Helping Data Science Students Develop Task Modularity

Jeffrey Saltz, Robert Heckman, Kevin G Crowston, Sangseok You, Yatish Hegde

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

Abstract

This 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.
Original languageEnglish (US)
Title of host publicationProceedings of the 52nd Hawaii International Conference on System Sciences
Volume52
StatePublished - Jan 2019

Fingerprint Dive into the research topics of 'Helping Data Science Students Develop Task Modularity'. Together they form a unique fingerprint.

  • Cite this

    Saltz, J., Heckman, R., Crowston, K. G., You, S., & Hegde, Y. (2019). Helping Data Science Students Develop Task Modularity. In Proceedings of the 52nd Hawaii International Conference on System Sciences (Vol. 52) http://hdl.handle.net/10125/59549