abstract

Data Science Education: Global Perspectives and Convergence

Authors:

Teresa Susana Mendes Pereira,

Lovisa SundinAuthors Info & Claims

ITiCSE '19: Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education

Pages 265 - 266

https://doi.org/10.1145/3304221.3325533

Published: 02 July 2019 Publication History

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Abstract

Over the past two decades, data science or data analytics degree programs have begun to emerge, reflecting the world's demand for data specialists to make sense of the vast amounts of collected data in the sciences, engineering, business, and other domains. As degree creation has occurred mainly due to demand, ACM and other professional bodies have recently stepped in to provide curricular guidance. However, no \em shared global framework for data science as an academic discipline exists, making growth unfocused and driven by employer demands. More recently, the growth of artificial intelligence has also impacted data science programs. This working group builds on prior efforts and participant experiences to develop a global taxonomy of approaches to data science education and expectations for graduates of data science programs to \em think like data scientists.

References

[1]

Boots Cassel and Heikki Topi. 2015. Strengthening Data Science Education Through Collaboration . Workshop on Data Science Education, October 2015, Workshop Report. https://goo.gl/F5xEkj

Google Scholar

[2]

Andrea Danyluk, Paul Leidig, Lillian Cassel, and Christian Servin. 2019. ACM Task Force on Data Science: Draft Report and Opportunity for Feedback. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (SIGCSE '19). ACM, New York, NY, USA, 2.

Digital Library

Google Scholar

[3]

Richard D. De Veaux, Mahesh Agarwal, Maia Averett, Benjamin S. Baumer, Andrew Bray, Thomas C. Bressoud, Lance Bryant, Lei Z. Cheng, Amanda Francis, Robert Gould, Albert Y. Kim, Matt Kretchmar, Qin Lu, Ann Moskol, Deborah Nolan, Roberto Pelayo, Sean Raleigh, Ricky J. Sethi, Mutiara Sondjaja, Neelesh Tiruviluamala, Paul X. Uhlig, Talitha M. Washington, Curtis L. Wesley, David White, and Ping Ye. 2017. Curriculum Guidelines for Undergraduate Programs in Data Science. Annual Review of Statistics and Its Application, Vol. 4, 1 (2017), 15--30.

Crossref

Google Scholar

[4]

EDISON Consortium. 2017a. EDISON Data Science Framework (EDSF). Technical Report. EDISON Initiative, Europe. http://edison-project.eu/book/export/html/488

Google Scholar

[5]

EDISON Consortium. 2017b. EDISON University Programs List. Technical Report. EDISON Initiative, Europe. http://edison-project.eu/university-programs-list

Google Scholar

[6]

Tony Hey, Stewart Tansley, and Kristin M. Tolle (Eds.). 2009. The Fourth Paradigm: Data-Intensive Scientific Discovery .Microsoft Research, Redmond, Washington. http://research.microsoft.com/en-us/collaboration/fourthparadigm/

Google Scholar

[7]

National Academies of Sciences, Engineering, and Medicine. 2018. Data Science for Undergraduates: Opportunities and Options .The National Academies Press, Washington, DC.

Google Scholar

[8]

John W. Tukey. 1962. The Future of Data Analysis. The Annals of Mathematical Statistics, Vol. 33, 1 (03 1962), 1--67.

Google Scholar

[9]

Nanyang Technological University. 2018. Bachelor of Science in Data Science and Artificial Intelligence . Brochure. http://scse.ntu.edu.sg/Programmes/CurrentStudents/Undergraduate/Documents/2018/DSAIbrochure.pdf

Google Scholar

[10]

Jeannette M. Wing, Vandana P. Janeja, Tyler Kloefkorn, and Lucy C. Erickson. 2018. Data Science Leadership Summit: Summary Report . Technical Report. Data Science Institute, USA. https://dl.acm.org/citation.cfm?id=3293458

Digital Library

Google Scholar

Cited By

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Hazzan OMike K(2023)Data Science Teacher Preparation: Implementation of the TPACK FrameworkACM Inroads10.1145/361410014:3(39-44)Online publication date: 16-Aug-2023
https://dl.acm.org/doi/10.1145/3614100
Poole A(2023) Data Flourishing: Developing Human‐Centered Data Science through Communities of Ethical Practice Proceedings of the Association for Information Science and Technology10.1002/pra2.79360:1(338-352)Online publication date: 22-Oct-2023
https://doi.org/10.1002/pra2.793
Zakaria M(2022)Data science education programmes in Middle Eastern institutions: A survey studyIFLA Journal10.1177/0340035222111336249:1(157-179)Online publication date: 4-Aug-2022
https://doi.org/10.1177/03400352221113362
Show More Cited By

Index Terms

Data Science Education: Global Perspectives and Convergence
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
      2. Model curricula

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Published In

ITiCSE '19: Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education

July 2019

583 pages

ISBN:9781450368957

DOI:10.1145/3304221

General Chairs:
Bruce Scharlau
University of Aberdeen, UK
,
Roger McDermott
Robert Gordon University, UK
,
Program Chairs:
Arnold Pears
KTH Royal Institute of Technology, Sweden
,
Mihaela Sabin
University of New Hampshire, USA

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 02 July 2019

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July 15 - 17, 2019

Aberdeen, Scotland Uk

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Overall Acceptance Rate 552 of 1,613 submissions, 34%

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Hazzan OMike K(2023)Data Science Teacher Preparation: Implementation of the TPACK FrameworkACM Inroads10.1145/361410014:3(39-44)Online publication date: 16-Aug-2023
https://dl.acm.org/doi/10.1145/3614100
Poole A(2023) Data Flourishing: Developing Human‐Centered Data Science through Communities of Ethical Practice Proceedings of the Association for Information Science and Technology10.1002/pra2.79360:1(338-352)Online publication date: 22-Oct-2023
https://doi.org/10.1002/pra2.793
Zakaria M(2022)Data science education programmes in Middle Eastern institutions: A survey studyIFLA Journal10.1177/0340035222111336249:1(157-179)Online publication date: 4-Aug-2022
https://doi.org/10.1177/03400352221113362
Mike KHazzan O(2022)Interdisciplinary CS1 Course for Non-Majors: The Case of Graduate Psychology Students2022 IEEE Global Engineering Education Conference (EDUCON)10.1109/EDUCON52537.2022.9766516(86-93)Online publication date: 28-Mar-2022
https://doi.org/10.1109/EDUCON52537.2022.9766516
Wu DXu HSun YLv S(2022) What should we teach? A h uman‐centered data science graduate curriculum model design for iField schools Journal of the Association for Information Science and Technology10.1002/asi.24644Online publication date: 28-Mar-2022
https://doi.org/10.1002/asi.24644
Poole A(2021) LEADING the Way: A New Model for Data Science Education Proceedings of the Association for Information Science and Technology10.1002/pra2.49158:1(525-531)Online publication date: 13-Oct-2021
https://doi.org/10.1002/pra2.491
Mike KHazan THazzan O(2020)Equalizing Data Science Curriculum for Computer Science PupilsProceedings of the 20th Koli Calling International Conference on Computing Education Research10.1145/3428029.3428045(1-5)Online publication date: 19-Nov-2020
https://dl.acm.org/doi/10.1145/3428029.3428045

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Data Science Education: Curriculum and pedagogy

An Empirical Approach to Understanding Data Science and Engineering Education

Professional Accreditation and Competency-Based Computing Education

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