Universität Wien
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052300 VU Foundations of Data Analysis (2023S)

6.00 ECTS (4.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle

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 01.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Thursday 02.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 08.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Thursday 09.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 15.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Thursday 16.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 22.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Thursday 23.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 29.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Thursday 30.03. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 19.04. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Thursday 20.04. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 26.04. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Thursday 27.04. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 03.05. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
    UZA2 Hörsaal 4 (Raum 2Z221) 2.OG
  • Thursday 04.05. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 10.05. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Thursday 11.05. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 17.05. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 24.05. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Thursday 25.05. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 31.05. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Thursday 01.06. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 07.06. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 14.06. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Thursday 15.06. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Wednesday 21.06. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Thursday 22.06. 09:45 - 11:15 Hörsaal 2, Währinger Straße 29 2.OG
    Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
    PC-Unterrichtsraum 1, Währinger Straße 29 1.UG
  • Wednesday 28.06. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9
  • Thursday 29.06. 09:45 - 11:15 Hörsaal 31 Hauptgebäude, 1.Stock, Stiege 9

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, dimensionality reduction.

Concepts as well as techniques are introduced and practiced.

The lecture will be onsite. Parts of the lecture will be recorded and/or streamed. Details will be announced on Moodle.

Zoom-link to the first lecture on March, 1:
https://univienna.zoom.us/j/65441749610?pwd=NStES0FSTXQzWUx6cVJzd1d5Qmo5UT09
Meeting-ID: 634 5494 8472
Code: 884794

Assessment and permitted materials

- 2 labs (i.e. programming exercises including peer review), for each lab you will get a maximum of 18% of the required points.

- 2 pen-and-paper exercise sheets. They serve as a preparation for the exams. For each exercise sheet you will be able to get a maximum of 5% of the required points.

- 2 exams, one mid-term and one final, each 25% of the total points.

Furthermore you can complete:

- 1 exercise sheet to assess your current mathematical (prerequisite) knowledge, 1% of the total points.

- 3 anonymized feedbacks, each 1% of the total points.

- 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%

Please keep in mind that in order to pass the course, you will need at least 30% of the total score in all labs and homeworks combined with 40% of the total score of the tests.

In order to successfully pass the course, regular attendance is strongly recommended, however not mandatory.

Examination topics

1. Models, Statistical Inference, and General Techniques
1.1. Fundamental Concepts in Inference
1.2. Parametric Inference
1.3. Hypothesis Testing and p-values
1.4. The Bootstrap
1.5. 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. Hierarchical Clustering
6.2. Model-based Clustering
6.3. Evaluation and Validation of Clustering Results
6.4. Density-based Clustering
6.5. Self Organizing Maps

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.

Association in the course directory

Module: FDA AKM SWI STW

Last modified: Tu 13.06.2023 10:27