Teaching on Jupyter

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.