Helping Data Science Students Develop Task Modularity
- jeff saltz, syracuse university, Syracuse, New York, United States
- Robert Heckman, Syracuse University, Syracuse, New York, United States
- Kevin Crowston, School of Information Studies, Syracuse University, Syracuse, New York, United States
- Sangseok You, HEC Paris, Paris, France
- Yatish Hegde, School of Information Studies, Syracuse University, Syracuse, New York, United States
AbstractThis paper explores the skills needed to be a data scientist. Specifically, we report on a mixed method study of a project-based data science class, where we evaluated student effectiveness with respect to dividing a project into appropriately sized modular tasks, which we termed task modularity. Our results suggest that while data science students can appreciate the value of task modularity, they struggle to achieve effective task modularity. As a first step, based our study, we identified six task decomposition best practices. However, these best practices do not fully address this gap of how to enable data science students to effectively use task modularity. We note that while computer science/information system programs typically teach modularity (e.g., the decomposition process and abstraction), and there remains a need identify a corresponding model to that used for computer science / information system students, to teach modularity to data science students.
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