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Data Science Pedagogy to Support Industry, Governmental, and Research Initiatives

Published: 01 December 2022 Publication History

Abstract

Data Science practices are increasingly leveraged in disparate domains of research, whether as part of industry workflows, governmental department initiatives, or open problems within academic communities. Herein, we describe designing term-projects to introduce senior undergraduate students to applied Data Science research for industry, governmental, or academic "clients" through a series of course assignments and client meetings. We outline the lessons learned and describe how they may be adapted within similar courses. Students are familiarized with data science best practices, obtain applied research experience, and (potentially) professionally benefit from an actual research contribution in the form of a peer-reviewed conference publication; at time of writing, we have published three student-led projects in the proceedings of eminent peer-reviewed conferences. We highly recommend introducing undergraduate students to such client-serving research applications early in their program to encourage them to consider pursuing a research-focused career path.

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cover image ACM Conferences
SPLASH-E 2022: Proceedings of the 2022 ACM SIGPLAN International Symposium on SPLASH-E
November 2022
69 pages
ISBN:9781450399005
DOI:10.1145/3563767
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 01 December 2022

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Author Tags

  1. data science pedagogy
  2. experiential-based learning
  3. low-resource computing
  4. open-source
  5. research-centric coursework

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