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Teaching on Jupyter: Using notebooks to accelerate learning and curriculum development

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  • Jonathan Reades

Abstract

The proliferation of large, complex data spatial data sets presents challenges to the way that regional science --- and geography more widely -- is researched and taught. Increasingly, it is not 'just' quantitative skills that are needed, but computational ones. However, the majority of undergraduate programmes have yet to offer much more than a one-off ‘GIS programming’ class since such courses are seen as challenging not only for students to take, but for staff to deliver. Using evaluation criterion of minimal complexity, maximal flexibility, interactivity, utility, and maintainability, we show how the technical features of Jupyter notebooks -- particularly when combined with the popularity of Anaconda Python and Docker -- enabled us to develop and deliver a suite of three 'geocomputation' modules to Geography undergraduates, with some progressing to data science and analytics roles.

Suggested Citation

  • Jonathan Reades, 2020. "Teaching on Jupyter: Using notebooks to accelerate learning and curriculum development," REGION, European Regional Science Association, vol. 7, pages 21-34.
    • Handle: RePEc:wiw:wiwreg:region_7_1_282
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    File URL: https://openjournals.wu.ac.at/ojs/index.php/region/article/view/282/version/549
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    References listed on IDEAS

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    1. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 2.
    2. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 4.
    3. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 3.
    4. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 1.
    5. David O’Sullivan & Steven M. Manson, 2015. "Do Physicists Have Geography Envy? And What Can Geographers Learn from It?," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 105(4), pages 704-722, July.
    6. Jonathan Reades & Jordan De Souza & Phil Hubbard, 2019. "Understanding urban gentrification through machine learning," Urban Studies, Urban Studies Journal Limited, vol. 56(5), pages 922-942, April.
    7. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 4.
    8. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 3.
    9. Editorial Article, 0. "The Information for Authors," Economics of Contemporary Russia, Regional Public Organization for Assistance to the Development of Institutions of the Department of Economics of the Russian Academy of Sciences, issue 2.
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    Cited by:

    1. Francisco Rowe & Gunther Maier & Daniel Arribas-Bel & Sergio Rey, 2020. "The Potential of Notebooks for Scientific Publication, Reproducibility and Dissemination," REGION, European Regional Science Association, vol. 7, pages 1-5.

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