The final eight columns of the table provide information about the computational data practices taught in each course using Jupyter notebooks, identified through examination of resources such as course syllabi, available teaching material, and instructor commentary. Beyond "Programming Language Basics", the remaining seven technical practice would be considered part of the wider professional Data Scientist skillset.
From the available resources the students receive instruction on applying these transferable technical tools and methods to diverse data to explore topics relevant to the field which the course/s belongs. This includes Economics, Political Science, Cognitive Science/Psychology, Cultural Analytics, Sociology, History, Legal Studies, Ethnics Studies, Geography, Linguistics, and Visual Art.
web scraping and hip hop albums.
From this table alone we of course cannot infer whether using Jupyter notebooks as opposed to an alternative tool has enabled these powerful and sophisticated technical capabilities to be taught and learnt, nor in fact if these technical content were successfully taught and learnt at all!
However it is useful context against which to review first-hand user experiences made available by some of the instructors and students of these interdisciplinary courses in the next section to gain insight of the exact pedagogical value they attribute to Jupyter notebooks being used as a core instructional tool.