Visualization

Basic information

Workload: 

45 hours

Prerequisite: 

Does not exist.

Syllabus: 

The Value of Visualization, Data Abstraction, Data Types, Visual Coding and Design, Interaction Techniques, Perception, Cognition, Uncertainty, Scalar Fields, Iso-contours and iso-surfaces, Volume Rendering Integral + Optical Models, Vector Field / Flow Visualization, Integral Curves (Streamlines, Pathlines, Streaklines, Timelines), Vector Calculus, Line Integral Convolution, Tensor Field Visualization, Diffusion Tensor Imaging, Statistical Data Analysis and Modeling Methods (correlation, linear regression, clustering, pattern extraction, classification, data mining ), Monte Carlo methods.

Bibliography

Mandatory: 

· Tamara Munzner (2014). Visualization Analysis and Design. CRC Press
· Alexandru Telea. Data Visualization: Principles and Practice
· Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. An Introduction to Statistical Learning, with Applications in R.

Complementary: 

· Scott Murray (). Interactive Data Visualization for the Web, 2nd Edition., O'Reilly Press.
· Meirelles, I. (2013). Design for Information.
· Cairo, A. (2012). The Functional Art: An introduction to information graphics and visualization.
· Shiffman, D. (2008). Learning Processing. Morgan Kaufmann
· Reas, C. and Fry, B. (2010). Getting Started with Processing. O'Reilly Media.
· Reas, C. and Fry, B (2007). Processing: A Programming Handbook for Visual Designers and Artists. MIT Press.
· Ware, C. (2008). Visual Thinking for Design. Morgan Kaufman