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 webpage about the HWS 2019 edition of this course is found in the lecture archive.
The lectures and exercises of this course will be continued online until Easter. Depending on whether the on-site teaching at the University of Mannheim is continued after the Easter break or not, the student projects and the project coaching will take place on-site or online. We for now plan to do the project presentations and the exams on-site.
As we still need to record a video for the Regression lecture, we have shifted this online lecture to the last week before Easter and will continue next week with the lecture and exercises on Association Analysis. See updated schedule below.
Note: there are three parallel exercise groups, you are supposed to attend only one.
Additional material will be found in the ILIAS group of the course.
Introduction to Data Mining
|19.02.2020||Lecture Clustering||Exercise Clustering|
|26.02.2020||Lecture Classification 1||Exercise Classification|
|04.03.2020||Lecture Classification 2||Exercise Classification|
|11.03.2020||Lecture Classification 3||Online Exercise Classification|
|18.03.2020||Video Lecture Association Analysis||Online Exercise Association Analysis|
|25.03.2020||Video Lecture Text Mining||Online Exercise Text Mining|
|01.04.2020||Video Lecture Regression||Online Exercise Regression|
|22.04.2020||Introduction to Student Projects
and Group Formation (hopefully on-site again)
|Preparation of Project Outlines|
|29.04.2020||Feedback on Project Outlines||Project Work|
|06.05.2020||Feedback on demand||Project Work|
|13.05.2020||Feedback on demand||Project Work|
|20.05.2020||Feedback on demand||Project Work|
|27.05.2020||Presentation of project results||Presentation of project results|
For all students which are not familiar with Python/
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.
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