Resources

Learning Spatial Data Science (SDS) is a bit like learning a new language and how to use it to solve real-world problems.
The resources below support different stages of your learning journey, whether you want to strengthen your Python basics, learn geospatial workflows, or think more deeply about spatial data science.

All resources listed here are:

• free and openly available

• widely used in university teaching

• well aligned with the SDS mindset (practical, reproducible, problem-oriented)

Online Courses¶

These courses work well alongside this course. You don’t need to complete any of them, pick what matches your current level and interests.

• Geo-Python
  Kamyar Hasanzadeh & Dave Whipp
  A hands-on course focusing on Python for geospatial data analysis and workflows.

• Introduction to GIS Programming
  Qiusheng Wu
  Combines GIS concepts with modern Python-based spatial analysis.

• Foundations of Spatial Data Science
  Jon Reades
  Strong focus on concepts, theory, and how spatial data science fits into the broader data science landscape.

• Python Foundation for Spatial Analysis
  Ujaval Gandhi
  A very solid introduction to Python using spatial examples. Ideal if you are new to Python or want to refresh the basics.

Python Books¶

These books work well as companions. Use them to look things up, revisit concepts, or read selected chapters when something is unclear.
UZH students also have access to a large collection of Python and data science books via the O’Reilly for Higher Education platform provided by the University Library.

• Introduction to Python for Geographic Data Analysis
  Henrikki Tenkanen, Vuokko Heikinheimo, David Whipp
  A geo-focused introduction to Python programming and geographic data analysis, written specifically for students and practitioners working with spatial data.

• Geographic Data Science with Python
  Sergio J. Rey, Dani Arribas-Bel, Levi J. Wolf
  A comprehensive introduction to geographic data science, covering methods, theory, and tools for analysing spatial data in modern, data-rich environments.

• The Python Coding Book
  Stephen Gruppetta
  Clear explanations and practical examples for learning Python effectively.

• Think Python
  Allen B. Downey
  An accessible introduction to Python for beginners, especially useful if you are new to programming or want a clear conceptual foundation.

YouTube¶

Short video playlists are ideal for visual explanations, tool walkthroughs, and quick refreshers. Use them selectively when you want to see how something is done.

• Geospatial Python
  Matt Forrest
  A YouTube playlist focused on learning Python for GIS and geospatial workflows.

• Introduction to GIS Programming
  Qiusheng Wu
  A video-based introduction to GIS programming concepts and Python-based workflows.

• Python Foundation for Spatial Analysis
  Ujaval Gandhi
  Companion videos to the Python Foundation for Spatial Analysis course.

• Getting Started with Visual Studio Code
  Microsoft
  Short, practical tutorials covering essential VS Code features such as editing, IntelliSense, the command palette, terminals, and extensions.

Podcasts¶

Podcasts are great for big-picture thinking, community insights, and staying connected to the wider spatial and Python ecosystem. Perfect for commuting or low-effort learning.

• Talk Python to Me
  Host: Michael Kennedy
  A weekly podcast covering Python libraries, tools, and people from the Python community.
  (Link to Spotify & Apple Podcasts)

• Spatial Stack
  Host: Matt Forrest
  Conversations on modern geospatial technology, AI, big data, and real-world spatial projects.
  (Link to Spotify & Apple Podcasts)

• Geomob Podcast
  Hosts: Ed Freyfolge, Steven Feldman, Alastair Graham, Denise McKenzie
  A community-driven geospatial podcast linked to the Geomob event series across Europe. Focuses on geoinnovation, industry perspectives, and informal discussions.
  (Link to Spotify & Apple Podcasts)

• The MapScaping Podcast
  Host: Daniel O’Donohue
  Weekly interviews and discussions on GIS, remote sensing, earth observation, and digital geography.
  (Link to Spotify & Apple Podcasts)

Blogs¶

Blogs are ideal for short-form insights, current debates, and practical tips that often do not (yet) appear in textbooks or courses. They are especially useful for staying up to date with emerging tools, methods, and perspectives in spatial data science.

• Spatial Thoughts
  Practical tutorials, reflections, and insights on geospatial workflows, Python, and spatial data science by Ujaval Gandhi.

• GeoAI Unpacked
  A Substack exploring the intersection of geospatial data, machine learning, and AI, with accessible explanations and applied examples.

• Strategic Geospatial
  Industry-focused blog covering trends, applications, and strategic developments in the geospatial sector.

Others¶

The following platforms are not structured courses, but they are extremely useful for quick lookups, syntax clarification, and solving specific coding problems.

Use them as reference tools when you need a fast answer or an alternative explanation.

• W3Schools – Python Tutorial A concise, example-driven reference for Python syntax and basic programming concepts. Particularly useful for quickly checking how a specific function, method, or data type works.

• Stack Overflow A large community-driven question-and-answer platform for programmers. Ideal for troubleshooting error messages, understanding unexpected behaviour, and exploring alternative implementations.

These resources are by no means complete or perfect. If you have any suggestions for additional resources or criticism, please let us know.