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Content

Visual Analytics

Lecturer: Marc Streit

Introduction
Visual Analytics (VA) can be defined as the science of analytical reasoning supported by interactive visual interfaces [1].
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.

In addition to the lecture, students can take a lab where they learn how to apply the Visual Analytics skills to solve real-world data analysis problems.

Contents
In particular, this course will discuss the following constitutive topics:

  • Introduction to Visual Analytics (Definition, VA Process, Historical Aspects)
  • Data Foundations and Management
  • Data Mining Principles (Clustering, PCA, SOM, etc.)
  • Visualization Principles
  • Interaction Principles
  • VA Infrastructure (including Processing Frameworks like R and WEKA)
  • Quantitative & Qualitative Evaluation Methods
  • VAST'12 Challenge: Dataset Introduction & Discussion of Solutions
  • The Cancer Genome Atlas Project: Challenges & Current State
  • Selected Current Research

Selected Readings

  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.
  4. Visualization Analysis and Design; Tamara Munzner; Taylor & Francis Inc., ISBN: 978-1466508910, 2014.