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TU Graz/ Education/ Degree and Certificate Programmes/ Continuing Education/

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.

Source: Sean Gladwell – Fotolia.com
Dates and Deadlines

Next course start: upon request

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) 

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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 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

Application and Contact

Programme Director

Stefan H. REITERER

Dipl.-Ing. DDr. 

 

Portraitfoto Sarah Meinhardt

Contact

Sarah Meinhardt
BA

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

Further Python Courses

PYTHON for Science and Engineering: Basics
Source: Sean Gladwell – Fotolia.com
PYTHON: Advanced
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