[ 921DASIVIAV17 ] VL Visual Analytics

Es ist eine neuere Version 2021W dieser LV im Curriculum Master's programme Business Informatics 2023W vorhanden.
Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS M - Master's programme Computer Science Marc Streit 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Master's programme Computer Science 2019W
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 such as data mining, data management, visualization 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 where they learn how to apply Visual Analytics skills to solve real-world visual data analysis problems.

  • Introduction to Visual Analytics,
  • Data Foundations and Management,
  • Visualization Principles I: Statistical Plots,
  • Visualization Principles II: Visualization Techniques by Data Type,
  • Visual Data Mining Principles,
  • Human-Interpretable Machine Learning,
  • VA Infrastructure: Libraries and Tools,
  • Evaluation: Quantitative & Qualitative Methods,
  • Reproducibility and Provenance,
  • Collaborative Visualization and Data-Driven Storytelling,
  • Selected Current Research and Case Studies.
Criteria for evaluation Written exam (oral exam in exceptional cases)
Methods Slide presentation with case studies
Language English
Study material
  1. Visualization Analysis and Design; Tamara Munzner; Taylor & Francis Inc., ISBN: 978-1466508910, 2014.
  2. Mastering the Information Age - Solving Problems with Visual Analytics, Daniel A. Keim, Jörn Kohlhammer, Geoffrey Ellis and Florian Mansmann, Eurographics Association, ISBN-13: 978-3-905673777, 2010. Free Download.
  3. Making Data Visual: A Practical Guide to Using Visualization for Insight, Danyel Fisher and Miriah Meyer, ISBN: 978-1491928462, 2018.
  4. Doing Data Science: Straight Talk from the Frontline, Cathy O'Neil and Rachel Schutt, 978-1449358655, 2013.
  5. Illuminating the Path: The Research and Development Agenda for Visual Analytics, James J. Thomas and Kristin A. Cook, National Visualization and Analytics Ctr, ISBN-13: 978-0769523231, 2005.
  6. Interactive Data Visualization: Foundations, Techniques, and Applications; Matthew Ward, George Grinstein and Daniel Keim, A K Peters, ISBN: 978-1568814735, 2010.
Changing subject? No
Further information
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