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

Objectives

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.

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 Analysis
• 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.