Photo credit: Anna Logue

Data Mining (FSS 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
  • Regression
  • 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 will take place on Friday, September 28th, at 13:00 in Room B6 C101.

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 (RapidMiner)

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

Final exam

  • 60 % written exam
  • 40 % project work
  • Contact Person

    • If you have any questions, please contact Oliver Lehmberg (oli(at)
  • Registration

    • The course is open to students of the Master Business Informatics, Lehr­amt Informatik and Mannheim Master in Data Science (MMDS).
    • The course is restricted to 80 participants.
    • Registration will be opened Wednesday, February 7th 2018, 10:15 am.
    • Registration is done via ILIAS using this link (once the registration is open)
    • Allocation of places is done by FCFS (limit  80 students)
    • We offer three alternative times (Thursdays 12.00, 13.45 and 15.30) for the exercise session. Sign-In to one of the three groups within ILIAS after you have registered for the course. The groups are restricted to 30 students each.
  • Outline

    Week Wednesday Thursday
    14.02.2018 Introduction to Data Mining Introduction to RapidMiner/Python
    21.02.2018 Lecture Clustering Exercise Clustering
    28.02.2018 Lecture Classification 1 Exercise Classification 
    07.03.2018 Lecture Classification 2 Exercise Classification 
    14.03.2018 Lecture Classification 3 Exercise Classification 
    21.03.2018 Lecture Regression Exercise Regression
       - Easter Break -  
    11.04.2018 Lecture Text Mining  Exercise Text Mining
    18.04.2018 Introduction to Student Projects 
    and Group Formation (Attendance obliatory)
    Preparation of Project Outlines
    25.04.2018 Lecture Association Analysis Exercise Association Analysis
    02.05.2018 Project Work Feedback on demand
    09.05.2018 Project Work Feedback on demand
    16.05.2018 Project Work Feedback on demand
    21.05.2018 Project Work Submission of project results
    24.05.2018 - Presentation of project results
  • Literature

    1. Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Pearson.
    2. Vijay Kotu, Bala Deshpande: Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner. Morgan Kaufmann.
  • Software

  • Videos and Screen Casts

    Video recordings of the Data Mining I lectures and screen casts of the exercises are available here.