Data Mining (HWS 2022)

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.

  • Instructors

  • Time and Location

    • Lecture: Wednesday, 10.15 – 11.45, Room B6 A1.01
    • Exercises: Students should attend one of the three exercise groups. The contents are identical.
      • Thursday, 12.00 – 13.30, Room B6 A 1.04 (Nico Heist)
      • Thursday, 13.45 – 15.15, Room B6 A 1.04 (Alex Brinkmann)
      • Thursday, 15.30 – 17.00, Room B6 A 1.04 (Sven Hertling)
  • Final exam

    • 75 % written exam
    • 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 FSS2022 will be preferred, equally ranked students will be drawn randomly.
    • You don't have to register for the Exercise.

Lecture Videos, Slides and Exercises

Slides:

Exercises:

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

Outline

Lectures and exercises take place on campus, unless specified otherwise.

WeekWednesdayThursday
05.09.2022

Lecture: Introduction to Data Mining

Exercise: Python Intro / Preprocessing

12.09.2022Lecture: ClusteringExercise: Cluster Analysis
19.09.2022Lecture: Classification 1Exercise: Classification 
26.09.2022Lecture: Classification 2Exercise: Classification 
03.10.2022Team Project Introduction and Team BuildingProject Work (no exercise)
10.10.2022Lecture: RegressionExercise: Regression
17.10.2022Lecture: Text MiningExercise: Text Mining
24.10.2022Lecture: Association AnalysisExercise: Association Analysis
31.10.2022Project feedback sessionProject Work (no exercise)
07.11.2022Project feedback sessionProject Work
14.11.2022Project feedback session

Project Work

21.11.2022Project feedback sessionProject Work

28.11..2022

Project feedback sessionProject Work
05.12.2022Project PresentationsProject Presentations

Important project dates:

  • Project Proposal due: Monday, October 10th, 23:59
  • Project Report due: Friday, December 9th, 23:59