Introduction to Data Visualization

COMS W4995.009 (3 pts)

Instructor: Christian Swinehart (cds2205; office hours by appt.)
IAs: Ruoyang ‘Kathy’ Liu (rl3323; Wed 5–7pm), Binny Naik (bn2341; Tue 3–5pm)
Class Time: Thurs. 4:10–6:40pm
Room: 420 Pupin
Courseworks (and Zoom Info)
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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, 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
1/19 Introduction: why visualize? schedule and expectations.
  APPLY TO ENROLL by Monday A2.1 Viz Design: divergence assigned
1/26 Design: 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, 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
2/2 Data Models: data types, task types, corresponding visualization formats.
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
A2 Design DUE
2/9 Data Exploration: EDA, data wrangling, and Tableau.
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
2/16 Visual Encoding: marks, channels, expressiveness & effectiveness.
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
2/23 Interaction: overview vs. details, small multiples, brushing, etc.
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
3/2 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 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    
3/9 Midterm Exam     A4.3 Interactive: evaluation assigned
3/16 Spring Break, no class.        
3/23 Animation: motion perception, transitions, pros/cons.
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
3/30 Maps & Narrative: projections; typography, rhythm; why storytelling, techniques.
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
4/6 Final Project In-progress Critique.
Guest Critics: Asad Pervaiz, Eugene Wu
      A5.2 In-Progress Presentation DUE
4/13 Ethics, Dataviz 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: 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.
4/20 Final Critique
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
    Extra Credit DUE
4/27 Final Project Showcase: Brown Institute, Pulitzer Hall 4–6pm       A5.3 Lightning Talk DUE
5/8 MON         A5.4 Final Visualization DUE
A5.5 Final Documentation DUE

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