Python-Praktikum (Informatik 1)

This lab course is a part of the module "Informatik 1" (CS 1). It cannot be used to cover lab course modules in higher semesters, e.g., a "Vertiefungspraktikum". That's because this course is concerned with programming, not with a deep dive into any specific sub-area of computer science.

Learning Outcomes:
On completion of the lab course, participants are able to design, implement and debug small programs in the Python programming language.

  • Participants have gathered some experience in using large libraries for specific tasks. Then can work with documentation and research APIs.
  • Students can precisely explain their solutions to programming task and answer questions about the solutions.

Course Contents:
During the first week, students work intensively with the Python programming language. In the second week they deepen their skills and extend them by working on applied tasks, using various libraries.

Participants pass the course if they complete all mandatory tasks (active participation) and prove a solid understanding of their solutions/programs in a short oral exam (individual assessment).

For capacity reasons, the internship is only open to students of the Faculty of Computer Science and individual participants of the Cognitive Science programme.



Course type
Lab courses
Winter Term 2023/2024
moodle course available


Lab course
Takes place every day from 09:00 to 16:00 in room HGB 10.
First appointment is on 26.02.2024
Last appointment is on 08.03.2024


The contents of the course "Informatik 1 - Programmierung" (CS 1 - programming) are assumed as prerequisites. This does not mean that participants must have passed the exam. Furthermore, participants should be confident in working with computers (and operating systems) in general, and with a Python IDE of their choice.

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210