[ 921DASIVIAV17 ] VL (*)Visual Analytics

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Workload Ausbildungslevel Studienfachbereich VerantwortlicheR Semesterstunden Anbietende Uni
3 ECTS M1 - Master 1. Jahr Informatik Marc Streit 2 SSt Johannes Kepler Universität Linz
Quellcurriculum Masterstudium Computer Science 2021W
Ziele (*)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.
Lehrinhalte (*)
  • 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
Beurteilungskriterien (*)Written exam (oral exam in exceptional cases)
Lehrmethoden (*)Slides combined with case studies and in-class exercises.
Abhaltungssprache Englisch
Literatur (*)Study material will be provided during the course.
Lehrinhalte wechselnd? Nein
Sonstige Informationen (*)The lecture can be combined with an optional practical lab.
Frühere Varianten Decken ebenfalls die Anforderungen des Curriculums ab (von - bis)
921CGELVIAV13: VL Visual Analytics (2013W-2017S)
Teilungsziffer -
Zuteilungsverfahren Direktzuteilung