Data Analysis and Data Mining
Students will understand methods and techniques to acquire, store, edit, process and display data from very large data sources, such as the internet, process control systems, business databases, bioinformatic applications, etc., to eventually extract "knowledge".They will learn to use statistical methods, such as correlation and regression analysis, as well as cluster analysis, genetic algorithms, neuro-informatics and machine learning. Students are able to select appropriate procedures, to discuss their advantages and disadvantages as well as to justify the application.
- Statistical Methods
- Knowledge Discovery und Machine Learning
- Data editing
- Association rule learning
- Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining
- Thomas A. Runkler : Data Mining: Methoden und Algorithmen intelligenter Datenanalyse Vieweg+Teubner