Introduction to Data Visualization

COMS W 4995 004 (3 pts)
Instructor: Agnes Chang (ac3882), office hours by appt.
IAs: Conder Shou (cs3544; Thurs 10a-12p), Jeevan Farias (jtf2126; Mon 2-4pm)
Class Time: Tues 6:10-8pm
Room: 415 Schapiro
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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
9/4 Introduction: why visualize? schedule and expectations.
(APPLY for this class)
• 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.
A1.1 Vis Design: divergence assigned  
9/11 Designing: form vs. function, generating ideas, iterating, and critique.
• Semiology of Graphics, Excerpt, Jacques Bertin, 1967.
A Tour through the Visualization Zoo. Jeffrey Heer, Michael Bostock, and Vadim Ogievetsky. ACM. 2010.
D3: Data-Driven Documents. Michael Bostock, Vadim Ogievetsky, Jeffrey Heer. InfoVis 2011.
Optional: The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations, Ben Shneiderman, 1996
A1.2 Vis Design: revisions assigned  
9/18 Data Models: data types, task types, corresponding visualization formats, and tools.
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
A2 Exploratory Data Analysis: assigned A1 Design DUE
9/25 Data Exploration: EDA, data wrangling and Tableau.
Understanding Comics, Chp. 5,7,8, by Scott McCloud
Now You See It, Chp. 3 by Stephen Few, 2009.
Visual Display of Quantitative Information, Chp. 2,4,5, by Tufte, E. 2007
Optional: Mastering Hued Color Scales, Gregor Aisch, 2013.
10/2 Visual Encoding: marks, channels, expressiveness & effectiveness.
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.
39 Studies About Human Perception in 30 Minutes by Kennedy Elliott.
Optional: Design and Redesign in Data Visualization by Viegas & Wattenberg, 2015.
A3.1 Interactive Vis: static assigned A2 EDA DUE
10/9 Perception and Evaluation: how we see, color and attention theory, misrepresentation and a framework for analysis.
Reinventing Explanation. Michael Nielsen, 2014.
Narrative Visualization: Telling Stories with Data in IEEE Vis by Segal and Heer, 2010.
Optional: The Architecture of a Data Visualization, Accurat Studio
Study for midterm  
10/16 Midterm Exam.

Narrative: why storytelling, techniques and examples.
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.
A3.2 Interactive Vis: dynamic assigned  
10/23 Interaction: overview vs. details, small multiples, brushing, etc. • Visual Display of Quantitative Information, Chp. 7, 8, by Tufte, E. 2007
Chp 5: Analytical Techniques from Now You See It by Stephen Few, 2009.
Powers of Ten(video), Charles & Ray Eames, 1977.
A4.1 Final Project: Proposals assigned  
10/30 Animation and Focus: providing context, managing complexity, multiple axes. Cartography: Thematic Map Design, Chp. 11 Value-by-Area Mapping, Borden Dent.
Adaptive Composite Map Projections. Bernhard Jenny. InfoVis 2012.
A4 Final Project assigned A3 Interactive DUE
A4.1 Proposals DUE
11/6 Election day, no class.      
11/13 Maps: projections, isolines, chloropleths. • Chp 9: Networks and Trees(Excerpt) from Visualization Analysis and Design by Munzner, T. 2014.
Visualizing Algorithms. Mike Bostock. 2014.
Teachable Machine.Google Creative Lab, 2017
Four Experiments in Handwriting with a Neural Network. Shan Carter et. al., 2016
  A4.2 Final in-progress critique
11/20 Networks and Algorithms: node-link diagrams, trees, layout algorithms; visualizing algorithms. Chp. 11 Information Visualization for Text Analysis. from Search User Interfaces by Hearst, M. 2009.
Behind The Scenes of “The Whole Brilliant Enterprise.” by Office of Creative Research.
• TBD Research Topics in InfoVis
11/27 Text and Vis Research Topics: text concordance, classification, flow; Prof. Wu guest lecture      
12/4 Final Project Presentations.     A4 Final Documentation DUE