Open AccessTeaching on Jupyter
Author(s) -
Jonathan Reades
Publication year - 2020
Publication title -
region
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.406
H-Index - 7
ISSN - 2409-5370
DOI - 10.18335/region.v7i1.282
Subject(s) - python (programming language) , interactivity , computer science , popularity , suite , maintainability , data science , geospatial analysis , analytics , flexibility (engineering) , software engineering , world wide web , geography , programming language , cartography , mathematics , psychology , social psychology , statistics , archaeology
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.