Data Science & Intelligent Analytics PT
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Quantitative Process & Quality Management (Six Sigma) (elective)

level of course unit

Master's course

Learning outcomes of course unit

- know the basics of descriptive and conclusive statistics.
- know how to examine measuring arrangements for repeatability and reproducibility.
- know how to calculate sample sizes.
- know how to monitor the stability of process results using statistical monitoring methods.
- know how to evaluate the ability of processes to meet customer requirements.
- know methods to search for the deviation causes in results using test procedures.
- know basic functionalities of the "Minitab" statistics software.
- know how to use "Minitab" in the context of process analysis.

prerequisites and co-requisites

not applicable

course contents

The following content is discussed in the course:

- Basics of descriptive statistics
- Measurement system analysis
- Sample determination
- Statistical process monitoring
- Process monitoring charts
- Process capability analysis
- Components of Variants Analysis (COV)
- Repetition Basics of inferential statistics
- Failure cause determination via hypothesis testing (T-test, Chi-Sq, ANOVA)
- Multiple regression analysis

recommended or required reading

Töpferer, A.; Six Sigma Konzeption und Erfolgsbeispiele für praktizierende Null-Fehler-Qualität; Berlin/Heidelberg/New York 2007; 4th edition
George M.; Rowlands D.; Price M.; Maxey J.; The Lean Six Sigma Pocket Toolbook; New York; 2005
Lunau St. (publisher); Six Sigma + Lean Toolset; 5th edition; Heidelberg; 2014

assessment methods and criteria

Seminar thesis

language of instruction


number of ECTS credits allocated


eLearning quota in percent


course-hours-per-week (chw)


planned learning activities and teaching methods

Lecture, individual work with software, group work, presentation and discussion of tasks

semester/trimester when the course unit is delivered


name of lecturer(s)

Prof. (FH) Dipl.-Informatiker Karsten Böhm

year of study

2.year of studies

recommended optional program components

not applicable

course unit code


type of course unit

integrated lecture

mode of delivery


work placement(s)