ERP Systems & Business Process Management PT
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Data Visualization & Visual Analytics (elective)*

level of course unit

Master`s course

Learning outcomes of course unit

The following learning outcomes are developed in the course:

- Students will have basic knowledge of data visualization and visual communication.
- Students can develop visualizations independently and use them for communication purposes.
- Students can work with different presentation tools and presentation libraries to present data and analysis results in a meaningful way.

prerequisites and co-requisites


course contents

The following content is discussed in the course:

- Evaluation tools with visual orientation, e.g. Bl tools such as MS PowerBl, Tableau, QlikView
- Display libraries, e.g. matplotlib.pyplot, gglot2
- Rules of visual communication, e.g. Hichert SUCCESSSS

recommended or required reading

- Chang, W. (2013): R Graphics Cookbook: Practical Recipes for Visualizing Data (Ed. 1), O´Reilly, Farnham (ISBN: 978-1449316952)
- Chen, C.; Härdle, W. K.; Unwin, A. (2008): Handbook of Data Visualization (Ed. 1), Springer, Berlin (ISBN: 978-3-662-50074-3)

- Dale, K. (2016): Data Visualization with Python and Javascript: Scrape, Clean, Explore & Transform Your Data (Ed. 1), O´Reilly, Farnham (ISBN: 978-1491920510)
- Murray, S. (2017): Interactive Data Visualization for the Web: An Introduction to Designing with D3 (Ed. 2), O´Reilly, Farnham (ISBN: 978-1491921289)

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

The following methods are used:

- Lecture with discussion
- Interactive workshop
- Case studies

semester/trimester when the course unit is delivered


name of lecturer(s)

Prof. (FH) DI Dr. Martin Adam

course unit code


type of course unit

integrated lecture

mode of delivery

Compulsory elective

work placement(s)