Photo credit: Anna Logue

Data Mining (FSS 2019)

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 exam review for the first and second exam of HWS2018 will take place on Monday, February 18th, 13:30-15:00, building B6, 26 room C1.01 (use the blue door and go to the first floor).

  • Time and Location

    • Lecture: Wednesday, 10.15 - 11.45, Room A5, B144
    • Exercise 1: Thursday, 10.15 - 11.45, Room B6, A1.04 (RapidMiner)
    • Exercise 2: Thursday, 12.00 - 13.30, Room B6, A1.04 (Python)
    • Exercise 3: Thursday, 13.45 - 15.15, Room B6, A1.04 (Python)

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

  • Instructors

  • Final exam

    • 60 % written exam
    • 40 % project work
  • Registration

    • For attending the course, please register for the lecture in Portal 2. The course is limited to 80 participants. There will be no „first come - first serve“. Students in higher semesters will be preferred, equally ranked students will be drawn randomly.
    • 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).

Slides and Exercises

The lecture slide sets and exercises will be published here every week.

  1. Slideset: Introduction and Organization
  2. Slideset: Cluster Analysis


  1. Exercise 01: Python (slides | Introduction to Python | Task) | RapidMiner (slides) | Datasets & Task
  2. Exercise 02: Python (Notebooks) | RapidMiner (slides) | Datasets & Task


Week Wednesday Thursday
13.02.2019 Introduction to Data Mining Introduction to RapidMiner/Python
20.02.2019 Lecture Clustering Exercise Clustering
27.02.2019 Lecture Classification 1 Exercise Classification 
06.03.2019 Lecture Classification 2 Exercise Classification 
13.03.2019 Lecture Classification 3 Exercise Classification 
20.03.2019 Lecture Regression Exercise Regression
27.03.2019 Lecture Text Mining  Exercise Text Mining
03.04.2019 Introduction to Student Projects 
and Group Formation (Attendance obliatory)
Preparation of Project Outlines
10.04.2019 Lecture Association Analysis Exercise Association Analysis
  - Easter Break -   
01.05.2019 - Holiday - Feedback on demand
08.05.2019 Project Work Feedback on demand
15.05.2019 Project Work Feedback on demand
22.05.2019 Project Work Submission of project results
29.05.2018 Presentation of project results - Holiday -