Data Analysis and Data Mining

Short Name
Datenanalyse, Data Mining
Module Code
PI5008
Module Coordinator
  • Andreas Peter Dominik
Teacher
  • Andreas Peter Dominik
Short Description
Methods and procedures for collection and storing, processing and depiction of data from different data sources as well as statistical procedures.
Learning Objectives

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.

Contents
  • Statistical Methods
  • Knowledge Discovery und Machine Learning
  • Data editing
  • Classifications
  • Association rule learning
  • Clustering
Duration in Semester
1
Instruction Language
German
Total Effort
6 CrP; an estimated 180 hours, of which approximately 60 are spent in class.
Weekly School Hours
4
Method of Instruction
Lecture 2 sppw Exercises 2 sppw
Requirements for the awarding of Credit Points
Written exam
Evaluation Standard
according to examination regulations (§ 9)
Availability
Yearly
References
  • Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining
  • Thomas A. Runkler : Data Mining: Methoden und Algorithmen intelligenter Datenanalyse Vieweg+Teubner
Prerequisites
None