Data visualization (DataVis) is a rich research area that focuses on the design and use of visual representations and interaction techniques to help people understand, explore, and analyze data. While fields such as scientific visualization involve the presentation of data that has some physical or geometric correspondence, data visualization focuses on abstract data without such correspondences such as symbolic, tabular, networked, hierarchical, or textual information sources.
DataVis methods are applied to data from many different application domains, including:
- News reporting – look at the interactive visualizations used by the New York Times, Wall Street Journal, Slate, etc.
- Political reporting and forecasting – as seen on TV and in the papers in election season.
- Social science and economics data, such as census and other surveys, and micro and macro economic trends.
- Social networking and web traffic, to understand patterns of communication
- Business intelligence and business dashboards – to forecast sales trends, understand competitive marketplace positions, allocate resources, manage production and logistics.
- Text analysis – to determine trends and relationships for literary analysis and for information retrieval.
- Criminal investigations – to portray the relationships between event, people, places and things.
- Performance analysis of computer networks and systems.
- Software engineering – developing, debugging and maintaining software.
- Bio- and health informatics – to understand DNA, gene expressions, systems biology, and healthcare delivery.
The objectives of this course are:
- Learn fundamental principles of effective data visualization.
- Understand the wide variety of data visualization techniques and know what visualizations are appropriate for various types of data and for different goals.
- Understand how to design and implement data visualizations using commercial and open-source software tools.
- Know how data visualization uses dynamic interaction methods to help users explore, analyze, and make sense of data.
- Gain an understanding of human perceptual and cognitive capabilities to the design of effective data visualizations.
- Develop skills in critiquing different data visualization techniques in the context of user goals and objectives.
The course will follow a lecture/seminar style with discussions, guest speakers from industry and academia, viewing of best-practice videos, and hands-on experience with DataVis design and development. Download the syllabus here, or view it online here.
Grading
Grading will be based on class participation, homeworks, assignments involving design, use and analysis of data visualization tools, cumulative exams, and a team-based semester project.
Class Participation: It is expected that students will come to class, be prepared by doing the readings, and will pay attention and actively participate in discussions and design exercises. Doing all three regularly will earn full credit. If you want to surf the Internet on your laptop in class, take another course. I will cold-call on students, so pay attention.
Late Turn-In of Assignments: For each class period late, 25% of the total grade will be deducted from an assignment’s score.
Text
- Tamara Munzer, Visualization Analysis and Design (2014)
- Stephen Few, Now You See It: Simple Visualization Techniques for Quantitative Analysis (2009)
- Marti Hearst, Search User Interfaces (2009)
- Edward Tufte, The Visual Display of Quantitative Information (2001)
Other Texts:
For those interested in design: Any of Edward Tufte’s other two books: Envisioning Information; and Visual Explanations.
For those interested in business intelligence and business dashboards: Wayne Eckerson, Performance Dashboards: Measuring, Monitoring, and Managing Your Business, Wiley, 2005, ISBN 978-0471724179
For those interested in Network Visualization, particularly Social Networks: Hansen, Shneiderman and Smith, Analyzing Social Media Networks with NodeXL, Morgan Kaufman, 2011, ISBN 978-0-12-382229-1.
For those interested in the psychological/perceptual factors affecting information visualization: Colin Ware, Information Visualization: Perception for Design, 2nd Edition, Morgan Kaufman Elsevier 2004, ISBN 978-1558608191.
Students from a variety of disciplines are invited to take the class, but some prior background in human-computer interaction will be helpful. Programming experience is not required but will be useful.
Materials
- Pencil/Eraser
- Set of Colored Pencils (12)
- Ruler
- Blank Paper
Collaboration and Academic Honesty
Unless explicitly stated otherwise, you are expected to do your homework on your own. Your project work may borrow libraries and code fragments from sources on the web that you integrate into an overall working system. Your source code should indicate what code is imported and used as is, what code is imported and modified, and what code is original. It is appropriate to discuss your project with others to gain ideas and feedback and help with sticky problems. It is not appropriate to find an data visualization system, modify it and submit it as your own work. If in doubt, confer with your instructor.
Software
Many assignments will be done using Tableau. Tableau’s data visualization software is provided through the Tableau for Teaching program. Please download and install the latest version of Tableau. The course-only key needed to activate the software is list on T-Square.
Instructions: Click on the link above and select Get Started. On the form, enter your university email address for “Business email”; and under “Organization”, please input the name of your school.
DataVis 