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.
Lecture: Introduction to Data Mining
|22.02.2023||Lecture: Cluster Analysis||Exercise: Cluster Analysis|
|01.03.2023||Lecture: Classification 1||Exercise: Classification|
|08.03.2023||Lecture: Classification 2||Exercise: Classification|
|15.03.2023||Lecture: Classification 3||Exercise: Classification|
|22.03.2023||Lecture: Regression||Exercise: Regression|
|29.03.2023||Lecture: Text Mining||Exercise: Text Mining|
- Easter Break -
|19.04.2023||Introduction to the Student Projects |
and Group Formation
|Preparation of project outline|
|26.04.2023||Lecture: Association Analysis||Exercise: Association Analysis|
|03.05.2023||Feedback on project outlines||Project Work|
|10.05.2023||Feedback on demand||Project Work|
|17.05.2023||Feedback on demand||Project Work|
|24.05.2023||Feedback on demand||Project Work|
|28.05.2023||Submission of project reports (Deadline: 23:59)|
|31.05.2023||Presentation of project results|
For all students which are not familiar with Python/
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.
Aurélien Géron: Hands-On Machine Learning with Scikit-Learn and TensorFlow. O'Reilly.