Lecturer: Marc Streit, Christian Steinparz, Andreas Hinterreiter
Visual Analytics (VA) can be defined as the science of analytical reasoning supported by interactive visual interfaces. VA is highly interdisciplinary and combines fields including visualization, data mining, data management, as well as human perception and cognition. In this course, students will learn how large and complex data, such as tables, networks, and text, can be effectively explored and analyzed using interactive means.
In the optional practical lab, students learn how to apply the theoretical concepts and foundations of VA, visual data science, and visualization to real-world data and problems.
Lecture: Written exam (oral exam in exceptional cases).
Lab: Individual and group assignments.
Lecture: Slides combined with case studies and in-class exercises.
Lab: Tutorials on various technologies, including Python, Matplotlib, Altair, VEGA, plot.ly, and Tableau; presentation and discussion of assignments.
Study material will be provided during the course.
The lecture can be combined with an optional practical lab.