FHV Vorarlberg University
of Applied Sciences
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Information on individual educational components (ECTS-Course descriptions) per semester

Scientific Computing with Python (E)

Course unit title Scientific Computing with Python (E)
Course unit code 074703019001
Language of instruction English
Type of course unit (compulsory, optional) Elective
Teaching hours per week 30
Year of study 2026
Number of ECTS credits allocated 2
Name of lecturer(s) Franz GEIGER
Degree programme Mechatronics
Subject area Engineering Technology
Type of degree Bachelor full-time
Type of course unit (compulsory, optional) Elective
Course unit code 074703019001
Teaching units 30
Year of study 2026
Name of lecturer(s) Franz GEIGER
Requirements and Prerequisites

None

Course content
  • Development environments (IDE)
  • Basics of programming in Python
  • Introduction to the Standard Python Library
  • Introduction to Python packages for scientific computing
  • Application examples from mathematics, physics and electrical engineering
Learning outcomes

After completing this course, students will be able to

  • write simple Python programs and use selected development environments for this purpose
  • process simple tasks from mathematics, physics, electrical engineering and mechatronics with the most important Python packages for scientific computing, i.e. they can
    • use the basic data structure array in different scenarios
    • assess the solvability of systems of equations
    • solve systems of equations
    • use vectorisation to solve multivariable problems in a run-time-efficient way
    • visualise results using matplotlib
    • represent complex numbers in the complex plane
    • interpret and visualise complex numbers as time-dependent vectors
Assessment methods and criteria
  • Evaluation of the elaboration and presentation of a example application (20 %)
  • Written examination (80%)


For a positive overall grade, at least 50% of the points must be achieved in each part of the examination.

Recommended or required reading
  • Physik mit Phyton - Simulationen, Visualisierungen und Animationen von Anfang an; Natt, Oliver; Springer Spektrum Verlag, 2020
  • Der Phyton-Kurs für Ingenieure und Naturwissenschaftler; Steinkamp, Veit; Rheinwerk Technik Verlag, 2021
  • Algorithmen in Python; Kopec, David; Rheinwerk Computing Verlag, 2020
Mode of delivery (face-to-face, distance learning)

Online, selected teaching units in presence.