Universität Wien

053718 VO+UE Fundamentals of Computing (2026W)

5.00 ECTS (3.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Prüfungsimmanente Lehrveranstaltung

Preparation meeting: Wednesday September 30th, 2026, 9:00 - 11:00
HS 2i, NIG, Universitätsstrasse 7, 2nd floor
Moodle
Mi 21.10. 16:45-20:00 Hörsaal 2i NIG 2.Stock C0228

An/Abmeldung

Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").

Details

max. 25 Teilnehmer*innen
Sprache: Englisch

Lehrende

Termine (iCal) - nächster Termin ist mit N markiert

  • Mittwoch 07.10. 16:45 - 20:00 Hörsaal 2i NIG 2.Stock C0228
  • Mittwoch 14.10. 16:45 - 20:00 Hörsaal 2i NIG 2.Stock C0228
  • Mittwoch 28.10. 16:45 - 20:00 Hörsaal 2i NIG 2.Stock C0228
  • Mittwoch 04.11. 16:45 - 20:00 Hörsaal 2i NIG 2.Stock C0228
  • Mittwoch 11.11. 16:45 - 20:00 Hörsaal 2i NIG 2.Stock C0228
  • Mittwoch 18.11. 16:45 - 20:00 Hörsaal 2i NIG 2.Stock C0228
  • Mittwoch 25.11. 16:45 - 20:00 Hörsaal 2i NIG 2.Stock C0228
  • Mittwoch 02.12. 16:45 - 20:00 Hörsaal 2i NIG 2.Stock C0228
  • Mittwoch 09.12. 16:45 - 20:00 Hörsaal 2i NIG 2.Stock C0228
  • Mittwoch 16.12. 16:45 - 20:00 Hörsaal 2i NIG 2.Stock C0228
  • Mittwoch 13.01. 16:45 - 20:00 Hörsaal 2i NIG 2.Stock C0228
  • Mittwoch 20.01. 16:45 - 20:00 Hörsaal 2i NIG 2.Stock C0228
  • Mittwoch 27.01. 11:30 - 14:45 PC-Seminarraum 1, Kolingasse 14-16, OG01
  • Mittwoch 27.01. 16:45 - 20:00 Hörsaal 2i NIG 2.Stock C0228

Information

Ziele, Inhalte und Methode der Lehrveranstaltung

Different branches of digital sciences and computer science such as artificial intelligence, robotics, computational cognitive modelling, human-computer interaction, digital ethics, human-centric computing, etc., can be considered important (and even core) research fields of cognitive science. Analytical thinking and programming play fundamental roles in all these fields. Moreover, beyond their application in digital sciences and computer science, analytical thinking and programming are also widely used in other branches of cognitive science, such as the development of computer-based empirical experiments, data collection, data representation, data analysis, data visualisation, etc. Therefore, acquiring basic and working knowledge of analytical thinking and programming is essential for all cognitive science students.

This course introduces students to:
• basic concepts and challenges of digital sciences
• basic concepts of computation
• analytical, systemic and algorithmic thinking
• applied problem-solving skills
• working knowledge of programming (using Python), skills related to programming and application of programming in cognitive science
• practical skills related to design, implementation, and evaluation of computer-based empirical experiments, data collection, representation, analysis, visualisation, etc.
• basic understanding of the role of computer science in cognitive science
• basic understanding of human-centric approaches in computer science as well as cognitive science
• basic understanding of ethics and accountability in computer science as well as cognitive science

Art der Leistungskontrolle und erlaubte Hilfsmittel

The course will be graded on a basis of 100 points in total:• 100-87 points: Excellent (1)• 86-75 points: Good (2)• 74-63 points: Satisfactory (3)• 62-50 points: Sufficient (4)• 49-0 points: Unsatisfactory (5) (fail)

Mindestanforderungen und Beurteilungsmaßstab

Assessment criteria:• 10% Active participation• 25% Homework assignments and presentation (debriefing) of homework assignments • 50% In-class quizzes and final Exam 15% Final Project• A positive score (>50%) in each of the above criteria is required for passing the course.• Regular participation in at least 90% of sessions is obligatory.

No materials or tools may be used or drawn upon during the quizzes and final exam

Prüfungsstoff

Exam questions will be based on what we discuss in class, the readings and the homework assignments.

Literatur

- Beecher, K. (2017). Computational Thinking: A beginner’s guide to problem-solving and programming (Illustrated edition). BCS, The Chartered Institute for IT.
- Bouras, A. S. (2019). Python and Algorithmic Thinking for the Complete Beginner (2nd Edition): Learn to Think Like a Programmer. Independently published.
- Farrell, S. (2018). Computational Modeling of Cognition and Behavior. Cambridge University Press.
- Curzon, P., & Mcowan, P. W. (2017). Power Of Computational Thinking, The: Games, Magic And Puzzles To Help You Become A Computational Thinker. WSPC.
- Jesús, S. D., & Martinez, D. (2020). Applied Computational Thinking with Python: Design algorithmic solutions for complex and challenging real-world problems. Packt Publishing.
- Maeda, J. (2019). How to Speak Machine: Computational Thinking for the Rest of Us. Portfolio.
- McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython (2nd edition). O’Reilly UK Ltd.
- Miller, B. N., & Ranum, D. L. (2011). Problem Solving with Algorithms and Data Structures Using Python (002 edition). FRANKLIN BEEDLE & ASSOC.
- Zingaro, D. (2020). Algorithmic Thinking: A Problem-Based Introduction. No Starch Press.
- Géron, A. (2019). Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (2nd ed edition). O’Reilly UK Ltd.

Further literature will be announced in the course

Zuordnung im Vorlesungsverzeichnis

Letzte Änderung: Do 24.09.2026 09:46