Module


General information
Datenvisualisierung und Visuelle Analyse
Data Visualization and Visual Analytics
MADS-DVVA
DataVisVisAn-01-MA-M
Prof. Dr. Schwörer, Tillmann (tillmann.schwoerer@haw-kiel.de)
Prof. Dr. Schwörer, Tillmann (tillmann.schwoerer@haw-kiel.de)
Wintersemester 2019/20
1 Semester
In der Regel jedes Semester
Englisch
Curricular relevance (according to examination regulations)
Study Subject Study Specialization Study Focus Module type Semester
M.Sc. - DS - Data Science Pflichtmodul

Qualification outcome
Areas of Competence: Knowledge and Understanding; Use, application and generation of knowledge; Communication and cooperation; Scientific self-understanding / professionalism.
Students know
- a broad portfolio of visualization techniques and understand for which purpose they are most suitable,
- tools and best practices to closely integrate visual analysis, documentation, and presentation,
- Programming frameworks and BI tools for data visualization
Students are able to
- use visualizations as a means to detect patterns in complex data,
- design and develop expressive visualizations tailored to the specific purpose and recipient using programming languages and BI tools
Students are able to
- concisely present their approach and results in technical and functional terms
- work successfully in teams on joint projects, leveraging and integrating the skills of all team members.
Students are able to
- reflect on the strengths and weaknesses of visualization techniques,
- give and receive constructive critique and advice
and they adhere to principles for scientific communication.
Content information
The Role of Graphics in the Data Science Workflow
Principles of Science and Design
Mapping Data to Graphics
Visual Analytics
R and Python for Data Visualization
BI Tools for Data Visualization
- Wilke: Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. O'Reilly, first edition, online available: https://serialmentor.com/dataviz.
- Chen, Härdle, and Unwin: Handbook of Data Visualization. Springer, 2008, online available: http://www.haralick.org/DV/Handbook_of_Data_Visualization.pdf.
- Haley: Data Visualization: A Practical Introduction. Princeton University Press, first edition. online available: http://socviz.co
- Wickham: ggplot2: Elegant Graphics for Data Analysis (Use R!). Springer, second edition. Online available: https://ggplot2-book.org/.
Teaching formats of the courses
Teaching format SWS
Lehrvortrag + Übung 4
Workload
4 SWS
5,0 Credits
48 Hours
102 Hours
Module Examination
Method of Examination Duration Weighting wird angerechnet gem. § 11 Absatz 2 PVO Graded Remark
Übung 20 %
Klausur 60 Minutes 50 %
Projektbezogene Arbeiten 30 %