Inhalt

[ 921CGELVIAV13 ] VL Visual Analytics

Versionsauswahl
Es ist eine neuere Version 2017W dieser LV im Curriculum Masterstudium Computer Science 2017W vorhanden.
Workload Ausbildungslevel Studienfachbereich VerantwortlicheR Semesterstunden Anbietende Uni
3 ECTS M - Master Informatik Marc Streit 2 SSt Johannes Kepler Universität Linz
Detailinformationen
Quellcurriculum Masterstudium Computer Science 2013W
Ziele VA is highly interdisciplinary and covers fields such as data mining, data management, visualization as well as human perception and cognition. In this course students will learn how large amounts of information, such as graphs, text, tables and maps can be effectively analyzed by a user.
Lehrinhalte Introduction to visual analytics (definition, VA process, historical aspects), data foundations and management, data mining principles (clustering, PCA, etc.), visualization principles, interaction principles, VA Infrastructure (including processing frameworks like R and WEKA), quantitative & qualitative evaluation methods, biological data analysis, selected current research.
Beurteilungskriterien Schriftliche Prüfung (mündliche Prüfung in Ausnahmefällen)
Abhaltungssprache Englisch
Literatur 1. 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.

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. Interactive Data Visualization: Foundations, Techniques, and Applications; Matthew Ward, George Grinstein and Daniel Keim, A K Peters, ISBN: 978-1568814735, 2010.

Lehrinhalte wechselnd? Nein
Sonstige Informationen http://www.jku.at/cg/content/e48361/e174976/e163866/
Präsenzlehrveranstaltung
Teilungsziffer -
Zuteilungsverfahren Direktzuteilung