[ 921DASIVIAV17 ] VL Visual Analytics

Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS M1 - Master's programme 1. year Computer Science Marc Streit 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Computer Science 2021W
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 addition to the lecture, students can take an optional lab to learn how to apply VA theory to real-world visual data analysis problems.
  • 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 Written exam (oral exam in exceptional cases)
Methods Slides combined with case studies and in-class exercises.
Language English
Study material Study material will be provided during the course.
Changing subject? No
Further information The lecture can be combined with an optional practical lab. Until term 2017S known as: 921CGELVIAV13 VL Visual Analytics
Earlier variants They also cover the requirements of the curriculum (from - to)
921CGELVIAV13: VL Visual Analytics (2013W-2017S)
On-site course
Maximum number of participants -
Assignment procedure Direct assignment