Lecturer: Marc Streit, Christian Steinparz, Andreas Hinterreiter
Contact: va-course@jku.at

Objectives

The aim of this module is to equip you with a comprehensive and practical understanding of data visualization: a multi-disciplinary recipe of science, math, technology and many other interesting ingredients. The emphasis of the course is to instil the necessary critical thinking required to best judge the many analytical, practical and design decisions involved in this activity.  
The course will offer a blend of academic and applied perspectives, covering the full suite of conceptual, theoretical and practical capabilities required to master this multidisciplinary pursuit.

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.

Subject

• Introduction and Course Overview
• Data Foundations and Tasks
• Visualization Principles I-III
• Fundamental Concepts: View & Interaction
• Visual Data Mining Principles
• Quantitative and Qualitative Evaluation
• Collaborative Visualization and Storytelling
• Visual Game Analytics
• Selected Current Research and Case Studies

Criteria for Evaluation

Lecture: Written exam (oral exam in exceptional cases).
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, plot.ly, and Tableau; presentation and discussion of assignments.

Study Material

Study material will be provided during the course.

Further Information

The lecture can be combined with an optional practical lab.