research-article

An Empirical Approach to Understanding Data Science and Engineering Education

Authors:

Rajendra K. Raj,

John Impagliazzo,

Carol J. Romanowski,

Casey C. Bennett,

Karen C. Davis,

Andrew McGettrick,

Teresa Susana Mendes Pereira,

Lovisa SundinAuthors Info & Claims

ITiCSE-WGR '19: Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education

Pages 73 - 87

https://doi.org/10.1145/3344429.3372503

Published: 18 December 2019 Publication History

Abstract

As data science is an evolving field, existing definitions reflect this uncertainty with overloaded terms and inconsistency. As a result of the field's fluidity, there is often a mismatch between what data-related programs teach, what employers expect, and the actual tasks data scientists are performing. In addition, the tools available to data scientists are not necessarily the tools being taught; textbooks do not seem to meet curricular needs; and empirical evidence does not seem to support existing program design. Currently, the field appears to be bifurcating into data science (DS) and data engineering (DE), with specific but overlapping roles in the combined data science and engineering (DSE) lifecycle. However, curriculum design has not yet caught up to this evolution. This working group report shows an empirical and data-driven view of the data-related education landscape, and includes several recommendations for both academia and industry that are based on this analysis.

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Saeed MSaeed RAhmed ZGaid AMokhtar R(2024)AI Technologies in Engineering EducationAI-Enhanced Teaching Methods10.4018/979-8-3693-2728-9.ch003(61-87)Online publication date: 14-Jun-2024
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Miedema DTaipalus TAjanovski VAlawini AGoodfellow MLiut MPeltsverger SYoung TMonga MLonati VBarendsen ESheard JPaterson J(2024)Curriculum Analysis for Data Systems EducationProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 210.1145/3649405.3659529(761-762)Online publication date: 8-Jul-2024
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Index Terms

An Empirical Approach to Understanding Data Science and Engineering Education
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
      2. Model curricula

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cover image ACM Conferences

ITiCSE-WGR '19: Proceedings of the Working Group Reports on Innovation and Technology in Computer Science Education

December 2019

218 pages

ISBN:9781450375672

DOI:10.1145/3344429

Co-chairs:
Guido Rößling
TU Darmstadt, Germany
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Norwegian University of Science and Technology, Norway
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University of Aberdeen, UK
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Roger McDermott
Robert Gordon University, UK
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KTH Royal Institute of Technology, Sweden
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Mihaela Sabin
University of New Hampshire, USA

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https://doi.org/10.4018/979-8-3693-2728-9.ch003
Miedema DTaipalus TAjanovski VAlawini AGoodfellow MLiut MPeltsverger SYoung TMonga MLonati VBarendsen ESheard JPaterson J(2024)Curriculum Analysis for Data Systems EducationProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 210.1145/3649405.3659529(761-762)Online publication date: 8-Jul-2024
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