Introduction to Data Visualization


COMS W W4995.006 (3 pts)
Instructor: Agnes Chang (ac3882), office hours by appt.
IAs: Emma Lu (ell2140; Mon 3-5p), Irene Koo (hk2919; Wed 2-4p)
Class Time: Tues. 6:10-8pm
Room: 301M Fayerweather
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Overview

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.

Schedule

  Class Reading Due Assigned Due Monday-before
1/21 Introduction: why visualize? schedule and expectations.
(slides)
(APPLY TO ENROLL by Friday)
  A2.1 Vis Design: divergence assigned  
1/28 Designing: form vs. function, generating ideas, iterating, and critique.
(slides)
(quiz)
• 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.
(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
A3 Exploratory Data Analysis: assigned A2 Design DUE
2/11 Data Exploration: EDA, data wrangling and Tableau.
(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
   
2/18 Visual Encoding: marks, channels, expressiveness & effectiveness.
(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.
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

Past semesters