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A Data Science Major: Building Skills and Confidence

Authors:

Stephanie Rosenthal,

Tingting (Rachel) ChungAuthors Info & Claims

SIGCSE '20: Proceedings of the 51st ACM Technical Symposium on Computer Science Education

Pages 178 - 184

https://doi.org/10.1145/3328778.3366791

Published: 26 February 2020 Publication History

Abstract

Data science is a growing field at the intersection of mathematics, computer science, and domain expertise. Like many universities that are building data science degree programs for undergraduates, our small, liberal-arts university saw increasing opportunities in the region and decided to build a data science degree from the ground up, without a pre-existing computer science (CS) department to leverage for courses or culture. We designed and implemented an academically-demanding curriculum that combined mathematics, information systems, and new data science courses, and that also encouraged and supported student success. Each introductory course included active learning design to engage students. To increase retention, all major courses included assignments designed to build skills but also student confidence in their ability to learn challenging technical topics. Outside of the classroom, we created opportunities for professional advancement and developed a technical culture at the university. We will share our approach, course highlights, and lessons learned from building such a curriculum at an institution without a CS department.

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Cited By

Brown NXie BSarder EFiesler CWiese E(2023)Teaching Ethics in Computing: A Systematic Literature Review of ACM Computer Science Education PublicationsACM Transactions on Computing Education10.1145/363468524:1(1-36)Online publication date: 27-Nov-2023
https://dl.acm.org/doi/10.1145/3634685
Burridge JFekete ABecker BQuille KLaakso MBarendsen ESimon (2022)Teaching Programming for First-Year Data ScienceProceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 110.1145/3502718.3524740(297-303)Online publication date: 7-Jul-2022
https://dl.acm.org/doi/10.1145/3502718.3524740
Lau SNolan DGonzalez JGuo PMerkle LDoyle MSheard JSoh LDorn B(2022)How Computer Science and Statistics Instructors Approach Data Science Pedagogy DifferentlyProceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 110.1145/3478431.3499384(29-35)Online publication date: 22-Feb-2022
https://dl.acm.org/doi/10.1145/3478431.3499384
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Index Terms

A Data Science Major: Building Skills and Confidence
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education
      2. Model curricula

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Information

Published In

cover image ACM Conferences

SIGCSE '20: Proceedings of the 51st ACM Technical Symposium on Computer Science Education

February 2020

1502 pages

ISBN:9781450367936

DOI:10.1145/3328778

General Chairs:
Jian Zhang
Texas Woman's University, USA
,
Mark Sherriff
University of Virginia, USA
,
Program Chairs:
Sarah Heckman
North Carolina State University, USA
,
Pamela Cutter
Kalamazoo College, USA
,
Alvaro Monge
California State University, Long Beach, USA

Copyright © 2020 ACM.

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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 February 2020

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SIGCSE '20: The 51st ACM Technical Symposium on Computer Science Education

March 11 - 14, 2020

OR, Portland, USA

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Overall Acceptance Rate 1,595 of 4,542 submissions, 35%

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Cited By

Brown NXie BSarder EFiesler CWiese E(2023)Teaching Ethics in Computing: A Systematic Literature Review of ACM Computer Science Education PublicationsACM Transactions on Computing Education10.1145/363468524:1(1-36)Online publication date: 27-Nov-2023
https://dl.acm.org/doi/10.1145/3634685
Burridge JFekete ABecker BQuille KLaakso MBarendsen ESimon (2022)Teaching Programming for First-Year Data ScienceProceedings of the 27th ACM Conference on on Innovation and Technology in Computer Science Education Vol. 110.1145/3502718.3524740(297-303)Online publication date: 7-Jul-2022
https://dl.acm.org/doi/10.1145/3502718.3524740
Lau SNolan DGonzalez JGuo PMerkle LDoyle MSheard JSoh LDorn B(2022)How Computer Science and Statistics Instructors Approach Data Science Pedagogy DifferentlyProceedings of the 53rd ACM Technical Symposium on Computer Science Education - Volume 110.1145/3478431.3499384(29-35)Online publication date: 22-Feb-2022
https://dl.acm.org/doi/10.1145/3478431.3499384
Barman AChen SChang AAllen G(2022)Experiential Learning in Data Science Through a Novel Client-Facing Consulting Course2022 IEEE Frontiers in Education Conference (FIE)10.1109/FIE56618.2022.9962532(1-9)Online publication date: 8-Oct-2022
https://doi.org/10.1109/FIE56618.2022.9962532
Kharlamova GStavytskyy AKomendant O(2022)Aligning Higher Education in Ukraine with the Demands for Data Science WorkforceICTERI 2021 Workshops10.1007/978-3-031-14841-5_7(97-111)Online publication date: 14-Sep-2022
https://doi.org/10.1007/978-3-031-14841-5_7
Xiao TGreenberg RAlbert MSchulte CBecker BDivitini MBarendsen E(2021)Design and Assessment of a Task-Driven Introductory Data Science Course Taught Concurrently in Multiple Languages: Python, R, and MATLABProceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education V. 110.1145/3430665.3456364(290-295)Online publication date: 26-Jun-2021
https://dl.acm.org/doi/10.1145/3430665.3456364
Wu WSherriff MMerkle LCutter PMonge ASheard J(2021)SQL2XProceedings of the 52nd ACM Technical Symposium on Computer Science Education10.1145/3408877.3432541(590-596)Online publication date: 3-Mar-2021
https://dl.acm.org/doi/10.1145/3408877.3432541
Allen GSherriff MMerkle LCutter PMonge ASheard J(2021)Experiential Learning in Data Science: Developing an Interdisciplinary, Client-Sponsored Capstone ProgramProceedings of the 52nd ACM Technical Symposium on Computer Science Education10.1145/3408877.3432536(516-522)Online publication date: 3-Mar-2021
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Fekete AKay JRöhm USherriff MMerkle LCutter PMonge ASheard J(2021)A Data-centric Computing Curriculum for a Data Science MajorProceedings of the 52nd ACM Technical Symposium on Computer Science Education10.1145/3408877.3432457(865-871)Online publication date: 3-Mar-2021
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Li FXiao ZNg JHu X(2021)Exploring Interdisciplinary Data Science Education for Undergraduates: Preliminary ResultsDiversity, Divergence, Dialogue10.1007/978-3-030-71292-1_43(551-561)Online publication date: 17-Mar-2021
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