This course is a hands-on introduction to design principles, theory, and software techniques for visualizing data. Classes will be a combination of lecture, design studio, and lab. Through readings, design critique and code assignments, students will learn how visual representations can help in the understanding of complex data, and how to design and evaluate visualizations for the purpose of analysis or communication. Students will develop skills in processing data, and building interactive visualizations using D3. Topics include visual perception, exploratory data analysis, task analysis, graphic design, narrative, etc.
|Class||Reading Due||Assigned||Due Monday-before|
|1/21||Introduction: why visualize? schedule and expectations.
(APPLY TO ENROLL by Friday)
|A2.1 Vis Design: divergence assigned|
|1/28||Designing: form vs. function, generating ideas, iterating, and critique.||• Visual Explanations, Chp. 2 Excerpt, by Tufte, E. 2007.
• How to be creative & How to be critical, Amy Ko. 2017.
• Lateral Thinking, Excerpts, Edward deBono, 1967.
• Optional: The Architecture of a Data Visualization, Accurat Studio.
|A2.2 Vis Design: revisions assigned|
|2/4||Data Models: data types, task types, corresponding visualization formats.||• Semiology of Graphics, Excerpt, Jacques Bertin, 1967.
• A Tour through the Visualization Zoo. Jeffrey Heer, Michael Bostock, and Vadim Ogievetsky. ACM. 2010.
• Visualization Analysis and Design, Chp. 2.1–2.5 by Munzner, T. 2014.
• Optional: The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations, Ben Shneiderman, 1996
|A3 Exploratory Data Analysis: assigned||A2 Design DUE|
|2/11||Data Exploration: EDA, data wrangling and Tableau.||• Chp. 6: Analytical Patterns from Now You See It by Stephen Few, 2009.
• Polaris: A System for Query, Analysis and. Visualization of Multi-dimensional Relational Databases by Stolte, C. et. al. ACM 2008.
• Postmortem of an Example, Jacques Bertin, 1981.
• Optional: A Layered Grammar of Graphics, Wickham, H. 2010.
• Optional: Bad Data Guide by Quartz data team
|2/18||Visual Encoding: marks, channels, expressiveness & effectiveness.||• Understanding Comics, Chp. 5,7,8, by Scott McCloud
• Chp. 3: The Power of Representation in Things That Make Us Smart by Norman, D. 1993.
• Visual Display of Quantitative Information, Chp. 2,4,5, by Tufte, E. 2007
• Optional: D3: Data-Driven Documents. Michael Bostock, Vadim Ogievetsky, Jeffrey Heer. InfoVis 2011.
|A4.1 Interactive: static assigned||A3 EDA DUE|
|2/25||Interaction: overview vs. details, small multiples, brushing, etc||• Interactive Dynamics for Visual Analysis. Jeffrey Heer & Ben Shneiderman. 2012.
• Ladder of Abstraction by Bret Victor, 2011.
• In Defense of Interactive Graphics, Gregor Aisch, 2017.
• Optional: Mastering Hued Color Scales, Gregor Aisch, 2013.
|A4.2 Interactive: dynamic assigned|
|3/3||Evaluation, Perception, Review: a framework for analysis; how we see, color and attention theory.||• Visualization Analysis and Design, Chp. 3.1–3.4, 4.1–4.6 by Munzner, T. 2014.
• The Design of Everyday Things, Chp.1 by Norman, D. 1988.
• Now You See It, Chp. 3 by Stephen Few, 2009.
• 39 Studies About Human Perception in 30 Minutes by Kennedy Elliott.
• Optional: Design and Redesign in Data Visualization by Viegas & Wattenberg, 2015.
|Study for midterm|
|3/10||Midterm Exam.||A4.3 Interactive: evaluation assigned|
|3/17||Spring Break, no class.|
|3/24||Animation: motion perception, transitions, pros/cons.||• Creating Usability with Motion, by Willenskomer, I. 2017
• Chp. 5: Analytical Techniques from Now You See It by Stephen Few, 2009.
• Powers of Ten(video), Charles & Ray Eames, 1977.
• Optional: Animated Transitions in Statistical Data Graphics by Heer, 2007.
|A5.1 Final: Proposals assigned||A4 Interactive DUE|
|3/31||Maps, Graphic Design, Narrative: projections; typography, rhythm; why storytelling, techniques.||• Reinventing Explanation. Michael Nielsen, 2014.
• The Making of R2D3 (video) and A Visual Introduction to Machine Learning (viz) by Chu, T. 2016.
• What to consider when creating choropleth maps by Rost, L. C. 2017.
• Optional: Narrative Visualization: Telling Stories with Data in IEEE Vis by Segal and Heer, 2010.
|A5.2 assigned||A5.1 Proposals DUE|
|4/7||Final Project In-progress Critique.||A5.2 In-Progress Presentation DUE|
|4/14||Networks, Text, Algorithms: node-link diagrams, trees, force layout; visualizing words and algorithms.||• Visualization Analysis and Design Chp 9: Networks and Trees by Munzner, T. 2014.
• Pictures of Arguments, Songs, and Ancient Texts (video, till 38’20”) by Viegas and Wattenberg, at Eyeo conference 2013.
• Visualizing Algorithms. Mike Bostock. 2014.
• Optional: Four Experiments in Handwriting with a Neural Network. Shan Carter et. al., 2016
|4/21||Ethics, Vis in Industry vs. Research: between persuasion and misrepresentation; jobs, and guest lecture by Prof. Wu on research.||• Six Provocations for Big Data by boyd & Crawford, 2011
• What Is Visualization Research? by Hullman, J.
• Optional: The Case for Data Visualization Management Systems by Wu, et. al.
|4/28||Final Project Showcase 6–8pm||A5.3 Lightning Talk DUE|
|5/4 MON||A5.4 Final Visualization DUE +
A5.5 Final Documentation DUE