COMS W 4995 006 (3 pts)
Instructor: Agnes Chang (ac3882), office hours by appt.
IAs: Conder Shou (cs3544; Mon. 3-5p), Jeevan Farias (jtf2126; Wed. 10:30-12:30)
Class Time: Tues. 6:10-8pm
Room: 963 EXT Schermerhorn
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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.
Students should have experience in JavaScript programming and web development, as well as familiarity with databases and data formats. You should be comfortable picking up new programming tools on your own. Experience in Python or R for data processing is helpful but not required.
| Class | Reading | Assigned | Due | |
|---|---|---|---|---|
| 1/22 | Introduction: why visualize? schedule and expectations. (slides) (APPLY TO ENROLL) |
• Visual Explanations, Chp. 2 Excerpt, by Tufte, E. 2007. • How to be creative & How to be critical, Andrew Ko. 2017. • Lateral Thinking, Excerpts, Edward deBono, 1967. |
A2.1 Vis Design: divergence assigned | |
| 1/29 | Designing: form vs. function, generating ideas, iterating, and critique. (slides) (quiz) |
• 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 |
A2.2 Vis Design: revisions assigned | |
| 2/5 | Data Models: data types, task types, corresponding visualization formats. (slides) (quiz) |
• 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 |
A3 Exploratory Data Analysis: assigned | A2 Design DUE |
| 2/12 | Data Exploration: EDA, data wrangling and Tableau. (slides) (quiz) |
• 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. |
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| 2/19 | Visual Encoding: marks, channels, expressiveness & effectiveness. (slides) (quiz) |
• 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. |
A4.1 Interactive: static assigned | A3 EDA DUE |
| 2/26 | Interaction: overview vs. details, small multiples, brushing, etc. (slides) (quiz) |
• 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. |
A4.2 Interactive: dynamic assigned | |
| 3/5 | Evaluation and Perception: a framework for analysis; how we see, color and attention theory. (slides) (quiz) |
Study for midterm | ||
| 3/12 | Midterm Exam. (final project inspiration) |
• 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: The Making of R2D3(video) by Chu, T. 2016. • Optional: Animated Transitions in Statistical Data Graphics by Heer, 2007. |
A5.1 Final Project: Proposals assigned A4.3 Interactive: evaluation assigned |
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| 3/19 | Spring break, no class. | |||
| 3/26 | Animation: motion perception, transitions, pros/cons. (slides) (quiz) |
• Reinventing Explanation. Michael Nielsen, 2014. • Narrative Visualization: Telling Stories with Data in IEEE Vis by Segal and Heer, 2010. • EPSG:4326 vs. EPSG:3857 by Diamond, L. 2017. • Optional: The Architecture of a Data Visualization, Accurat Studio. • Optional: Chp. 11 Value-by-Area Mapping, Cartography by Borden Dent. |
A5.2 assigned | A4 Interactive DUE A5.1 Proposals DUE |
| 4/2 | Maps, Graphic Design, Narrative: projections; typography, rhythm; why storytelling, techniques. (slides) (quiz) (survey) |
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| 4/9 | Final Project In-progress Critique. (slides) Critics: Jia Zhang, Christian Swinehart, Hannah Fresque, and Eugene Wu |
• Visualization Analysis and Design Chp 9: Networks and Trees by Munzner, T. 2014. • Chp. 11 Information Visualization for Text Analysis. from Search User Interfaces by Hearst, M. 2009. • Visualizing Algorithms. Mike Bostock. 2014. • Optional: Four Experiments in Handwriting with a Neural Network. Shan Carter et. al., 2016 |
A5.2 In-Progress Presentation DUE | |
| 4/16 | Networks, Text, Algorithms: node-link diagrams, trees, force layout; visualizing algorithms. (slides) (quiz) |
• 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. |
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| 4/23 | Ethics, Vis Roles and Vis Research: between persuasion and misrepresentation; vis in industry and research (slides) (quiz) |
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| 4/30 | Final Project Showcase @ Brown Institute, Pulitzer Hall (slides) |
A5.3 Lightning Talk + A5.4 Project DUE | ||
| 5/6 | A5 Final Documentation DUE |