Description | LMS IIB DARMAJAYA

The world is awash with increasing amounts of data, and we must keep a float with our relatively constant perceptual and cognitive abilities. Visualization provides one means of combating information overload, as a well-designed visual encoding can supplant cognitive calculations with simpler perceptual inferences and improve comprehension, memory, and decision making. Furthermore, visual representations may help engage more diverse audiences in the process of analytic thinking.

In this course we will study techniques and algorithms for creating effective visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science. The course is targeted both towards students interested in using visualization in their own work, as well as students interested in building better visualization tools and systems.

In addition to participating in class discussions, students will have to complete several short programming and data analysis assignments as well as a final project.

There are no prerequisites for the class and the class is open to graduate students. Basic working knowledge of, or willingness to learn, graphics/visualization tools (e.g., D3, HTML5, OpenGL, etc) and data analysis tools (e.g., R, Tableau, Matlab, Excel) will be useful.

Textbooks

Visualization Analysis & Design by Tamara Munzner (2014)
The Visual Display of Quantitative Information (2nd Edition). E. Tufte. Graphics Press, 2001.
Envisioning Information, E. Tufte. Graphics Press, 1990.

Reference Material (optional, but awesome):

Interactive Data Visualization for the Web by Scott Murray 2nd Edition (2017)
D3.js in Action by Elijah Meeks 2nd Edition (2017)
Semiology of Graphics by Jacques Bertin (2010)
The Grammar of Graphics by Leland Wilkinson
ggplot2 Elegant Graphics for Data Analysis by Hadley Wickham

Learning Goals & Objectives

This course is designed to provide students with the foundations necessary for understanding and extending the current state of the art in data visualization. By the end of the course, students will have gained:

An understanding of the key techniques and theory used in visualization, including data models, graphical perception and techniques for visual encoding and interaction.
Exposure to a number of common data domains and corresponding analysis tasks, including multivariate data, networks, text and cartography.
Practical experience building and evaluating visualization systems.
The ability to read and discuss research papers from the visualization literature.

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