136080 UE Advanced Python Programming: Data Structures and Algorithms as Models of Professional Assumptions (2025S)
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Th 06.02.2025 08:00 to We 26.02.2025 23:59
- Registration is open from Sa 01.03.2025 08:00 to Mo 07.04.2025 23:59
- Deregistration possible until Tu 29.04.2025 23:59
Details
max. 25 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Tuesday 08.04. 09:45 - 13:00 Seminarraum 4 2H558 UZA II Rotunde
- Tuesday 29.04. 09:45 - 13:00 Seminarraum 4 2H558 UZA II Rotunde
- Tuesday 13.05. 09:45 - 13:00 Seminarraum 4 2H558 UZA II Rotunde
- Tuesday 27.05. 09:45 - 13:00 Seminarraum 4 2H558 UZA II Rotunde
- N Saturday 14.06. 09:45 - 14:45 Hörsaal 2 Hauptgebäude, Tiefparterre Stiege 5 Hof 3
- Tuesday 24.06. 09:45 - 13:00 Seminarraum 4 2H558 UZA II Rotunde
Information
Aims, contents and method of the course
Methods will be mostly hands on. Based on code-snippets, real live and synthetic data, problem descriptions, hand-outs and other provided material students will step by step alone, in pairs and groups solve problems to acquire deeper understanding of approaches, concepts and practices.Smaller tasks will be done as home work.
Assessment and permitted materials
It is essential, that students have a working python environment in Visual Studio Code and git installed on their computers at the start of the course.LLM-based Helpers and tools like CoPilot are highly encouraged, but not needed for the course.
Minimum requirements and assessment criteria
The evaluation of the course will be based on the engagement during the course time (70%) and a small project done by each student (30%).
Examination topics
There is no exam for the course. In addition to regular reading assigments, the grade will mostly be based on a research project showcasing the skills taught in class.
Reading list
Complete list will be handed out at the start of the seminar.Bhargava, A. Y. (2024). Grokking algorithms (Second edition). Manning Publications Co.
Fowler, M. (2019). Refactoring: Improving the design of existing code (Second edition). Addison-Wesley.
Freeman, E., Robson, E., Sierra, K., & Bates, B. (2021). Head first design patterns: Building extensible & maintainable object-oriented software (2nd edition). O’Reilly®.
Kofler, M., Öggl, B., & Springer, S. (2025). Coding mit KI: Das Praxisbuch für die Softwareentwicklung (1. Auflage). Rheinwerk Verlag.
Kopec, D. (2019). Classic Computer Science Problems in Python. Manning Publications Co. LLC.
Lahres, B., Raýman, G., & Strich, S. (2021). Objektorientierte Programmierung: Das umfassende Handbuch (5., aktualisierte Auflage). Rheinwerk Verlag.
Molak, A., & Jaokar, A. (2023). Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more. Packt Publishing.
Moretti, F., & Moretti, F. (2007). Graphs, maps, trees: Abstract models for literary history (Paperback edition). Verso.
Percival, H. J. W. (2017). Test-driven development with Python: Obey the testing goat: using Django, Selenium, and JavaScript (Second edition). O’Reilly.
Slatkin, B. (2025). Effective Python: 125 specific ways to write better Python (Third edition). Addison-Wesley.
Steinkamp, V. (2024). Mathematische Algorithmen mit Python: Aufgaben vom Sieb des Eratosthenes bis zur RSA-Verschlüsselung (2., aktualisierte und erweiterte Auflage). Rheinwerk.
Fowler, M. (2019). Refactoring: Improving the design of existing code (Second edition). Addison-Wesley.
Freeman, E., Robson, E., Sierra, K., & Bates, B. (2021). Head first design patterns: Building extensible & maintainable object-oriented software (2nd edition). O’Reilly®.
Kofler, M., Öggl, B., & Springer, S. (2025). Coding mit KI: Das Praxisbuch für die Softwareentwicklung (1. Auflage). Rheinwerk Verlag.
Kopec, D. (2019). Classic Computer Science Problems in Python. Manning Publications Co. LLC.
Lahres, B., Raýman, G., & Strich, S. (2021). Objektorientierte Programmierung: Das umfassende Handbuch (5., aktualisierte Auflage). Rheinwerk Verlag.
Molak, A., & Jaokar, A. (2023). Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more. Packt Publishing.
Moretti, F., & Moretti, F. (2007). Graphs, maps, trees: Abstract models for literary history (Paperback edition). Verso.
Percival, H. J. W. (2017). Test-driven development with Python: Obey the testing goat: using Django, Selenium, and JavaScript (Second edition). O’Reilly.
Slatkin, B. (2025). Effective Python: 125 specific ways to write better Python (Third edition). Addison-Wesley.
Steinkamp, V. (2024). Mathematische Algorithmen mit Python: Aufgaben vom Sieb des Eratosthenes bis zur RSA-Verschlüsselung (2., aktualisierte und erweiterte Auflage). Rheinwerk.
Association in the course directory
DH-S II
Cluster I: Sprache und Literatur
Cluster I: Sprache und Literatur
Last modified: Fr 14.03.2025 13:26