In this course, you'll learn to work effectively with geospatial data using Python. Over two hands-on days, you'll explore the fundamentals of GIS, analyse vector and raster data, work with open geodata sources such as PDOK and the Kadaster, and create compelling visualisations using modern Python tools. By combining practical exercises with real-world examples, you'll develop the skills to build geospatial analyses and interactive maps for professional applications, from urban planning and policy analysis to data-driven decision-making.

What you'll learn

• Introduction to GIS and geodata: what GIS is, how geographic data works, and how Python fits into a modern geospatial workflow.
• Map projections & coordinates: working with projection systems such as RD (Rijksdriehoekstelsel) and WGS84.
• Visualisation with GeoPandas & Plotly Express: turning raw geodata into clear visual insights.
• Spatial analysis: point-in-polygon operations, spatial joins, and location-based decision-making.
• Working with open geodata: using open datasets such as PDOK and the Kadaster in your projects.
• QGIS for advanced visualisations: building interactive and shareable maps for in-depth analyses.

Programme

Part 1 – GIS Fundamentals & Geodata Concepts

• Projections and coordinate reference systems.

Part 2 – Your First Geospatial Workflows in Python

• Visualisation with GeoPandas and Plotly Express.

Part 3 – Spatial Analysis Techniques

• Spatial joins, geometric operations, and point-in-polygon analysis.

Part 4 – Working with Open Datasets

• PDOK, Kadaster, and other open data sources.

Part 5 – Visualisation & Maps

• Interactive maps and advanced visualisations with QGIS.

Part 6 – Best Practices & Q&A

• Data scientists, data analysts, and developers who want to work with geospatial data in Python.
• Teams and organisations using geodata for decision-making, urban planning, marketing, or policy analysis.
• Anyone from beginner to advanced who wants to apply Python in GIS-driven projects.

Prerequisites

• Basic knowledge of Python.
• A general understanding of data analysis is helpful.
• No prior GIS experience required.