Data Mining (HWS2023)

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
  • Clustering
  • Classification
  • Regression
  • Association Analysis
  • Text Mining

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 FSS2023 will take place on Wednesday, 13 September 2023, starting from 11:00.

You have to register for the exam review by writing a mail to Alexander Brinkmann until Tuesday, 6 September 2023. 

  • Instructors

  • Time and Location

    • Lecture: Wednesday, 10.15 – 11.45, Room SN 169 (Castle Schneckenhof Nord)
    • Exercises: Students should attend one of the three exercise groups. The contents are identical.
      • Thursday, 12.00 – 13.30, Room B6 D0.07 (with Nico)
      • Thursday, 13.45 – 15.15, Room B6 D0.07 (with Ralph)
      • Thursday, 15.30 – 17.00, Room B6 D0.07 (with Andreea)
  • 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 in FSS 2023 will be preferred, equally ranked students will be drawn randomly.
    • You don't have to register for the Exercise.
  • Outline


    Lecture: Introduction

    Exercise: Introduction to Python / Preprocessing & Visualization

    11.09.2023Lecture: ClusteringExercise: Clustering
    18.09.2023Lecture: Classification I  Exercise: Classification I
    25.09.2023Kick Off Team Project--
    02.10.2023Lecture: Classification II Exercise: Classification II
    09.10.2023Lecture: RegressionExercise: Regression
    16.10.2023Lecture: Text MiningExercise: Text Mining
    23.10.2023Lecture: Association AnalysisExercise: Association Analysis
    30.10.2023Team Project Feedback


    06.11.2023Team Project Feedback


    13.11.2023Team Project Feedback


    20.11.2023Team Project Feedback


    27.11.2023Results Presentation--
    04.12.2023Results Presentation--

    Important project dates:

    • Project Proposal due: Sunday, October 8th, 23:59
    • Project Report due: Friday, December 8th, 23:59

Lecture Videos and Slides

Lecture slides:

Exercise material (e.g., slides, solutions) can be found in the ILIAS group of the course.