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PYTHON in Numerical and Scientific Computing

In recent years, Python has grown in importance for the areas of numeric simulation and scientific computing. Python is used in a variety of projects (e.g. Numpy, Scipy, Matplotlib), as a scripting language in finite element codes, as a system language in Linux and in web development (e.g. Django).

In the course ‘Python in Numerical and Scientific Computing’ at TU Graz you will learn what you need to begin working with scientific libraries in Python.


  • You will learn the basics of working with scientific libraries in Python (Python 2 and Python 3):
    • Introduction to Numpy: arrays, arithmetic, indexing
    • Linear algebra with Numpy/Scipy (including linear equation systems, matrix operations, sparse matrices)
    • Nonlinear problems with Scipy: finding maxima and minima, data fitting
    • Optimisation with Scipy and CVXOPT
    • Creating graphics with Matplotlib
    • Tuning numeric codes: vectorising, Cython and F2PY
    • Brief presentation of advanced applications: computer algebra with SageMath, FEM with Salome and Code_Aster
  • You will develop realistic and meaningful examples.
  • You will learn how to tackle complex problems.
  • You will learn to use the possibilities of various features of Python.
You will be able to use what you learn in this course anywhere and without additional costs, because all the software used is free.

Target Groups and Admission Requirements

  • Experts from research and development who want to use Python in their work and are looking for an alternative or supplement to MATLAB.
To participate in the course you will need to understand the basics of numerical mathematics and programming in Python.

Dates and Deadlines

Next course date: to be announced

Quick Facts

  • Duration: 16 hours
  • Certificate: Confirmation of participation
  • Language of instruction: German or English
  • Course fees: € 550 (VAT free) or for Bachelor's and Master's students: € 250 (VAT free)
    The course fees include course handouts and refreshments in breaks.
  • Course location: online or in-house training
  • Course times: by agreement

Why you Should Learn Python

  • 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.

Programme Director


Dipl.-Ing. Dr.


Sarah Meinhardt

TU Graz Life Long Learning
Tel.: +43 316 873 4945