Data Mining (HWS 2018)

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:

  • Goals and Principles of Data Mining
  • Data Representation and Preprocessing
  • Clustering
  • Classification
  • Association Analysis
  • Text Mining
  • Systems and Applications (e.g. Retail, Finance, Web 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 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 review for the first and second exam of FSS2018 will take place on Friday, 28 September, at 13:00 in Room B6 C101.

Time and Location

  • Lecture: Wednesday, 10.15 – 11.45, Room A5, C0.14
  • Exercise 1: Thursday, 12.00 – 13.30, Room B6, A2.04 (Python)
  • Exercise 2: Thursday, 13.45 – 15.15, Room B6, A2.04 (RapidMiner)
  • Exercise 3: Thursday, 15.30 – 17.00, Room B6, A2.04 (Python)

Note: there are three parallel exercise groups, you are supposed to only attend one.


Final exam

  • 60 % written exam
  • 40 % project work


For attending the course, please register for the lecture in Portal 2 (link to lecture and exercise).The course is limited to 80 participants. From this semester on we will have a new process for the course registration and allocation. There will be no “first come – first serve”. Students in higher semesters will be preferred, equally ranked students will be drawn randomly. You can register from 13th of August until 29th of August.

We offer three alternative times (Thursdays 12.00, 13.45 and 15.30) for the exercise session. Choose one and attend the exercise at the corresponding time (you don't have to register for it).