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.
Additional material (e.g., exercise solutions) can be found in the ILIAS group of the course.
Lectures and exercises take place on campus, unless specified otherwise.
Lecture: Introduction to Data Mining
Exercise: Python Intro / Preprocessing
|12.09.2022||Lecture: Clustering||Exercise: Cluster Analysis|
|19.09.2022||Lecture: Classification 1||Exercise: Classification|
|26.09.2022||Lecture: Classification 2||Exercise: Classification|
|03.10.2022||Team Project Introduction and Team Building||Project Work (no exercise)|
|10.10.2022||Lecture: Regression||Exercise: Regression|
|17.10.2022||Lecture: Text Mining||Exercise: Text Mining|
|24.10.2022||Lecture: Association Analysis||Exercise: Association Analysis|
|31.10.2022||Project feedback session||Project Work (no exercise)|
|07.11.2022||Project feedback session||Project Work|
|14.11.2022||Project feedback session|
|21.11.2022||Project feedback session||Project Work|
|Project feedback session||Project Work|
|05.12.2022||Project Presentations||Project Presentations|
Important project dates:
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.