The module provides students with core competences in Geographic Data Science
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.
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.
The student will receive feedback through the following channels:
- Formal assessment of two summative assignments. 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
- 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 readings, videos, and/or podcasts, as well as academic references will be
provided for each lecture and lab, and can be accessed through the course