Inhalt

[ 320ERFAADOP21 ] PR Algorithms, Data and Objects with Python

Versionsauswahl
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Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS B1 - Bachelor's programme 1. year (*)Medical Engineering Werner Baumgartner (werner.baumgartner@jku.at) 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Bachelor's programme Fundamentals of Natural Sciences for Technology 2025W
Learning Outcomes
Competences
After successfully completing the course, students will be able to use Python to automate the evaluation of experiments - from the raw data to the print-ready image.
Skills Knowledge
By attending the course, students learn to

• analyze tasks, decompose them with the help of step-by-step refinement and develop algorithms and programs for them. (K1-K6)

• select and use data types and programming paradigms correctly. (K1-K5)

• perform exception handling, runtime analyses and correctness checks. (K1-K5)

• implement complex data types as new classes. (K1-K6)

During the course, students acquire basic knowledge of hardware and programming concepts:

• Basic structure of computing systems.

• Von Neumann architecture/Harward architecture

• Problem analysis and specification of algorithms

• Fundamentals of algorithms and data structures in Python

• Procedural programming in Python

• Object-oriented programming in Python

• Interface programming

Criteria for evaluation Quality of the solution and presentation of programming exercises; written examination
Methods Lecture and programming excercises.
Language German
Study material Script
Changing subject? No
Corresponding lecture (*)320ERFAPR1K18: KV Programmierung I (3 ECTS)

oder

ist gemeinsam mit 320ERFAPR2P21: PR Programmierung II (3 ECTS) äquivalent zu
536COSCPP1V20: VL Programming in Python I (3 ECTS) + 536COSCPP1U20: UE Programming in Python I (3 ECTS)
On-site course
Maximum number of participants 25
Assignment procedure Direct assignment