The course introduces data science and computational analysis using open source tools written in the Python programming language. The course supports students with little prior knowledge of core competencies in Spatial Data Science (SDS). It includes:
• Advancing their statistical and numerical literacy.
• Introducing basic principles of programming and state-of-the-art computational tools for SDS.
• Presenting a comprehensive overview of the main methodologies available to the Spatial Data Scientist and their intuition on how and when they can be applied.
• Focusing on real-world applications of these techniques in a geographical and applied context.
The course revolves around data typically used in social geography, but its applicability is not limited to social geography. In practice, you will work more with vector data than rasters (although we cover those a bit as well) and often with data capturing various aspects of human life. The spatial data science concepts, however, are universal.
Martin, Fleischmann, Ph.D.
Přírodovědecká fakulta, Katedra sociální geografie a regionálního rozvoje
martin.fleischmann@natur.cuni.cz
