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Connecting the Dots: A Programmatic Approach to Data Science within Engineering

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Conference

2023 ASEE Annual Conference & Exposition

Location

Baltimore , Maryland

Publication Date

June 25, 2023

Start Date

June 25, 2023

End Date

June 28, 2023

Conference Session

Multidisciplinary Engineering Division (MULTI) Technical Session 7

Tagged Division

Multidisciplinary Engineering Division (MULTI)

Tagged Topic

Diversity

Page Count

14

DOI

10.18260/1-2--42746

Permanent URL

https://peer.asee.org/42746

Download Count

197

Paper Authors

biography

Kristen Moore University at Buffalo, The State University of New York

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Kristen R. Moore is an Associate Professor in the Department of Engineering Education at University at Buffalo. Her research focuses primarily on technical communication and issues of equity, inclusion, and social justice.

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biography

Liesl Folks The University of Arizona

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Liesl Folks holds a BSc(Hons) and a PhD, both in Physics, from The University of
Western Australia. Her research interests are in spin electronic devices for logic and data storage.

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biography

Erin Rowley University at Buffalo, The State University of New York Orcid 16x16 orcid.org/0000-0001-5789-6790

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Erin Rowley is the Head of Science and Engineering Library Services at the University at Buffalo and serves as the Engineering Librarian. Her research interests include the use of technical standards in engineering education, the role of the librarian in entrepreneurial information literacy, and collaboration between business and engineering librarians in academia.

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Abstract

The importance of “data acumen” for STEM students has been well-articulated by scholars and industry professionals—in part because data science infiltrates many areas of engineering and science. Yet within engineering programs, students often have few opportunities to develop expertise in data science or even to explore how data science is relevant to their degree specializations. This paper reports on an NSF-funded study of a program that prepares STEM students to engage with data science in coursework and then mentors them as they secure internships and complete a capstone that demonstrates their application of data science expertise. Drawing on a mixed-methods study, including student reflections, capstone project assessment, and survey reporting, this paper suggests not only that students make deep connections between their existing majors and data science but also that students trained in our data science micro-credential have unique opportunities to improve critical super-skills, including written communication, project management, iterative thinking, and real-world problem-solving.

Moore, K., & Folks, L., & Rowley, E. (2023, June), Connecting the Dots: A Programmatic Approach to Data Science within Engineering Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--42746

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