Modul


Allgemeine Informationen
Data Visualization and Visual Analytics
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 2022/23
1 Semester
In der Regel jedes Semester
Englisch
Studiengänge und Art des Moduls (gemäß Prüfungsordnung)
Studiengang Vertiefungsrichtung Schwerpunkt Modulart Fachsemester
M.Sc. - DS - Data Science Pflichtmodul

Kompetenzen / Lernergebnisse
Kompetenzbereiche: Wissen und Verstehen; Einsatz, Anwendung und Erzeugung von Wissen; Kommunikation und Kooperation; Wissenschaftliches Selbstverständnis/Professionalität.
Students know
- available 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 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
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.
Angaben zum Inhalt
R essentials
- RStudio and RMarkdown
- Data acquistion and exploration with tidyverse
- Data visualization packages

Principles of Data Visualization
- Grammar of Graphics
- Visual perception and visual design
- Storytelling

Applications
- Raw data
- Statistical plots
- Time Series
- Geo spatial data
- PCA and regression output

Interactive visualization
- Java-script based R libraries
- Shiny
Baumer, B., Kaplan, D. and Horton, N. (2017): Modern Data Science with R. 2. Auflage. Taylor & Francis Inc.
Wilke: Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. O'Reilly, first edition, online available: https://serialmentor.com/dataviz.
Wickham, H. (2016): ggplot2: Elegant Graphics for Data Analysis (Use R!). 2. Auflage. Springer.
Wickham, H. (2021): Mastering Shiny: Build Interactive Apps, Reports, and Dashboards Powered by R. 1. Auflage. O'Reilly UK Ltd.
Lehrformen der Lehrveranstaltungen
Lehrform SWS
Lehrvortrag + Übung 4
Arbeitsaufwand
4 SWS
5,0 Leistungspunkte
48 Stunden
102 Stunden
Modulprüfung
Prüfungsform Dauer Gewichtung wird angerechnet gem. § 11 Absatz 2 PVO Benotet Anmerkung
Portfolioprüfung 100 %
Sonstiges
Basic knowledge of a programming language such as R or Python.