On the way from experiment to interpretation, the data is usually organized, converted and fitted with an appropriate model. This can be done in many ways, with Python being particularly well suited as a toolbox. The course is aimed at practitioners who want to understand their data and are not afraid of short, self-written programs. At the same time, the course gives a first insight into programming using a simple and popular language (Python).

First, the elementary steps of structured programming are discussed (assignments, arrays, branches, loops, subroutines). There is much more to say about programming in general, but that is not covered here. In addition, a data set is loaded, converted, displayed graphically and saved using an example.

In the next step - and this is the learning objective of the course in addition to programming in Python itself - the data is fitted with a model. Such models are the beacon of scientific knowledge (an opinion). Ideally, after 6 x 60 minutes, participants will have acquired the ability to test their own models against experimental data.