Problem-Centered Data Pre-Processing
level of course unit
Learning outcomes of course unit
Students are familiar with various techniques for data pre processing, preparation and integration and are able to evaluate which of these techniques is necessary and appropriate within the context of a specific problem area. Furthermore, they are able to independently use and embed these techniques in an existing toolchain.
prerequisites and co-requisites
Students learn about the fundamental operations of data pre-processing, their usage and practical execution. Specifically in the following areas:
Students work on real examples and independently apply individual pre-processing techniques in interactive workshops.
recommended or required reading
- Runkler, T. A. (2015) Data Mining: Methoden und Algorithmen intelligenter Datenanalyse. 2. Auflage, Springer Vieweg, Wiesbaden (ISBN: 978-3834816948).
assessment methods and criteria
language of instruction
number of ECTS credits allocated
planned learning activities and teaching methods
-Lecture with discussion
semester/trimester when the course unit is delivered
name of lecturer(s)
Head of studies
year of study
recommended optional program components
course unit code
type of course unit
mode of delivery