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 exam review for the exam of FSS2019 will take place on Friday, September 27th, 14:00, building B6, 26 room C1.01 (use the blue door and go to the first floor). There is no second exam for FSS2019. The next oportunity to retake the project and exam is in HWS2019.
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||Project Work||Feedback on demand|
|11.11.2019||Project Work||Feedback on demand|
|18.11.2019||Project Work||Feedback on demand|
|25.11.2019||Project Work||Presentation of project results|
|02.12.2019||Submission 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.
Tracking cookies are currently allowed.
Tracking cookies are currently not allowed.