Introduction to Data Visualization

COMS W W4995.010 (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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Spring 2021 Final Projects

• What’s Shakespeare About Anyways? “As You Like It” through introductory text analysis
• Visualizing COVID Testing Policies
• Is Netflix Actually Chill? Exploring popularity of Netflix vs. non-Netflix produced shows
• Injuries on Japan’s Railway System
• GBBO: Managing Risk in Your Competitive Baking Flavor Portfolio
• Heartbeat of a City: Economic inequality and neighborhood transportation in a time of COVID
• How Diverse is Museum Mile?
• NBA Exploratory Viz Tool
• Music Moods & COVID-19
• NYC Transit Trends
• MTA + COVID-19
• Flight Delays
• Economical effects of COVID-19 crisis

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 (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.	A4.2 Interactive: dynamic assigned
3/3	Evaluation, Perception, Review: a framework for analysis; how we see, color and attention theory. (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.	Study for midterm
3/10	Midterm Exam. Postponed, moved online.		A4.3 Interactive: evaluation assigned
3/17	Spring Break, no class.
3/24 3/31	Animation: motion perception, transitions, pros/cons. (slides) (quiz)	• 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 Online midterm DUE
3/31 4/7	Maps & Narrative: projections; typography, rhythm; why storytelling, techniques. (slides) (quiz) (survey)	• 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 4/14	Final Project In-progress Critique. (slides) Critics: Hannah Fresques, Jia Zhang, Erica Greene, and Eugene Wu			A5.2 In-Progress Presentation DUE
4/14 4/21	Networks, Text, Algorithms: node-link diagrams, trees, force layout; visualizing words and algorithms. (slides) (quiz)	• 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	Extra Credit DUE
4/21 4/28	Ethics, Vis in Industry vs. Research: between persuasion and misrepresentation; jobs, and guest lecture by Prof. Wu on research. (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. • Optional: What is a Senior Data Visualization Engineer? by Meeks, E. 2018.
4/28 5/5	Final Project Showcase 6–8pm			A5.3 Lightning Talk DUE
5/11 MON				A5.4 Final Visualization DUE + A5.5 Final Documentation DUE

Past semesters

• Fall 2019
• Spring 2019
• Fall 2018