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 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.
The lectures and question-and-answer sessions set in bold are held live via ZOOM. For the other lectures, video recordings will be provided.
Lecture: Introduction to Data Mining
|23.02.2022||Video Lecture: Cluster Analysis||Exercise: Cluster Analysis|
|02.03.2022||Video Lecture: Classification 1||Exercise: Classification|
|09.03.2022||Video Lecture: Classification 2||Exercise: Classification|
|16.03.2022||Video Lecture: Classification 3|
Question and Answer Session 1
|23.03.2022||Video Lecture: Regression||Exercise: Regression|
|30.03.2022||Video Lecture: Text Mining||Exercise Text Mining|
|06.04.2022||Video Lecture: Association Analysis|
Introduction to the Student Projects
and Group Formation
Question and Answer Session 2
|Exercise Association Analysis|
Preparation of Project Outlines
|- Easter Break -|
|27.04.2022||Feedback on Project Outlines||Project Work|
|04.05.2022||Feedback on demand||Project Work|
|11.05.2022||Feedback on demand|
|18.05.2022||Feedback on demand||Project Work|
|25.05.2022||Feedback on demand||Project Work|
|29.05.2022||Submission of project reports (Deadline: 23:59)|
|01.06.2022||Presentation of project results|
(offline, room A5, B144)
|Presentation of project results |
(offline, room Schloss O151)
|07.06.2022||Final exam (offline, room B6 A001, 8:30)|
For all students which are not familiar with Python/
Lecture Videos and Slides:
Additional material will be found in the ILIAS group of the course.
Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar: Introduction to Data Mining, 2nd Global Edition, Pearson.
Vijay Kotu, Bala Deshpande: Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner. Morgan Kaufmann.
Aurélien Géron: Hands-On Machine Learning with Scikit-Learn and TensorFlow. O'Reilly.