Helping Data Science Students Develop Task Modularity

dc.contributor.author saltz, jeff
dc.contributor.author Heckman, Robert
dc.contributor.author Crowston, Kevin
dc.contributor.author You, Sangseok
dc.contributor.author Hegde, Yatish
dc.date.accessioned 2019-01-02T23:48:58Z
dc.date.available 2019-01-02T23:48:58Z
dc.date.issued 2019-01-08
dc.description.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.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2019.134
dc.identifier.isbn 978-0-9981331-2-6
dc.identifier.uri http://hdl.handle.net/10125/59549
dc.language.iso eng
dc.relation.ispartof Proceedings of the 52nd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Big Data and Analytics: Pathways to Maturity
dc.subject Decision Analytics, Mobile Services, and Service Science
dc.subject Data Science, Education, Modularity
dc.title Helping Data Science Students Develop Task Modularity
dc.type Conference Paper
dc.type.dcmi Text
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