Teaching material for geospatial Python

This year I developed new teaching resources aimed at MSc students in geographic data science. The technology stack includes Anaconda, Jupyter notebooks, and an array of open-source Python packages for geospatial analytics and machine learning. I put it all on GitHub at https://github.com/andrea-ballatore/teaching-programming-for-gis.

Anaconda 3 logo

As computer programming was one of the most feared subjects in our department, I felt extremely pleased to see that breaking the content up into short cells in Jupyter notebooks massively improved the module clarity and accessibility. I always thought that the traditional and intimidating scripts in .py files never fully worked for non-STEM students.

This is a summary of the content:

  • 01: Data types and variables (Python)
  • 02: Control structures; Pandas data frames (pandas)
  • 03: Defining functions; temporal data (Python)
  • 04: Vector data (geopandas)
  • 05: Raster data (rasteriorasterstats)
  • 06: Network analysis (networkxosmnx)
  • 07: Machine learning (sklearn); Natural Language Processing (spacy)

License: Creative Commons Attribution-NonCommercial-ShareAlike (CC-BY-NC-SA)