. 2 (2025), p. 129
Autor Principal: Bien, Jacob
Outros autores: Mukherjee, Gourab
Publicado:
Taylor & Francis Ltd.
Materias:
Teaching
Language
Software
Students
Regression analysis
Syntax
Science education
Generalized linear models
Data science
Large language models
Generative artificial intelligence
Learning
Chatbots
MBA programs & graduates
Housing
Nonmajors
Statistics Education
Coding
Teaching Models
Artificial Intelligence
Science Instruction
Statistical Data
Science Curriculum
Computer Software
Introductory Courses
Regression (Statistics)
Acceso en liña:Citation/Abstract
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Resumo: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.
ISSN:2693-9169
1069-1898
DOI:10.1080/26939169.2024.2432397
Fonte:Research Library