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 FSS2023 will take place on Wednesday, 13 September 2023, starting from 11:00.
You have to register for the exam review by writing a mail to Alexander Brinkmann until Tuesday, 6 September 2023.
Week | Wednesday | Thursday |
04.09.2023 | Lecture: Introduction | Exercise: Introduction to Python / Preprocessing & Visualization |
11.09.2023 | Lecture: Clustering | Exercise: Clustering |
18.09.2023 | Lecture: Classification I | Exercise: Classification I |
25.09.2023 | Kick Off Team Project | -- |
02.10.2023 | Lecture: Classification II | Exercise: Classification II |
09.10.2023 | Lecture: Regression | Exercise: Regression |
16.10.2023 | Lecture: Text Mining | Exercise: Text Mining |
23.10.2023 | Lecture: Association Analysis | Exercise: Association Analysis |
30.10.2023 | Team Project Feedback | -- |
06.11.2023 | Team Project Feedback | -- |
13.11.2023 | Team Project Feedback | -- |
20.11.2023 | Team Project Feedback | -- |
27.11.2023 | Results Presentation | -- |
04.12.2023 | Results Presentation | -- |
Important project dates:
Lecture slides:
Exercise material (e.g., slides, solutions) can be found in the ILIAS group of the course.
Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar: Introduction to Data Mining, 2nd Global Edition, Pearson.
Aurélien Géron: Hands-On Machine Learning with Scikit-Learn and TensorFlow. O'Reilly.