Geospatial Data Training

Geospatial Data Science in Python - 8h Training

With a track record of client projects, from innovative startups to major corporations, Milan has become a recognized figure in geospatial data. He regularly published Geospatial Python tutorials for Towards Data Science and is a top voice in data science and GIS on LinkedIn. Leveraging this extensive experience, Milan now offers a comprehensive 8-hour course and a 3-hour project presentation workshop on geospatial data science.

This program is specifically tailored to provide a general overview and hands-on technical experience on the modern stack of geospatial data science using Python. Besides the foundations, the course will touch base on topics such as building digital twins using OpenStreetMap data, analyzing road network and transportation data, and combining spatial analytics with machine learning.

The course is built around extensive Python coding and is designed for data scientists and GIS specialists interested in mastering the combination of these two fields. The course closed with a take-home assignment presented on Day 5 of the course.

This is an in-person course in Budapest. ​

Outline

Day 1: Introduction to Geospatial Analytics with Python

• Historical context and significance of geospatial analytics.
• Introduction to Python for geospatial analysis, focusing on basic concepts and tools.
• Hands-on exercises: Working with key geospatial libraries in Python (e.g., Geopandas, Rasterio).
• Data visualization techniques for spatial data, including interactive maps.

Day 2: Mapping Urban Areas

• Getting familiar with free data sources, such as demographics data dn OpenStreetMap
• Download and quantify urban areas based on OSM
• Visualizing and processing road network and public transport data
• Exploratory data analysis for geospatial datasets

Day 3: Geospatial Profiling

• Advanced map visualization with Python
• Overlaying various raster and urban data sets, such as population grid
• Create detailed feature profiles of urban areas
• The concept of spatial indexing

Day 4: Machine Learning in Spatial Analytics

• Introduction to machine learning for geospatial data
• Spatial clustering of topical POIs
• Classification and regression on geospatial problems

Day 5: Final Project Presentation

Please note: Course details are subject to change to ensure the most current and engaging learning experience.

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Technical Details

• Venue : Central European University, Budapest, Nádor utca 15
• App Deadline : April 5 12.00
• Schedule : April 16 18.00-20.00, April 18 18.00-20.00, April 23 18.00-20.00, April 25 18.00-20.00, April 29 - 17.30-20.30
• Prerequisites : Beginner level of Python experience
• Price : HUF 142.000 + VAT
• Equipment : Personal laptop

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Registration and Information

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