This is a second course which is organized by PIER due to high demand for the first course (https://indico.desy.de/event/32340/).
In the five-day course “Advanced Python - Analysis and Visualization of Scientific Data” the
participants learn the most fundamental skills for analyzing (large) scientific data sets using
Python and Jupyter Notebooks.
The course will take place in five sessions on
Wed, June 1, 9am - 12.30pm
Wed, June 8, 9am - 12.30pm
Wed, June 15, 9am - 12.30pm
Wed, June 22, 9am - 12.30pm
Wed, June 29, 9am - 12.30pm
Each of the five course days consists of lectures and exercises. In the exercises, the participants are encouraged to work on their own data sets, if
applicable.
The specific content of the course is:
Day 1: Brief revision of Python Syntax and Jupyter notebooks, first data analysis and visualization with Numpy and Matplotlib. Discussion of participants’ projects.
Day 2: Treating larger data sets with Numpy and Pandas.
Day 3: Visualization of data with Matplotlib, version control with git.
Day 4: Fitting and filtering of data, virtual environments.
Day 5: Brief introduction to parallelization, symbolic calculations.
By education Alexander is an interdisciplinary scientist between chemistry, physics, and materials science. In his scientific career he used X-rays, lasers, and electron beams to investigate ultrafast dynamics of novel materials and chemical systems. When teaching this Python course, he draws on his experience as a scientist analyzing and interpreting experimental data from large scale facilities such as XFELs and synchrotrons.
