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 review for the first and second exam will take place on Friday, 28 September, at 13:00 in Room B6 C101.
Note: there are three parallel exercise groups, you are supposed to only attend one.
|14.02.2018||Introduction to Data Mining||Introduction to RapidMiner/|
|21.02.2018||Lecture Clustering||Exercise Clustering|
|28.02.2018||Lecture Classification 1||Exercise Classification|
|07.03.2018||Lecture Classification 2||Exercise Classification|
|14.03.2018||Lecture Classification 3||Exercise Classification|
|21.03.2018||Lecture Regression||Exercise Regression|
|– Easter Break -|
|11.04.2018||Lecture Text Mining||Exercise Text Mining|
|18.04.2018||Introduction to Student Projects |
and Group Formation (Attendance obliatory)
|Preparation of Project Outlines|
|25.04.2018||Lecture Association Analysis||Exercise Association Analysis|
|02.05.2018||Project Work||Feedback on demand|
|09.05.2018||Project Work||Feedback on demand|
|16.05.2018||Project Work||Feedback on demand|
|21.05.2018||Project Work||Submission of project results|
|24.05.2018||-||Presentation of project results|