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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 creating a variety of maps and visualizations in QGIS
Tutorial on querying, downloading, and mapping OpenStreetMap data
Introduction to Git/GitHub
Introduction to Python (optional)
Writing and executing a simple Python script
Python 101 (data types, conditionals, loops, functions, loading/saving files, etc.)
Finding and working with libraries
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.)
Exploratory data analysis, statistics, and data visualization in Python (with Seaborn)
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.)
Day 3 - Web Maps and (Spatial) Relational Databases
Relational databases, SQL, postgres
Spatial databases, PostGIS
Connecting to databases in Python
Intro to web-development (HTML, CSS, JS)
Making a simple web-map (with Maplibre)
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