Universität Wien

040313 KU Introduction to Programming for Business Students (BA) (2025W)

4.00 ECTS (2.00 SWS), SPL 4 - Wirtschaftswissenschaften
Prüfungsimmanente Lehrveranstaltung

Zusammenfassung

1 Kocatürk , Moodle
Di 18.11. 13:15-14:45 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
2 Schüttengruber , Moodle
Di 18.11. 15:00-16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
3 Schüttengruber , Moodle
Di 18.11. 16:45-18:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
An/Abmeldeinformationen sind bei der jeweiligen Gruppe verfügbar.

Gruppen

Gruppe 1

max. 50 Teilnehmer*innen
Sprache: Englisch
Lernplattform: Moodle

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Dienstag 07.10. 13:15 - 14:45 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 14.10. 13:15 - 14:45 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 21.10. 13:15 - 14:45 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 04.11. 13:15 - 14:45 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 11.11. 13:15 - 14:45 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 25.11. 13:15 - 14:45 PC-Seminarraum 3 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 02.12. 13:15 - 14:45 Digital
  • Dienstag 09.12. 13:15 - 14:45 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 16.12. 13:15 - 14:45 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 13.01. 13:15 - 14:45 Digital
  • Dienstag 20.01. 13:15 - 14:45 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 27.01. 13:15 - 14:45 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß

Gruppe 2

max. 50 Teilnehmer*innen
Sprache: Englisch
Lernplattform: Moodle

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

Achtung: Dieser Kurs wird in drei Gruppen zu den vorgesehenen Terminen durchgeführt. Jede Woche gibt es zwei aufeinanderfolgende Unterrichtsblöcke von 1,5 Stunden von 13:15, 15:00-16:30, 16:45-18:15 Uhr, mit einer Pause dazwischen.

  • Dienstag 07.10. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 14.10. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 21.10. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 04.11. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 11.11. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 25.11. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 02.12. 15:00 - 16:30 Digital
  • Dienstag 09.12. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 16.12. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 13.01. 15:00 - 16:30 Digital
  • Dienstag 20.01. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 27.01. 15:00 - 16:30 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß

Gruppe 3

max. 50 Teilnehmer*innen
Sprache: Englisch
Lernplattform: Moodle

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

Der Kurs startet wöchentlich um 16:45.

  • Dienstag 07.10. 16:45 - 18:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 14.10. 16:45 - 18:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 21.10. 16:45 - 18:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 04.11. 16:45 - 18:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 11.11. 16:45 - 18:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 25.11. 16:45 - 18:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 02.12. 16:45 - 18:15 Digital
  • Dienstag 09.12. 16:45 - 18:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 16.12. 16:45 - 18:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 13.01. 16:45 - 18:15 Digital
  • Dienstag 20.01. 16:45 - 18:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß
  • Dienstag 27.01. 16:45 - 18:15 PC-Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Untergeschoß

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

This course is designed to provide students with a foundation in the principles of programming logic by utilizing the Python programming language and environment. The course is designed to assist students in developing an understanding of how to apply programming solutions and related algorithmic thinking to address common business and decision-making problems. They have knowledge of Python syntax, data structures (data types, variables, operators, etc.), and control structures (conditional statements, loops) as well as standard I/O, functions, and exception handling. They know how to access file systems and are familiar with important libraries for data analysis. Finally, they will learn how to benefit from AI (i.e., ChatGPT, Gemini) in an ethical way in order to write Python codes and to resolve debugs in the code.

The course employs a blended learning format, integrating in-person and distance learning components. The students are provided with the relevant course material in advance, which are addressed in class. During the course, students are assigned various tasks and provided with illustrative examples (typical problems in programming and data analysis) to solve. This enables students to evaluate their ability to apply their knowledge. The potential outcomes of these tasks and illustrative examples are discussed in the classroom. For specific topics, there are additional examples and tasks, which are solved together in class. It is imperative that students have the opportunity to engage in reflective practices and engage in discussions about problems in the class. At the end of the lectures, students should each independently solve a small example on site and briefly explain how they solved the problem.

Although the course takes place in a computer lab, students are encouraged to use Python on their own laptops. Bringing a personal computer is optional but recommended. For those who do, the installation and setup of Python will be covered at the beginning of the course. Having Python installed locally may be helpful for working on assignments and the final project outside of class.

No prior experience in programming is necessary. It is expected that students have the fundamental computer skills encompass the ability to navigate webpages, run software applications, and manage documents.

Art der Leistungskontrolle und erlaubte Hilfsmittel

The solution of individual examples at the end of the exercise sessions will be weighted at 30%.

Students will be divided into groups of 3-4 and each group will propose a project topic, during which each group will solve a different small business problem. After the project topics have been determined, each group will be paired with another group. Four weeks after the project topics are determined, each group will present their progress to the paired group, and after the presentations, group members will provide constructive feedback to the paired group. Each group will be evaluated by the instructors based on their progress in the project and the usefulness and constructiveness of the feedback they provide. These peer review presentations will account for 20% of the total grade.

Finally, the course will conclude with a final project presentation. The final project presentation will require a collaborative project presentation to demonstrate the following objectives: motivation, problem definition, scope, architecture, and potential solution design using the Python environment. The final presentation will account for 50% of the total grade.

Mindestanforderungen und Beurteilungsmaßstab

Students who do not contribute to the assigned project and do not attend the final presentations will be considered failed and will receive an NA grade. Students must accumulate at least 50% of the points from the following grading elements to pass this course:

• Task Evaluations: 30%
• Peer Review Presentations: 20%
• Final Presentation: 50%

Prüfungsstoff

Task evaluation will be graded cumulatively throughout the semester based on the solutions and explanations of the solution process for the tasks/small examples given at the end of the sessions.

In peer review presentations, each group should present their progress and provide useful and constructive feedback to the partner group.

In the final presentation, each group should present their project, and each group member should speak during the presentation. The final presentation should include the following sections: motivation, problem definition, scope, architecture, and potential solution design using the Python environment.

Literatur

Course books:
• Frederick Kaefer, Paul Kaefer, Introduction to Python Programming for Business and Social Science Applications, 1st Edition, SAGE Publications;2020.
• Allen B. Downey, Think Python, 3rd Edition, O’Reilly Media, Incorporated; 2024. Online version: https://allendowney.github.io/ThinkPython/index.html
• Lubanovic B., Introducing Python, 2nd Edition, O’Reilly Media, Incorporated; 2019.
• Wes McKinney, Python for Data Analysis: Data Wrangling with Pandas, NumPy and IPython, 2nd Edition, O’Reilly Media, Incorporated; 2017.
• Nathan Hunter, The Art of Prompt Engineering with chatGPT: A Hands-On Guide, Independently published; 2023.
• Flaig S., Python programmieren lernen mit ChatGPT: Als Einsteiger 5-mal schneller professionelle Anwendungen programmieren mit Künstlicher Intelligenz (KI), AES Verlag; 2024.

Free & Online Resources:
• Kaggle Courses – Python: https://www.kaggle.com/learn/python/
• W3Schools Python Tutorial: https://www.w3schools.com/python/
• Real Python: https://realpython.com/
• OpenAI Cookbook: https://cookbook.openai.com/

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Mi 01.10.2025 10:05