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GEOG 213/313: Advanced Geospatial Analytics with Python
Introduction
Syllabus
Team
Schedule
Assessment
Resources
Policies
Lectures
1. Intro to Unix and Bash
2. Intro to Version Control and Git
3. Python Environments
4. Working with GenAI Tools
5. Intro to JupyterLab
6. Xarray Fundamentals
7. Advanced Xarray Operations
8. Review of CRS
9. CRS and Projections in Python
10. Geospatial Landscape
11. Searching a STAC Catalog
12. Intro to Dask & Dask Array
13. Scaling stackstac w Dask
14. Into to Dask DataFrame
15. Review of Raster Data
16. Raster Data in Python
17. Raster Data Processing
18. Review of Vector Data
19. Vector Data in Python
20. Intro to Dask-GeoPandas
21. Scalable Vector Analysis
22. Workflows in Python
23. Writing Pseudocode
24. Workflow Best Practices
25. Lab: Workflows with an AI Agent
26. Communicating with Maps and Visualizations
27. Geospatial Visualization in Python
Assignments
Assignment 1
Assignment 2
Assignment 3
Assignment 4
Assignment 5
Assignment 6
Assignment 7
NASA Open Science 101
Overview
OS101 Module 1: The Ethos of Open Science
Lesson 1: What is Open Science?
Lesson 2: Why is Open Science Important?
Lesson 3: How to do Open Science
Lesson 4: When Not to be Open
Lesson 5: Planning for Open Science: From Theory to Practice
OS101 Module 2: Open Tools and Resources
Lesson 1: Introduction to the Process of Open Science
Lesson 2: General Tools for Open Science
Lesson 3: Tools for Open Data
Lesson 4: Tools for Open Code
Lesson 5: Tools for Open Results
OS101 Module 3: Open Data
Lesson 1: Introduction to Open Data
Lesson 2: Using Open Data
Lesson 3: Making Open Data
Lesson 4: Sharing Open Data
Lesson 5: From Theory to Practice
OS101 Module 4: Open Code
Lesson 1: Introduction to Open Code
Lesson 2: Using Open Code
Lesson 3: Making Open Code
Lesson 4: Sharing Open Code
Lesson 5: From Theory to Practice
OS101 Module 5: Open Results
Appendices
License
References
Repository
Open issue
Index
