Python for Science and Engineering: Numerical and Scientific Computing

Python for Numerical Methods and Scientific Modelling

Python has established itself as a powerful tool for numerical computing and scientific programming.
This course focuses on the use of Python in numerical mathematics and scientific computing.
You will learn how to implement mathematical models efficiently, apply numerical methods, and analyse and visualise scientific data.

Python – Numerical and Scientific Computing is aimed at anyone looking to use Python specifically for complex numerical and analytical tasks – from simulations and solving differential equations to data-driven 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.

Dates and Deadlines

Next start: 19 November 2026

19 & 20 November 2026
26 & 27 November 2026

Quick Facts

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)

Curriculum (in German only)

Man at a table with a notebook and a smartphone.

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

Upon successful completion of Python – Numerical and Scientific Computing

you will be able to:

Automate scientific processes
Perform numerical simulation and modelling
Create reproducible research and implement its visualisation

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.

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

Programme Director

Stefan H. REITERER

Dipl.-Ing. DDr.

Contact

Sarah Meinhardt
BA

TU Graz Life Long Learning
Tel.: +43 316 873 4945
lifelong.learningnoSpam@tugraz.at

Application

If you want to sign up for the course, submit the completed registration form by e-mail to lifelong.learningnoSpam@tugraz.at

Application deadline: 19 October 2026

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