Data Science & Intelligent Analytics PT
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Problem-Centered Data Pre-Processing

level of course unit

Master course

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

not specified

course contents

Students learn about the fundamental operations of data pre-processing, their usage and practical execution. Specifically in the following areas:

-Data integration
-Data scaling
-Data centering
-Data imputation
-Data recoding

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

Final examination

language of instruction

German

number of ECTS credits allocated

3

course-hours-per-week (chw)

2

planned learning activities and teaching methods

-Lecture with discussion
-Interactive workshop
-Case studies

semester/trimester when the course unit is delivered

3

name of lecturer(s)

Head of studies

year of study

2

recommended optional program components

N.A.

course unit code

DPR.6

type of course unit

ILV

mode of delivery

In-class course

work placement(s)

N.A.

Kontaktpersonen

Mitarbeiterfoto Karsten Böhm
Prof. (FH) Dipl.-Informatiker Karsten Böhm
Director of Studies
+43 5372 71819 133
Karsten.Boehmfh-kufstein.ac.at
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Förderer

Mit Unterstützung von Bund, Land und Europäischer Union: