After successful completion of the course, students are able to

write Python programs

- having with a solid background in the main packages used in
  scientific programming (NumPy, SciPy),
- to solve their own scientific problems with Python,
- to simulate a specific phenomenon using Python,
- to formulate and to solve various optimization problems, and
- to analyze and visualize scientific data by plotting 2D or 3D
  graphs.

- Introduction to the Python programming language
- The SciPy and NumPy ecosystem
- Data processing and plotting (Matplotlib)
- Code testing
- Reproducible and interactive data processing with IPython/Jupyter
- Introduction to solving optimization problems with Python (e.g.,
  SciPy, PuLP)
- Parallel processing in Python
- Interfaces to other programming languages (e.g., Julia)

- programming exercises
- small software projects

- completing a student project
- exam (oral or written)

Students enrolled in Master program "Computational Science and Engineering" have priority.