TY - Generic T1 - Helping data science students develop task modularity T2 - Proceedings of the 52nd Hawai'i International Conference on System Sciences (HICSS-52) Y1 - 2019 A1 - Jeffery Saltz A1 - Heckman, Robert A1 - Kevin Crowston A1 - Sangseok You A1 - Yatish Hegde KW - data science KW - modularity KW - Stigmergy AB -

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.

JF - Proceedings of the 52nd Hawai'i International Conference on System Sciences (HICSS-52) UR - http://hdl.handle.net/10125/59549 ER -