research-article

Open access

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.

References

[1]

Reni A Abraham, John R Slate, D Patrick Saxon, and Wally Barnes. 2014. College-readiness in math: A conceptual analysis of the literature. Research & Teaching in Developmental Education, Vol. 30, 2 (2014), 4.

[2]

Daniel S. Alexander and Bradley Meyer. 2016. Equipping Liberal Arts Students with Skills in Data Analytics. (2016). www.bhef.com/sites/default/files/BHEF_2016_DSA_Liberal_Arts.pdf

[3]

Matthew C Atherton. 2014. Academic preparedness of first-generation college students: Different perspectives. Journal of College Student Development, Vol. 55, 8 (2014), 824--829.

[4]

Patricia B Campbell, Eric J Jolly, and Lesley Perlman. 2007. Introducing the trilogy of success: Examining the role of engagement, capacity and continuity in women's STEM choices. Women in Engineering ProActive Network (2007).

[5]

Price Waterhouse Cooper and Business-Higher Education Forum. 2017. Investing in America's data science and analytics talent - the case for action. (2017). www.pwc.com/us/dsa-skills

[6]

Yuri Demchenko, A. Belloum, and Tomasz Wiktorski. 2016. Edison Data Science Framework: Part 1. Data Science Competence Framework (CF-DS) Release 1. (01 2016). https://doi.org/10.5281/zenodo.167585

[7]

Carol S Dweck. 2008. Mindset: The new psychology of success .Random House Digital, Inc.

[8]

ACM Data Science Task Force. 2019. Computing Competencies for Undergraduate Data Science Curricula. (2019). http://www.cs.williams.edu/ andrea/DSReportInitialFull.pdf

[9]

Business-Higher Education Forum. 2016. Competency Map for the Data Science and Analytics-Enabled Graduate. (2016). www.bhef.com/news-events/events/webinar-data-science-and-analytics-dsa-enabled-graduate-competency-map

[10]

Geoffrey Fox and Wo Chang (Eds.). 2019. NIST Big Data Interoperability Framework: Volume 3, Use Cases and General Requirements. NIST Big Data Public Working Group (2019). https://doi.org/10.6028/NIST.SP.1500--3r2

[11]

Catherine Hill, Christianne Corbett, and Andresse St Rose. 2010. Why so few? Women in science, technology, engineering, and mathematics. ERIC.

[12]

Trudy Howles. 2007. Preliminary results of a longitudinal study of computer science student trends, behaviors and preferences. Journal of Computing Sciences in Colleges, Vol. 22, 6 (2007), 18--27.

Digital Library

[13]

E.J. Jolly, P.B. Campbell, and L. Perlman. 2004. Engagement, capacity and continuity: a trilogy for student success. (2004). www.campbell-kibler.com

[14]

E. J. Jolly and P. Campbell. 2014. Ten years of engagement, capacity and continuity: Reflections on a trilogy for student success. (2014). www.campbell-kibler.com/trilogy.pdf

[15]

Sean Kandel, Andreas Paepcke, Joseph M Hellerstein, and Jeffrey Heer. 2012. Enterprise data analysis and visualization: An interview study. IEEE Transactions on Visualization and Computer Graphics, Vol. 18, 12 (2012), 2917--2926.

Digital Library

[16]

Paul H Kvam. 2000. The effect of active learning methods on student retention in engineering statistics. The American Statistician, Vol. 54, 2 (2000), 136--140.

[17]

Jeffrey J McConnell. 1996. Active learning and its use in computer science. ACM SIGCSE Bulletin, Vol. 28, SI (1996), 52--54.

Digital Library

[18]

Susan Staffin Metz. 2007. Attracting the engineers of 2020 today. Women and minorities in science, technology, engineering, and mathematics: Upping the numbers (2007), 184--209.

[19]

Michael Chui James Manyika Tamim Saleh Bill Wiseman Nicolaus Henke, Jacques Bughin and Guru Sethupathy. 2016. The Age of Analytics: Competing in a Data-Driven World. (2016).

[20]

Joan Peckham, Lisa L Harlow, David A Stuart, Barbara Silver, Helen Mederer, and Peter D Stephenson. 2007. Broadening participation in computing: issues and challenges. In ACM SIGCSE Bulletin, Vol. 39. ACM, 9--13.

Digital Library

[21]

Bina Ramamurthy. 2016. A Practical and Sustainable Model for Learning and Teaching Data Science. In Proceedings of the 47th ACM Technical Symposium on Computing Science Education (SIGCSE '16). ACM, New York, NY, USA, 169--174. https://doi.org/10.1145/2839509.2844603

Digital Library

[22]

Eric S. Roberts, Marina Kassianidou, and Lilly Irani. 2002. Encouraging Women in Computer Science. SIGCSE Bull., Vol. 34, 2 (June 2002), 84--88. https://doi.org/10.1145/543812.543837

Digital Library

[23]

Felesia Stukes, Hang Chen, and Terik Tidwell. 2018. Applying the Engagement, Capacity and Continuity Trilogy for Computing Undergraduates at Johnson C. Smith University. In 2018 Research on Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT). IEEE, 1--4.

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
Show More Cited By

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

Recommendations

Data Science Curriculum Design: A Case Study
SIGCSE '21: Proceedings of the 52nd ACM Technical Symposium on Computer Science Education

There is an increasing demand for data scientists in the current job market. Hence, many two-year and four-year colleges and universities started to offer Data Science degrees in the recent decade. In this paper, we describe an undergraduate Data ...
Building Out Data Science at Small Colleges: (Abstract Only)
SIGCSE '18: Proceedings of the 49th ACM Technical Symposium on Computer Science Education

Abstract: Data science is on the rise, being a high-demand skill for a number of employers, both in the tech field and in various scientific disciplines. Since it is a new field, only a handful of schools offer a data science program, most of those ...
Integrating communication skills into the computer science curriculum
SIGCSE '12: Proceedings of the 43rd ACM technical symposium on Computer Science Education

Computer Science majors must be able to communicate effectively. Industry surveys identify the development of communication and critical thinking skills as key to the reform of the higher education sector. However, academics are challenged by time and ...

Comments

Information & Contributors

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].

Sponsors

SIGCSE: ACM Special Interest Group on Computer Science Education

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 February 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SIGCSE '20

Sponsor:

SIGCSE

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

March 11 - 14, 2020

OR, Portland, USA

Acceptance Rates

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

Upcoming Conference

SIGCSE Virtual 2024

Sponsor:
sigcse

1st ACM Virtual Global Computing Education Conference

December 5 - 8, 2024

Virtual Event , NC , USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
664
Total Downloads

Downloads (Last 12 months)214
Downloads (Last 6 weeks)20

Reflects downloads up to 11 Aug 2024

Other Metrics

View Author Metrics

Citations

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
https://dl.acm.org/doi/10.1145/3408877.3432536
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
https://dl.acm.org/doi/10.1145/3408877.3432457
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
https://dl.acm.org/doi/10.1007/978-3-030-71292-1_43
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents