Overview

Course Description

Introduces principles and techniques for the visualization of content and relationships in large datasets of quantitative and symbolic data. Survey includes sense making and perceptual aspects of data visualization, including graphical methods for specialized types of data (time series, categorical data, etc.), and appropriate software and computer systems for data visualization, including interactive, dynamic graphics. Specific issues include paradigms for information display and human/computer interfaces.

This course addresses the methods and practice of interpreting and creating data visualizations. Students will review appropriate literature based in a variety of fields including cognitive science, psychology, education, and information visualization. Students will apply recommended techniques to interpret information from common data-based visualizations and learn how to create their own using a variety of software packages that may include, but not be limited to, Adobe Illustrator, SPSS, Matlab, Excel and Powerpoint.

Course Objectives

Through a combination of lectures, demonstrations and hands-on lab exercises, students learn to:

• Identify appropriate data visualization techniques given particular requirements imposed by the data.
• Interpret meaning from various multidimensional formats and presentation techniques.
• Create multiple versions of technology-based visualizations using techniques from various software packages.

Prerequisites

Experience with basic functions of the computer interface. Students are encouraged to either be in the process of preparing data or have access to data appropriate for their particular field of interest, although this is not required.

Course Format

First Part - Lecture

• Introduction / review of position within course, current day’s activities.
• Quiz / Written Response over readings for that week.
• Class discussion that reviews that week’s readings.
• Group discussion / activity to situate the reading and topic for that day.
• Data visualization presentation.

Second Part - Lab

• Putting data visualization into practice using technology.