After successful completion of the course, students are able to use Python  in their studies and their research. They will be familiar with standard libraries for data visualization, cheminformatics, quantum chemistry and machine learning. Students will also be able to write and test their own Python packages.

Python programming including commonly used libraries for scientific Python with a focus on chemistry (scipy, numpy, matplotlib, scikit-learn, scikit-image, pandas, RDKit).

Covered topics are (among others):

• visualization of datasets typical encountered in chemistry (point clouds, lines, spectra, distributions,...)
• machine learning using measurement data (linear and multivariate regressions) and molecular data
• cheminformatics (molecular descriptors, structure and sub-structure search)
• image processing (image correction, particle detection)
• debugging, testing and packaging

This course uses a blended learning approach: course material (transcripts, code examples, slide shows) will be  available online for self-study. In person lectures offer the opportunity to ask questions about these materials. Each lesson will be accompanied by online programming exercises.

• 80% results of online programming exercises
• 20% class room participation

This course is targetted at chemistry students, hence enrollment in one of the studies assigned to Technical Chemistry (Bachelor, Master, Doctorate) is reuired.

The course is limited to 40 participants to ensure our work load stays manageable.