Maps as data. Latitude / longitude, coordinate reference systems (CRS, EPSG, WGS84, Web Mercator EPSG:3857), projections (Mercator, equal-area, conformal, conic), vector data (points, lines, polygons, GeoJSON, Shapefile, GeoPackage, KML), raster data (GeoTIFF, COG, NetCDF, HDF5), GIS fundamentals (QGIS, ArcGIS), Python ecosystem (Geopandas, Shapely, Fiona, PyProj, Rasterio, Folium, Pydeck, Kepler.gl, Geoplot), R ecosystem (sf, terra, leaflet, tmap), spatial operations (intersects, contains, within, buffer, union, difference), spatial joins, distance & geodesic calculations, geocoding (Nominatim, Mapbox, Google), reverse geocoding, isochrones & routing (OSRM, Valhalla, Mapbox Directions), choropleth maps, dot density maps, heat maps & kernel density (KDE), spatial autocorrelation (Moran's I, Geary's C, Getis-Ord), hot-spot analysis, geographically weighted regression (GWR), spatial clustering (DBSCAN, HDBSCAN, spatial K-means), interpolation (IDW, Kriging), satellite imagery & Earth observation (Sentinel, Landsat, MODIS, Planet), Google Earth Engine, OpenStreetMap data (Overpass API, Osmium), web mapping (Leaflet, Mapbox GL JS, MapLibre, deck.gl), tile servers, vector tiles & MVT, PostGIS in PostgreSQL, BigQuery GIS, Snowflake geospatial, location intelligence (LCA, retail site selection, delivery optimization), and a capstone real-world map project. 30 units, 450 lessons.