Data Visualization and Visual Analytics (elective) (WP)*
Niveau
Master
Learning outcomes of the courses/module
The following learning outcomes are developed in the course:
- Students have basic knowledge of data visualization and visual communication.
- Students can develop visualizations independently and use them for communication purpos-es.
- Students can work with different presentation tools and presentation libraries to present data and analysis results in a meaningful way.
Prerequisites for the course
none
Course content
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 specialist literature
PRIMARY LITERATURE:
- 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)
SECONDARY LITERATURE:
- Dale, K. (2016): Data Visualization with Python and Javascript: Scrape, Clean, Explore and 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
Written exam or seminar thesis
Language
German
Number of ECTS credits awarded
4
Share of e-learning in %
15
Semester hours per week
2.0
Planned teaching and learning method
The following methods are used:
- Lecture with discussion
- Interactive workshop
- Case studies
Semester/trimester in which the course/module is offered
3
Name of lecturer
Prof. (FH) Dr. Michael Kohlegger
Academic year
Key figure of the course/module
WPF.10
Type of course/module
integrated lecture
Type of course
Compulsory elective