Scientific Python course for chemistry students at SDU.
The academic goals of the study group is to gain programming and scripting skills with Python, with focus on solving problems in the natural sciences. First part of the course draws mostly from materials from the SciPy Lecture Notes; in the later parts we will use selected chapters from Fabio Nellis book on Python Data Analytics. Initially, basic programming concepts and the Python syntax will be introduced. Following this, the use of Python as a tool for rapidly processing large amounts of scientific data will be covered, with applications on e.g. treating and visualizing large amounts of data, plotting of experimental spectra or on using image analysis. A tentative list of topics is listed below:
- Basics of Python I; installation, types, syntax
- Basics of Python II; control flow, functions
- Basics of Python III; input/output
- NumPy; numeric Python
- Matplotlib; plotting
- Pandas; data analysis
- SciPy; fitting, function optimization, image analysis
- Advanced topics; code optimization
- Scipy Lecture Notes
- Nelli, Fabio. Python Data Analytics: Data Analysis and Science using Pandas, matplotlib and the Python Programming Language. Apress, 2015. Accessible through SDU library
- How to Think Like a Computer Scientist
The study group consists of biweekly (total: 11x2) meetings distributed over the four months of the spring semester. Students will prepare for the meetings by reading distributed materials, as well as by completing programming exercises. These task is expected to represent the majority of the workload in the course. The meetings will consist of discussions of the prepared programming exercises, as well as discussions with the supervisors. Following this, new concepts are briefly introduced, after which live coding exercises will commence. The evaluation is an oral evaluation assessed internally by the supervisor as passed/not passed.