Data Mining (FSS 2022)

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.

Exam Review

The exam review for FSS2022 will take place on Thursday, September 22th 2022 at 9:00. Information about the room in which the review will take place will be published on this page in September.

  • Instructors

  • Time and Location

    • Lecture: Wednesday, 10.15 – 11.45, online ZOOM (Christian Bizer)
      Due to the current Corona siutation, the kickoff session, the team formation session as well as the Q&A-sessions will be held online via ZOOM. Video recordings will be provided for the other lectures. See Outline below.
    • Exercises: Students should attend one of the three exercise groups. Two exercises will be held offline and will have a restricted number of places. These places which will be assigned to interested students on a weekly basis. The third exercise will be held online via ZOOM and the amount of places in this exercise will not be restricted.
      • Thursday, 10.15 – 11.45, Room A 104 (B6 , Bauteil A)
      • Thursday, 12.00 – 13.30, online ZOOM
      • Thursday, 13.45 – 15.15, Room A 104 (B6 , Bauteil A)
  • Final exam

    • 75 % written exam
    • 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 HWS2021 will be preferred, equally ranked students will be drawn randomly.
    • You don't have to register for the Exercise.


The lectures and question-and-answer sessions set in bold are held live via ZOOM. For the other lectures, video recordings will be provided.


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

Exercise: Preprocessing/Visualization

23.02.2022Video Lecture: Cluster AnalysisExercise: Cluster Analysis
02.03.2022Video Lecture: Classification 1Exercise: Classification 
09.03.2022Video Lecture: Classification 2Exercise: Classification 
16.03.2022Video Lecture: Classification 3
Question and Answer Session 1
Exercise: Classification 
23.03.2022Video Lecture: RegressionExercise: Regression
30.03.2022Video Lecture: Text MiningExercise Text Mining
06.04.2022Video Lecture: Association Analysis
Introduction to the Student Projects 
and Group Formation

Question and Answer Session 2
Exercise Association Analysis
Preparation of Project Outlines
 - Easter Break - 
27.04.2022Feedback on Project OutlinesProject Work
04.05.2022Feedback on demandProject Work
11.05.2022Feedback on demand

Project Work

18.05.2022Feedback on demandProject Work
25.05.2022Feedback on demandProject Work
29.05.2022Submission of project reports (Deadline: 23:59) 
01.06.2022Presentation of project results
(offline, room A5, B144)
Presentation of project results 
(offline, room Schloss O151)
07.06.2022Final exam (offline, room B6 A001, 8:30)


For all students which are not familiar with Python/Jupyter Notebooks, we offer an online introduction on Wednesday, 16 February 2022 between 15:30 and 17:00 in room ZOOM-Lehre-051.

Lecture Videos, Slides and Exercises

Lecture Videos and Slides:


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