A review of data science definitions and competencies in higher education
Bahar Memarian 1 * , Tenzin Doleck 1
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1 Simon Fraser University, Burnaby, BC, CANADA* Corresponding Author

Abstract

Data science is expanding as a discipline and profession, yet its conceptual and philosophical foundations—particularly within higher education—remain underexamined. This study addresses this gap through a systematic literature review of peer-reviewed publications indexed in Scopus and Web of Science, focusing on how data science is defined and how its competencies and learning outcomes are articulated. Drawing on formal conceptual analysis, we examine whether definitions are structured as extensional or intensional, and whether learning outcomes are framed as nouns or verb-noun pairs. Using an interpretive framework, we evaluate the quality, strengths, and weaknesses of reported data science definitions and learning outcomes in higher education contexts. Findings indicate that most data science definitions adopt an intensional structure, with clearer insight achieved when both category and differentia are explicitly specified. Learning outcomes are predominantly expressed as verb-noun pairs and are more meaningful when they emphasize adaptive, timeless skills. The review also highlights ongoing tensions between university-based programs and certificate offerings, questions surrounding curriculum design, accreditation, stakeholder involvement, and the evolving role of industry and artificial intelligence in shaping the field. Overall, this work provides conceptual understanding and critical insights into the benefits, challenges, and future implications for defining data science and designing robust, inclusive data science curricula in higher education.

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Review Article

Journal of Digital Educational Technology, Volume 6, Issue 2, October 2026, Article No: ep2611

https://doi.org/10.29333/jdet/18527

Publication date: 05 May 2026

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Article Downloads: 4

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