Lecturers: Marc Streit, Andreas Hinterreiter, Christian Steinparz
Contact: vis-course(at)jku.at

Competences

Students have a comprehensive and practical understanding of data visualization, including all its aspects in science, math, and technology. They are trained in critical thinking to judge the many analytical, practical, and design decisions involved in this activity. Students have obtained the conceptual, theoretical, and practical capabilities to master this multidisciplinary pursuit.

In the optional practical lab, students learn to apply the theoretical concepts and foundations of visualization, visual analytics, and visual data science to real-world data and problems.

Skills

• Apply a practical data visualization design workflow to take on any data visualization challenge
• Interpret different chart types and understand what insights can be derived from them
• Judge the appropriate analytical and design decisions required for different contextual circumstances
• Create elegant and informative data visualizations that help you understand data and communicate findings
• Evaluate and decide in which situations you need to use state-of-the-art data visualization systems and libraries

Knowledge

• Familiarity with a range of contemporary data visualization techniques 
• Understanding the theories of visual perception and their relevance to data visualization 
• Understanding the main principles of good visualization design 
• Enhanced data, statistical, and graphical literacy
• Acquired a more sophisticated language for defining, describing, and evaluating visualization designs 
• Practical understanding of relevant design concepts such as color theory and user interface design
• Refined instincts of an effective analyst 
• Familiarity with state-of-the-art visualization tools and libraries 
• Solid foundation in exploratory data analysis in Python (optional lab part)
• Working knowledge of data visualization systems and libraries (optional lab part)

Criteria for Evaluation

Lecture: Written exam
Lab: Individual and group assignments

Methods

Lecture: Slides combined with case studies and in-class exercises
Lab: Tutorials on various technologies, including Python, Matplotlib, Altair, VEGA, and plot.ly; presentation and discussion of assignments

Study Material

Study material provided during the course

Language

English

Further information

The course was formerly known as Visual Analytics.