Data Mining (FSS 2023)

The course provides an introduction to advanced data analysis techniques as a basis for analyzing business data and providing input for decision support systems. The course will cover the following topics:

  • The Data Mining Process
  • Data Representation and Preprocessing
  • Clustering
  • Classification
  • Regression
  • Association Analysis
  • Text Mining

The course consists of a lecture together with accompanying practical exercises as well as student team projects.  In the exercises the participants will gather initial expertise in applying state of the art data mining tools on realistic data sets. The team projects take place in the last third of the term. Within the projects, students realize more sophisticated data mining projects of personal choice and report about the results of their projects in the form of a written report as well as an oral presentation.

  • Instructors

  • Time and Location

    • Lecture: Wednesday, 10.15 – 11.45, Room A5 B1.44
    • Exercises: Students should attend one of the three exercise groups. The contents are identical.
      • Thursday, 10.15 – 11.45, Room B6 A 1.04 (Alex)
      • Thursday, 12.00 – 13.30, Room B6 A 1.04 (Ralph)
      • Thursday, 13.45 – 15.15, Room B6 A 1.04 (Keti)
  • Grading

    • 75 % written exam (we offer only a single exam and no re-take as the course is offered every semester)
    • 25 % project work (20% report, 5% presentation)
  • Registration

    • For attending the course, please register for the lecture in Portal 2. The course is limited to 90 participants. There will be no “first come – first serve”. Students in higher semesters and students that have failed the course in HWS2022 will be preferred, equally ranked students will be drawn randomly.
    • You don't have to register for the Exercise.



Lecture: Introduction to Data Mining
Tutorial: Introduction to Python (see below table)

Exercise: Preprocessing/Visualization

22.02.2023Lecture: Cluster AnalysisExercise: Cluster Analysis
01.03.2023Lecture: Classification 1Exercise: Classification 
08.03.2023Lecture: Classification 2Exercise: Classification 
15.03.2023Lecture: Classification 3Exercise: Classification 
22.03.2023Lecture: RegressionExercise: Regression
29.03.2023Lecture: Text MiningExercise: Text Mining

- Easter Break -

19.04.2023Introduction to the Student Projects 
and Group Formation
Preparation of project outline
26.04.2023Lecture: Association AnalysisExercise: Association Analysis
03.05.2023Feedback on project outlinesProject Work
10.05.2023Feedback on demandProject Work
17.05.2023Feedback on demandProject Work
24.05.2023Feedback on demandProject Work
28.05.2023Submission of project reports (Deadline: 23:59) 
31.05.2023Presentation of project results 

Final exam


For all students which are not familiar with Python/Jupyter Notebooks, we offer an introduction on Wednesday, 15 February 2023 between 15:30 and 17:00 in room C 015 A5,6.

Lecture Slides and Exercises

Lecture Slides:


Additional material will be found in the ILIAS group of the course.