Geospatial Data Science

Description from DukeHub

Learn about the foundations of modern geospatial data science - how to analyze vector, raster, and network data in Python & train a basic machine learning model to make predictions. Assignments will be based on real data with analytical methods addressing contemporary environ challenges in the US. Learn about aspects of data science including data acquisition, scalability, & visualization and general principles in project mgmt. Undergrads will conduct a research project. Grad students will complete a final project - including a short research paper. Recommended: some experience with programming and GIS. This is a graduate level version of an undergraduate intro course. Open to grads only.

Skills

• programmingdescription: "python" and 1 more
• data and statisticsdescription: "geospatial" and 3 more
• lab workhas a lab component
• ai and machine learningAI review: "train a basic machine learning model"
• research methodsAI review: "Undergrads will conduct a research project"

AI-resilient: 1 of 5 1/5

• ✓Yes: Physical, lab or field work · "laboratory"
• No: Live work with people
• No: Judgment someone is accountable for
• No: A craft you practice with your hands or body
• No: Small, in-person format

The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works

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