Data Mining (HWS 2026)
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.
No Registration for Exercise
No registration for the exercises is necessary!
Once you are registered for the lecture, you can freely choose which exercise session you would like to attend.
You only need to attend one exercise session, as all exercise sessions cover the same content.
No lecture in the first week
Due to the course assignments, there will be no lecture in the first week (September 7th 2026).
Exam Review (FSS2026)
The exam review for FSS2026 will take place on September 23rd 2026, starting from 14:00.
You have to register for the exam review by filling out the Google Forms until September 20th, 2026.
During the exam review, you may also request a detailed breakdown of your grade, including the individual scores for the presentation, report, and written exam. For privacy reasons, this information is only provided during the exam review.
Instructors
- Sven Hertling (Lecture)
- Franz Krause (Exercise and Project)
- Andreea Iana (Exercise and Project)
Time and Location
- Lecture: Monday, 13:45 – 15:15, Room A001 Building B6 Part A
- Exercises: Students should attend one of the three exercise groups. The contents are identical.
- Thursday, 12:00 – 13:30, Room A104 Building B6, 26 Part A
- Thursday, 13:45 – 15:15, Room A203 Building B6, 26 Part A
- Thursday, 15:30 – 17:00, Room A104 Building B6, 26 Part A
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
| Week | Monday(Offline Lecture, Room A001) | Online Lecture (see Ilias Course) | Thursday (Exercise) |
| 07.09.2026 | no lecture | ||
| 14.09.2026 | Introduction to Data Mining | Introduction to Python | |
| 21.09.2026 | Preprocessing | Preprocessing | |
| 28.09.2026 | Classification 1 + Intro to Student Project | Nearest Centroids | Classification 1 |
| 05.10.2026 | Classification 2 | Comparing Classifiers | Classification 2 |
| 12.10.2026 | Regression | Ensembles | Regression |
| 19.10.2026 | Clustering and Anomalies | Hierarchical Clustering | Clustering and Anomalies |
| 26.10.2026 | Feedback on project outlines (no lecture) | Time Series | Time Series |
| 02.11.2026 | Association Analysis and Subgroup Discovery | Multi Modal Data | Association Analysis |
| 09.11.2026 | Project feedback session | Project Work | |
| 16.11.2026 | Project feedback session | Project Work | |
| 23.11.2026 | Project feedback session | Project Work | |
| 30.11.2026 | Project feedback session | Project Work | |
| 07.12.2026 | Q&A | Project Presentations |
Important dates for the student projects:
- Monday, October 5th, 23:59 Deadline for team formation (all students without a team will be assigned afterwards)
- Wednesday, October 14th, 23:59: Submission of project outlines
- Sunday, December 6th, 23:59: Submission of final project reports
- Wednesday, December 9th, 23:59 Submission of project presentation (PDF)
Course Evaluation
Results of the course evaluation (PDF, 599 kB) (Spring Semester 2026)
Literature
Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar: Introduction to Data Mining, 2nd Global Edition, Pearson.
Aurélien Géron: Hands-On Machine Learning with Scikit-Learn and TensorFlow. O'Reilly.
Software
Videos and Screen Casts
- Video recordings of the Data Mining I lectures and screen casts of the exercises are available here.