The dashboard folder holds the files that are used by streamlit. This python library enables the creation of dashboards without using HTML or having to design the page yourself. To run the dashboard use make streamlit or run streamlit run src/dashboard/dashboard.py manually. A page in your browser should automatically open up, rendering your streamlit code.
If you want to know how to create dashboards with streamlit, take a look into their documentation.
The provided dashboard file uses the values from the API (so make sure you ran the pipeline at least once before starting the dashboard) to demonstrate different visualizations for the data. It can create box-, scatter- and violin plots, line- and bar charts and a custom created HTML table.
In order to create the visualizations, the plotting library altair was used, where you can create graphs from pandas dataframes and filter them. Using altair enables the dashboard do be more dynamic, since the user can choose the generated semesters or institutes and the inital graph can be filtered by using altair.FieldOneOfPredicate(field=field, oneOf=[1,2,3]).