Data science education in undergraduate physics: Lessons learned from a community of practice

Author:

Shah Karan1ORCID,Butler Julie2ORCID,Knaub Alexis V.34ORCID,Zenginoğlu Anıl4ORCID,Ratcliff William567ORCID,Soltanieh-ha Mohammad8ORCID

Affiliation:

1. Center for Advanced Systems Understanding, Helmholtz-Zentrum Dresden-Rossendorf , Görlitz, Germany

2. Department of Biochemistry, Chemistry, and Physics, University of Mount Union , Alliance, Ohio 44601

3. American Association of Physics Teachers , College Park, Maryland 20740

4. Institute for Physical Science and Technology, University of Maryland , College Park, Maryland 20742

5. NIST Center for Neutron Research, National Institute of Standards and Technology , Gaithersburg, Maryland 20899

6. Department of Physics, University of Maryland , College Park, Maryland 20742

7. Department of Materials Science and Engineering, University of Maryland , College Park, Maryland 20742

8. Information Systems Department, Boston University, Boston, Massachusetts 02215

Abstract

It is becoming increasingly important that physics educators equip their students with the skills to work with data effectively. However, many educators may lack the necessary training and expertise in data science to teach these skills. To address this gap, we created the Data Science Education Community of Practice (DSECOP), bringing together graduate students and physics educators from different institutions and backgrounds to share best practices and lessons learned from integrating data science into undergraduate physics education. In this article, we present insight and experiences from this community of practice, highlighting key strategies and challenges in incorporating data science into the introductory physics curriculum. Our goal is to provide guidance and inspiration to educators who seek to integrate data science into their teaching, helping to prepare the next generation of physicists for a data-driven world.

Funder

American Physical Society

National Science Foundation

Publisher

American Association of Physics Teachers (AAPT)

Reference27 articles.

1. Data science and its relationship to big data and data-driven decision making;Big data,2013

2. Rafael C. Alvarado , “ Data Science from 1963 to 2012,” arXiv:2311.03292 (2023).

3. The role of academia in data science education;Harvard Data Sci. Rev.,2020

4. NIST Big Data Public Working Group Definitions and Taxonomies Subgroup, “ NIST Big Data interoperability framework: Volume 1, definitions,” Technical Report No. NIST SP 1500-1 ( National Institute of Standards and Technology, 2015).

5. Undergraduate data science degrees emphasize computer science and statistics but fall short in ethics training and domain-specific context;PeerJ Comput. Sci.,2021

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