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.
Note: there are three parallel exercise groups, you are supposed to attend only one.
Solutions and additional material can be found in the ILIAS group of the course.
For all students which are not familiar with Python/
Introduction to Data Mining
Introduction to Python (see above)
|09.09.2019||Lecture Clustering||Exercise Clustering|
|16.09.2019||Lecture Classification 1||Exercise Classification|
|23.09.2019||Lecture Classification 2||Exercise Classification|
|30.09.2019||Lecture Classification 3||Holiday (no exercise)|
|07.10.2019||Lecture Regression||Exercise Regression|
|14.10.2019||Lecture Text Mining||Exercise Text Mining|
|21.10.2019||Lecture Association Analysis||Exercise Association Analysis|
|28.10.2019||Introduction to Student Projects |
and Group Formation (Attendance obligatory)
|Preparation of Project Outlines|
|04.11.2019||Feedback on demand||Project Work|
|11.11.2019||Feedback on demand||Project Work|
|18.11.2019||Feedback on demand||Project Work|
|25.11.2019||Submission of project results||Presentation of project results|
|02.12.2019||Presentation of project results|
Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, 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.
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