Teaching content

This module

• provides an introduction to programming using the Python programming language
• provides a basis for self-learning or directed learning of more specialised topics at a later stage
• has been designed for scientists and friends (such as physicists, biologists, chemists, mathematicians, engineers) to teach the practical skills that are relevant for modern computational science and data science
• does not assume that participants have attended computer science lectures before
• does not assume prior programming knowledge of participants (i.e. complete beginners are welcome although they may experience the pace as high)
• introduces main programming paradigms (imperative, object oriented and functional)
• introduces relevant Python modules for numerical calculations and data processing (such as numpy, scipy, pandas)
• introduces plotting skills (primarily matplotlib)
• introduces symbolic calculations (sympy)
• introduces efficient software engineering skills for scientists (including testing). Version control is not covered (but see this course)
• provides a wide spectrum of skills that are advantageous for all scientists who need to handle data - be it from experiment or simulation
• introduces the Jupyter Notebook (a more detailed introduction is available in this course)
• outlines the challenges and some solution of reproducible computational science and data analysis.

No machine learning is taught in this module (other than conventional data analysis methods such as curve fitting).

Teaching methods

The central learning activities are self-based problem-solving sessions where participants need to write Python code to solve given problems. These start at a very basic level and get more complicated over time (additional exercise become available during the course)..

The lectures provide the knowledge that is required to solve the given practical problems.

Student receive (automated) feedback on every exercise they complete; in particular this feedback provides an indication if the functionality of the result is as was requested.

(Human) Help and advice is available during the scheduled exercise sessions so that students can seek advice while working on the exercises. It is also encouraged to discuss solutions with the teaching staff: generally, many solutions are possible and appropriate - the real value is in understanding why one of them may be better (or not).

Contact

The course is open to staff and students from MPSD and partner organisations.

For questions, please contact the module load (Hans Fangohr, hans.fangohr@mpsd.mpg.de).