Universität Wien

052300 VU Foundations of Data Analysis (2025W)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle
We 10.12. 09:45-11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03

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).

Details

max. 50 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Wednesday 01.10. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 02.10. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
  • Wednesday 08.10. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 09.10. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
  • Wednesday 15.10. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 16.10. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
  • Wednesday 22.10. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 23.10. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
  • Wednesday 29.10. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 30.10. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
  • Wednesday 05.11. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 06.11. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
  • Wednesday 12.11. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 13.11. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
  • Wednesday 19.11. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 20.11. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
  • Wednesday 26.11. 09:45 - 11:15 Audimax Zentrum für Translationswissenschaft, Gymnasiumstraße 50
  • Thursday 27.11. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
  • Wednesday 03.12. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 04.12. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
  • Thursday 11.12. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
  • Wednesday 17.12. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 18.12. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
  • Wednesday 07.01. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 08.01. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
  • Wednesday 14.01. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 15.01. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
  • Wednesday 21.01. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 22.01. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
  • Wednesday 28.01. 09:45 - 11:15 Hörsaal C2 UniCampus Hof 2 2G-K1-03
  • Thursday 29.01. 11:30 - 13:00 Hörsaal D Unicampus Hof 10 Hirnforschungzentrum Spitalgasse 4
    Hörsaal I NIG Erdgeschoß

Information

Aims, contents and method of the course


Today's currency is data. However, data is only useful if we are able to extract useful information from it. This is the aim of data analysis in general. This course aims to survey the foundations of data analysis. This includes concepts from statistical inference, regression analysis, classification analysis, clustering analysis, and dimensionality reduction.
Concepts as well as techniques are introduced and practiced.

Methods: lectures, pre-recorded video lectures, live lectures and review sessions. New video lectures, tutorials and other learning materials will be made available on Moodle on an ongoing basis. In the review sessions, we will review the most important concepts introduced in the videos and answer any questions you may have.

Attendance in the first lecture is mandatory!

You can attend the introductory lecture on October, 1st over Zoom:

https://univienna.zoom.us/j/67439409673?pwd=0NVSz9w70d7rVMcoAvgrKxtAU2GpE7.1
Meeting-ID: 674 3940 9673
Code: 210470

Assessment and permitted materials

- 2 Exams (24% each): 1 mid-term exam about part 1 (supervised learning) and 1 end-term exam about part 2 (unsupervised learning)
- 2 Labs (24% each): 1 Supervised, 1 Unsupervised (this will be split into 3 mini – labs (programming) + 3 mini – test (in-person, attendance at these lecture dates is mandatory)
- 1 Mathematics Prerequisite Exercise (1%) to check mathematical background knowledge (in Moodle until October, 19th)
- 3 Feedback Opportunities (1% each), anonymously via Moodle, deadlines will be announced on Moodle

In addition, you can earn at most 10% of bonus points for completing voluntary quizzes

Minimum requirements and assessment criteria

For bachelor students, the mandatory prerequisite for this class is the successful completion of the following courses:
- StEOP
- Programmierung 2 (PR2)
- Mathematische Grundlagen der Informatik 2 (MG2)
- Theoretische Informatik (THI)
- Modellierung (MOD)
- Algorithmen und Datenstrukturen (ADS)

Grading will be done according to the following scheme:
1 – at least 87.5%
2 – at least 75.0%
3 – at least 60.0%
4 – at least 40.0%

To pass the course, you need at least 30% of the total score in all assignments combined with 30% of the total score of the exams.

In order to successfully pass the course, regular attendance is strongly recommended.

For the assignments, no aids are allowed unless explicitly stated otherwise.

Examination topics

1. Models, Statistical Inference, and General Techniques
1.1. Fundamental Concepts in Inference
1.2. Parametric Inference
1.3. Data Splitting, Cross-Validation
2. Regression Modelling
2.1. Simple Linear Regression
2.2. Multiple Regression
2.3. Further Regression Methods
2.4. Generalized Linear Models
2.5. Regression Trees
3. Classification Modelling
3.1. Decision Theoretic Introduction; Error rates, and Bayes Optimality
3.2. Logistic Regression
3.3. Classification Trees
3.4. Support Vector Machines
3.6. Further Classification Methods
4. Neural Networks
5. Basic Techniques of Unsupervised Learning
5.1. Dimension Reduction (Matrix Factorization)
5.2. Association Rules
6. Clustering Methods
6.1. Partitioning Clustering
6.2. Hierarchical Clustering
6.3. Density-based Clustering
6.4. Evaluation and Validation of Clustering Results

Reading list

Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer 2007.

Han, Kamber: Data Mining: Concepts and Techniques, Elsevier 2012.

Hastie-Tibshirani-Friedman: The Elements of Statistical Learning, Springer 2009.

James-Witten-Hastie-Tibshirani: An Introduction to Statistical Learning with Applications in R, Springer 2015.

Shai Shalev-Shwartz, Shai Ben-David: Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press 2014.

Association in the course directory

Module: FDA AKM SWI STW

Last modified: Tu 18.11.2025 10:05