Data Mining (FFS 2025)

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
  • Classification
  • Regression
  • Clustering
  • Association Analysis

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 libraries on realistic data sets. The team projects take place in the last third of the term. Within the projects, groups of 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.

Python Introduction

For all students which are not familiar with Python/Jupyter Notebooks, we offer an introduction on Thursday, 13 February 2025 between 13:45 and 15:15 in exercise room A104 Building B6, 26 Part A.

If you want to join the Python intro and cannot make it for the specified timeslot, please write a mail to Ezgi Yilmaz with your preferred timeslot (10.15–11:45 or 12:00–13:30).

  • Instructors

  • Time and Location

    • Lecture: Wednesday, 10.15 – 11.45, Room B144 Building A 5,6 Part B (Start: 19.02.2025)
    • Exercises: Students should attend one of the three exercise groups. The contents are identical.
      • Thursday, 10.15 – 11.45, A104 Building B6, 26 Part A (Aaron)
      • Thursday, 12.00 – 13.30, A104 Building B6, 26 Part A (Andreea/Franz)
      • Thursday, 13.45 – 15.15, A104 Building B6, 26 Part A (Andreea/Franz)
  • 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 will be preferred, equally ranked students will be drawn randomly.
    • You don't have to register for the Exercise.

Outline and Course Materials

WeekWednesday (Lecture)Thursday (Exercise)
10.02.2025

no lecture

Introduction to Python (13:45–15:15)
17.02.2025Introduction to Data MiningIntro
24.02.2025PreprocessingPreprocessing
03.03.2025Classification 1Classification 1
10.03.2025Classification 2Classification 2
17.03.2025RegressionRegression
24.03.2025Clustering and AnomaliesClustering
31.03.2025Feedback on project outlinesProject Work
07.04.2025Association Analysis and Subgroup DiscoveryAssociation Analysis
14.04.2025Easter BreakEaster Break
21.04.2025Easter BreakEaster Break
28.04.2025Project feedback sessionPublic holiday
05.05.2025Project feedback sessionProject Work
12.05.2025Project feedback sessionProject Work
19.05.2025Project WorkProject Presentations
26.05.2025Q&APublic holiday

Important dates for the student projects:

  • Sundy, March, 16th, 23:56 Deadline for team formation (all students without a team will be assigned afterwards)
  • Sunday, March, 23rd, 23:59: Submission of project outlines
  • Sunday, May 18th, 23:59: Submission of final project reports
  • Wednesday, May 21st, 23:59 Submission of project presentation (PDF)