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Interactive Computing for Accelerated Learning in Computation and Data Science

Author

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  • David L. Alderson

    (Operations Research Department, Naval Postgraduate School, Monterey, California 93943)

Abstract

This article describes the motivation and design for introductory coursework in computation aimed at midcareer professionals who desire to work in data science and analytics but who have little or no background in programming. In particular, we describe how we use modern interactive computing platforms to accelerate the learning of our students both in and out of the classroom. We emphasize the importance of organizing the interaction with course material so that students learn not only to “think computationally” but also to “do computationally.” We provide details of existing courses in computation offered at the Naval Postgraduate School, and we describe their ongoing evolution in response to increased demand from members of the civilian and military workforce.

Suggested Citation

  • David L. Alderson, 2022. "Interactive Computing for Accelerated Learning in Computation and Data Science," INFORMS Transactions on Education, INFORMS, vol. 22(2), pages 130-145, January.
    • Handle: RePEc:inm:orited:v:22:y:2022:i:2:p:130-145
    DOI: 10.1287/ited.2021.0261
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    References listed on IDEAS

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    1. Jill R. Hardin & Allen Holder & J. Christopher Beck & Kevin Furman & Arthur Hanna & David Rader & Cesar Rego, 2012. "Recommendations for an Undergraduate Curriculum at the Interface of Operations Research and Computer Science," INFORMS Transactions on Education, INFORMS, vol. 12(3), pages 117-123, May.
    2. Jeffrey M. Perkel, 2021. "Ten computer codes that transformed science," Nature, Nature, vol. 589(7842), pages 344-348, January.
    3. Jeffrey M. Perkel, 2018. "Why Jupyter is data scientists’ computational notebook of choice," Nature, Nature, vol. 563(7729), pages 145-146, November.
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