COMS W4995.011 (3 pts)
Instructor: Christian Swinehart (cds2205; office hours by appt.)
IAs: Natalia Dorogi (ngd2111; Mon 1–3pm), Charlotte Li (aw3156; Wed 9–11am)
Class Time: Thurs. 4:10–6:40pm
Room: 644 Mudd
Courseworks (and Zoom Info)
Class Slack
Course Feedback Form
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, visual hierarchy, 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 with Python or R for data processing is helpful but not required.
| Topic | Reading Due In Class | Assigned, Individual | Assigned, Group | Due Wed. Before Class | |
|---|---|---|---|---|---|
| 1/20 | Introduction: why visualize? schedule and expectations. ⟨slides⟩ |
APPLY TO ENROLL by Monday | A2.1 Viz Design: divergence assigned |
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| 1/27 | Design: form vs. function, generating ideas, iterating, and critique. ⟨slides⟩ |
• Visual Explanations, Chp. 2 Excerpt, by Tufte, E. 2007. • How to be creative & How to be critical, Ko, A. 2017. • Lateral Thinking, Excerpts, deBono, E. 1967. • Optional: The Architecture of a Data Visualization, Accurat Studio. |
L1 Shapes and Styles assigned |
A2.2 Viz Design: revisions assigned |
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| 2/3 | Data Models: data types, task types, corresponding visualization formats. ⟨slides⟩ |
• Semiology of Graphics, Excerpt, by Bertin, J. 1967. • A Tour through the Visualization Zoo. Heer, Bostock & 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, by Shneiderman, B. 1996 |
L2 Data Binding assigned |
A3 Exploratory Data Analysis assigned |
L1 DUE A2 Design DUE |
| 2/10 | Data Exploration: EDA, data wrangling, and Tableau. ⟨slides⟩ |
• Chp. 6: Analytical Patterns from Now You See It by Few, S. 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 by Bertin, J. 1981. • Optional: A Layered Grammar of Graphics by Wickham, H. 2010. • Optional: Bad Data Guide by Quartz data team |
L2 DUE |
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| 2/17 | Visual Encoding: marks, channels, expressiveness & effectiveness. ⟨slides⟩ |
• Understanding Comics, Chp. 5,7,8, by McCloud, S. • 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: What to consider when creating choropleth maps by Rost, L. C. 2017. • Optional: D3: Data-Driven Documents. Bostock, Ogievetsky, Heer. InfoVis 2011. |
L3 Interactivity assigned |
A4.1 Interactive: static assigned |
A3 EDA DUE |
| 2/24 | Interaction: overview vs. details, small multiples, brushing, etc. ⟨slides⟩ |
• Interactive Dynamics for Visual Analysis. Heer & Shneiderman. 2012. • Ladder of Abstraction by Victor, B. 2011. • In Defense of Interactive Graphics, Aisch, G. 2017. • Optional: Mastering Hued Color Scales, Aisch, G. 2013. |
A4.2 Interactive: dynamic assigned |
L3 DUE |
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| 3/3 | Evaluation, Perception, Review: a framework for analysis; how we see, color, and attention theory. ⟨slides⟩ |
• 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 Few, S. 2009. • 39 Studies About Human Perception in 30 Minutes by Elliott, K. • Optional: Design and Redesign in Data Visualization by Viegas & Wattenberg, 2015. |
Study for midterm | ||
| 3/10 | Midterm Exam | A4.3 Interactive: evaluation assigned |
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| 3/17 | Spring Break, no class. | ||||
| 3/24 | Animation: motion perception, transitions, pros/cons. ⟨slides⟩ ⟨final project inspiration⟩ |
• Creating Usability with Motion, by Willenskomer, I. 2017 • Chp. 5: Analytical Techniques from Now You See It by Few, S. 2009. • Powers of Ten (video), Charles & Ray Eames, 1977. • Optional: Animated Transitions in Statistical Data Graphics by Heer, J. 2007. |
A5.1 Final: Proposals assigned |
A4 Interactive DUE |
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| 3/31 | Maps & Narrative: projections; typography, rhythm; why storytelling, techniques. ⟨slides⟩ ⟨survey⟩ |
• The Making of R2D3 (video) and A Visual Introduction to Machine Learning (viz) by Chu, T. 2016. • Communicating with Interactive Articles by Hohman, Conlen, Heer, & Chau, 2020. • What to consider when creating choropleth maps by Rost, L. C. 2017. • Optional: Narrative Visualization: Telling Stories with Data in IEEE Vis by Segal & Heer, 2010. • Optional: Reinventing Explanation. by Nielsen, M. 2014. |
A5.2 assigned |
A5.1 Proposals DUE |
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| 4/7 | Final Project In-progress Critique. Guest Critics: Lisa Strausfeld, Ryan Best, Eugene Wu ⟨slides⟩ |
A5.2 In-Progress Presentation DUE |
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| 4/14 | Networks, Text, Algorithms: node-link diagrams, trees, force layout; visualizing words and algorithms. ⟨slides⟩ |
• Visualization Analysis and Design Chp 9: Networks and Trees by Munzner, T. 2014. • Pictures of Arguments, Songs, and Ancient Texts (video, 1’30”–32’46”) by Viegas and Wattenberg, at Eyeo 2013. • Visualizing Algorithms. by Bostock, M. 2014. • Optional: Four Experiments in Handwriting with a Neural Network. by Carter, S. et. al., 2016 |
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| 4/21 | Ethics, Dataviz in Industry vs. Research: between persuasion and misrepresentation; jobs, and guest lecture by Prof. Wu on research. ⟨slides⟩ |
• Six Provocations for Big Data by boyd & Crawford, 2011 • What Is Visualization Research? by Hullman, J. • Optional: Applying Racial Equity Awareness in Data Visualization by Urban Institute. 2020. • Optional: Connecting with the Dots by Harris, J. 2015. • Optional: What is a Senior Data Visualization Engineer? by Meeks, E. 2018. |
Extra Credit DUE |
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| 4/28 | Final Project Showcase ⟨slides⟩ |
A5.3 Lightning Talk DUE |
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| 5/9 MON | A5.4 Final Visualization DUE A5.5 Final Documentation DUE |