# Learning goals Below you find the *intended learning outcomes* after each study week. Please use this table to monitor your learning progress: you can always go back to revisit a previous week’s material. **After lesson 1, you should be able to:** - search for information about GIS packages available in/for Python, - understand how geometric objects are represented in [Shapely](https://shapely.readthedocs.io/), - create geometric object based on coordinate values, and - (optionally) install Python packages on your own computer. **After lesson 2, you should be able to:** - read and write spatial data from and to common file formats, - conduct simple analysis on spatial and non-spatial data sets, and - manage coordinate reference systems and re-project data. **After lesson 3, you should be able to:** - carry out *geocoding*, i.e., convert addresses into coordinates, and vice versa, - conduct *Point-in-Polygon* queries, - read data from a KML file, - join layers on spatial and attribute values, and - find the nearest neighbour among point objects. **After lesson 4, you should be able to:** - reclassify data based on different criteria (common and custom classifiers), - carry out overlay analysis, e.g., select data based on the boundaries of another layer, - aggregate data and merge geometric objects, and - simplify geometries. **After lesson 5, you should be able to:** - create a static map image with a background map, - create a simple interactive map, and - share your static or interactive map on the internet. **After lesson 6, you should be able to:** - retrieve and save data from OpenStreetMap, - extract simple street network properties and statistics, and - carry out simple route optimisation, using a shortest-path algorithm. %**After working through the optional material for self-study, you should be able to:** %- understand the basics of raster data processing, and %- use Python scripts in QGIS.