udsc-2026

Prerequisite - Introduction to Python

• Python 101 (data types, conditionals, loops, functions, loading/saving files, etc.)
• Writing and executing a simple Python script
• Finding and working with libraries
• Review Programming with Python and computational notebooks and practice in "Learn the Basics"

Day 1 - Urban Data, Maps, Visualization, and GIS

• Intro to urban data analytics, research, and storytelling.
• Intro to common data structures, formats, metrics, variables, and sources for urban data analysis
• Tutorial on spatial data and exploring data in QGIS
• How to make effective charts and maps - lecture and discussion on effective cartography & data visualization
• Tutorial on finding and analyzing census data for demographic / socio-economic analysis and research
• Tutorial on querying, downloading, and mapping OpenStreetMap data
• Tutorials on creating a variety of maps and visualizations in QGIS (choropleths, proportional symbol, dot density, bivariate maps, etc.)
• Introduction to Git/GitHub

Day 2 - Urban Data Analysis in Python

• Jupyter notebook intro (Python + Markdown). Installing packages locally with pip / conda
• Pandas 101 (loading, showing table and subsets, filtering, aggregating, summarizing, descriptive stats, etc.)
• Spatial data in Python using GeoPandas (loading data, viewing data, converting non-spatial to spatial data
• Processing spatial data in Python (geocoding, buffers, dissolve, spatial joins, overlays, etc.)
• Exploratory data visualization in Python (with Seaborn)

Day 3 - Statistics and Web Mapping

• Introductory statistics (descriptive statistics, correlations, linear regression, hypothesis testing)
• Introduction to clustering (k-means, DBSCAN) and dimensionality reduction (PCA)
• Intro to web-development (HTML, CSS, JS)
• Making a simple web-map (with Maplibre)