Inhalt

[ 921DASIVIAV24 ] VL Visualization

Versionsauswahl
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 2024W
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 addition to the lecture, students can take an optional lab to learn how to apply VA theory to real-world visual data analysis 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 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.
Earlier variants They also cover the requirements of the curriculum (from - to)
921DASIVIAV17: VL Visual Analytics (2017W-2024S)
921CGELVIAV13: VL Visual Analytics (2013W-2017S)
On-site course
Maximum number of participants -
Assignment procedure Direct assignment