The module provides students with little or no prior knowledge core competences in Geographic Data Science (GDS). This includes the following:

  • Advancing their statistical and numerical literacy.

  • Introducing basic principles of programming and state-of-the-art computational tools for GDS.

  • Presenting a comprehensive overview of the main methodologies available to the Geographic Data Scientist, as well as their intuition as to how and when they can be applied.

  • Focusing on real world applications of these techniques in a geographical and applied context.

Learning outcomes

By the end of the course, students will be able to:

  • Demonstrate advanced GIS/GDS concepts and be able to use the tools programmatically to import, manipulate and analyse spatial data in different formats.

  • Understand the motivation and inner workings of the main methodological approcahes of GDS, both analytical and visual.

  • Critically evaluate the suitability of a specific technique, what it can offer and how it can help answer questions of interest.

  • Apply a number of spatial analysis techniques and explain how to interpret the results, in a process of turning data into information.

  • When faced with a new data-set, work independently using GIS/GDS tools programmatically to extract valuable insight.

Feedback strategy

The student will receive feedback through the following channels:

  • Formal assessment of three summative assignments: two tests and a computational essay. This will be on the form of reasoning of the mark assigned as well as comments specifying how the mark could be improved. This will be provided no later than three working weeks after the deadline of the assignment submission.

  • Direct interaction with Module Leader and demonstrators in the computer labs. This will take place in each of the scheduled lab sessions of the course.

  • Online forum maintained by the Module Leader where students can contribute by asking and answering questions related to the module.

Key texts and learning resources

Access to materials, including lecture slides and lab notebooks, is centralized through the use of a course website available in the following url:

Specific videos, (computational) notebooks, and other resources, as well as academic references are provided for each learning block.

In addition, the currently-in-progress book “Geographic Data Science with PySAL and the PyData stack provides and additional resource for more in-depth coverage of similar content.