The Foundations of Spatial Data Science (FSDS) module is part of CASA's MSc degree offering builds on a step-change in our ability to work with large spatial data sets. The design of the module is informed by the dearth of planners and geographers able to think computationally using programming, data analysis, and data manipulation skills. There is a severe skills shortage in this domain across all sectors: non-profit, government, corporate, and academic.
The module's objective is to enable students to access, understand, utilise, visualise and communicate data in a spatial context. FSDS is not about pushing buttons, but about using logic, programming, and analytical skills to tackle complex real-world problems in a creative, reproducible, and open manner.
In order to make use of these materials you will need to download and install the Spatial Data Science computing environment.
- Shift away from .values
- Swap Weeks 8 (Visualising Data) and 9 (Dimensions in Data) + explain rationale to students.
- Shift focus of (new) Week 9 & 10 to review/discussing the final assessment. Content still available to view/practice but not required/delivered.
- Require more active use of GitHub/Git — a submission in which it’s easy to automate checks that they’ve created a repo and populated it with the completed notebooks?
- Refresh Code Camp and make it more self-test oriented. Use the self-test to set expectations about level of effort/preparation required (and whether or not taking the module will be useful).
- Standardise delivery of practical content by TAs: should have consistent approach across practicals.
- Point to cross-module content/recaps.
- Joint reading list?
- Investigate CoCalc as an alternative. What is situation with JupyterHub at UCL.