Spatial Data Science in Python

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).

Application deadline:

31. 8. 2025

Detailed information and application

Length of study: 50 hours
Form: online
Language: English
Fee: 7500 CZK

Basic information about the microcertificate programme

Annotation

Standalone course validated by the micro-credential.

1. Introduction to Python for Data Science
2. Open Data Science, Data manipulation in Python (pandas)
3. Spatial data (geopandas)
4. Spatial relationships (libpysal)
5. Exploratory spatial data analysis (esda)
6. Point patterns (pointpats)
7. Clustering (scikit-learn)
8. Raster data (xarray)
9. Interpolation (tobler, pyinterpolate)
10. Regression (statsmodels, mgwr)

Location

online (the link will be sent to all registered users)

Results of learning

After finishing the course, students will be able to:

Describe advanced concepts of spatial data science and use the open tools to load and analyze spatial data.
Explain the motivation and inner logic of the main methodological approaches of open SDS.
Critically evaluate the suitability of a specific technique, what it can offer, and how it can help answer questions of interest.
Apply several spatial analysis techniques and explain how to interpret the results in the process of turning data into information.
Work independently using SDS tools to extract valuable insight when faced with a new dataset.

Martin Fleischmann, M.Sc., Ph.D.
martin.fleischmann@natur.cuni.cz