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 using Jupyter notebooks
Part 1- successfully completing the exercises
Part 2- written exam with programming exercises- mode: TUWEL quiz or Jupyter notebook- required infrastructure: Computer with Internet connection, Webcam
Students enrolled in Master program "Computational Science and Engineering" have priority.