# 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.