MUSA-5500-Geospatial Data Science in Python

This course will provide students with the knowledge and tools to turn data into meaningful insights, with a focus on real-world case studies in the urban planning and public policy realms and transform results into understandable and compelling narratives.

Outline

• Week 1. Introduction to Python and Programming Basics

  • Week 1A. Environment setup and Python basics (Link)
  • Week 1B. More about Python (Link)

• Week 2. Data Visualization Fundamentals

  • 2.1 Exploratory data science (Link)
  • 2.2 Data visualization fundamentals-A (Link)
  • 2.3 data visualization fundamentals-B (Link)

• Week 3. Data Manipulation with Pandas

  • 3.1 More about data visualization (Link)
  • 3.2 Intro to GeoPandas and vector data (Link)

• Week 4. Geospatial data mapping

  • 4.1 More about geospatial data mapping (Link)
  • 4.2 Interactive spatial data visualization (Link)

• Week 5. Raster data operations in Python

  • 5.1 Raster data operations (link)
  • 5.2 Raster data analysis (lin)

• Week 6. Advanced geospatial analysis

  • 6.1 Spatial joins and overlays (Link)
  • 6.2. Spatial data analysis using fiona and shapely (Link)

• Week 7. Fall break — No class

• Week 8. Network Analysis

  • 8.1 Network analysis using OSMnx (link)
  • 8.2 Web scraping (link)

• Week 9. Web Scraping

  • 9.1 Web scraping basics (link)
  • 9.2 Web scraping through APIs (link)

• Week 10. Downloading data through APIs

  • 10.1 APIs basics (link)
  • 10.2 More about APIs (link)

• Week 11. Web hosting: GitHub, Quarto, create web pages (Vibe coding using ChatGPT)

• Week 12. Machine Learning (I)

  • Custer analysis using Kmeans(link)
  • Spatially cluster anlaysis using DBSCAN (link)
  • Predictive Modeling (I) (link)

• Week 13. Machine Learning (II)

  • Predictive Modeling (II) (link)
  • Predictive Modeling (III) (link)
  • Deep neural networks (link)

• Week 14. Thanksgiving break — No class

• Week 15. Final project presentations