Python for Numerical Methods and Scientific Modelling
This course is part of the university programme "Python for Science and Engineering" (Module 3) and, if successfully completed, can be accredited towards that programme.
Next start: 9 April 2026
Application deadline: 10 March 2026
Course dates: 9, 10 April 2026 from 9 a.m. to 5 p.m.
- Duration: 16 hours
- Certificate: Award of a microcredential (with examination), confirmation of participation (without examination)
- ECTS credit points: 1
- Language of instruction: German or English
- Course fees: € 550 (VAT free) or for Bachelor's and Master's students: € 250 (VAT free)
- Course location: online (Webex)
Contents Python – Numerical and Scientific Computing
- Foundations for Working with Scientific Libraries in PYTHON 3
- Basics of NumPy: arrays, arithmetic operations, indexing
- Linear algebra with NumPy/SciPy (including systems of linear equations, matrix operations, sparse matrices)
- Nonlinear problems with SciPy: root finding, data fitting
- Optimisation with SciPy and CVXOPT
- Creating plots with Matplotlib
- Tuning numerical code: vectorisation, Cython and F2PY
- Outlook on advanced applications: computer algebra with SageMath, FEM with Salome and Code_Aste
you will be able to:
- Automate scientific processes
- Perform numerical simulation and modelling
- Create reproducible research and implement its visualisation
- Python is easy to learn and offers high complexity.
- You can create connections to fast C/C++ and Fortran libraries.
- You can produce highly effective codes with little effort.
- Its high flexibility makes Python ideal for rapid prototyping.
- Python and the associated software are free software. This means you will gain a cost-saving alternative to commercial programmes such as MATLAB.
Target Groups and Admission Requirements
Target groups of Python – Numerical and Scientific Computing
- Professionals working in research and development who are looking for an alternative or complement to MATLAB
Entry requirements of Python – Numerical and Scientific Computing
- Basic knowledge of numerical mathematics
- Basic programming skills in Python
- Recommended: Successful completion of Python – Basics and Python – Advanced
Application and Contact
Stefan H. REITERER
Dipl.-Ing. DDr.
Contact
Sarah Meinhardt
BA
TU Graz Life Long Learning
Tel.: +43 316 873 4945
lifelong.learning@tugraz.at
If you want to sign up for the course, submit the completed registration form by e-mail to lifelong.learning@tugraz.at
Application deadline: 10 March 2026
