Data Visualization from 2D to 4D
[Screen Graphics to Physical Objects]

DM-GY 9103-C, Fall 2016
Prof. Arlene Ducao, arlduc [at] nyu.edu
Thursdays, 4:30-7:20 PM
2 Metrotech, Room 811

Overview

What is data visualization? Why and how do we do it? Who do we do it for? This course will take you through the process of understanding data visualization role’s in our information landscape, evaluating the kind of data that is best for visualization, and connecting with communities that provide and use the data. Prerequisites: a basic understanding of HTML, CSS, and one scripting language, i.e. Javascript.

Learning Goals

• To understand the history, functionality, and anatomy of data visualization.
• To classify data and information visualization based on temporal, spatial, tangible, and contextual criteria.
• To understand the politics and community contexts that inform data visualization.
• To choose and apply the appropriate tools for developing a wide array of basic data visualizations.
• To plan and execute a complex data visualization project based on human-centered design principles, including significance, relevance, and usability.

Schedule

Note: Guest lecturers and trips are subject to change.

Phase I: Let’s Visualize.

• Session 1: September 8. Class Overview and Toolkit.
  
• Session 2: September 15. Toolkit Deep Dives.
• Session 3: September 22. Field Trip to ProPublica. Optional: NYC Media Lab Summit.
• Session 4: September 29. Diving into data. Guest Speaker: Rahul Bhargava, MIT; April Gu and David Segall, NYC Stern Center for Business and Human Rights.
• Session 5: October 6. Phase I project presentations.

Phase 2: Whose Data Is It, Anyway?

• NO CLASS: October 13. Contact community partners to work with.
• Session 6: October 20. Field Trip to New York Hall of Science (discuss with class)
• Session 7: October 27. Guest speaker on R and data analysis: Kevin Miklasz, Brainpop.
• Session 8: November 3. Field trip to Viacom. Book and community updates.
• Session 9: November 10. Discussion on color. Phase 2/3 one-on-one project discussions.

Phase 3: Visualization for Actual People.

• Session 10: November 17. Phase 2 paper presentations.
• NO CLASS: November 24, Thanksgiving.
• Session 11: December 1. Final project studio time.
• Session 12: December 8. Field Trip to Facebook.
• Session 13: December 15. Final project presentations. Invite your community partners!

Recommended Tools (and see more in the Assignment 1 PDF)

• Data exploration tools. See some examples at Northwestern Knight Lab and DataBasic.io.
• A tabular software environment (Excel, Google sheet, Zoho sheet, etc)
• A relational software environment or interface (MySQL, Tableau, SODA)
• A cartographic package (i.e. TileMill, CartoDB, QGIS)
• A natural language processing environment (IBM Watson)
• A 3D CAD tool (I recommend trying TinkerCAD)
• Creative computing tools like Processing, Arduino, or Quartz Composer (for Mac only)
  
• Statistical analytics tools like R, Matlab, or SPSS.

Recommended Books (to be discussed in Class 1)

Technique / Science Books

• Börner, Katy. Atlas of Science: Visualizing What We Know. Cambridge: MIT Press, 2010.
• Börner, Katy. Atlas of Knowledge: Anyone Can Map. Cambridge: MIT Press, 2015.
• Börner, Katy and David E. Polley. Visual Insights: A Practical Guide to Making Sense of Data. Cambridge: MIT Press, 2014.
• Cairo, Alberto. The Truthful Art: Data, Charts, and Maps for Communication. New Riders, 2016.
• Day, Ronald. Indexing It All: The Subject in the Age of Documentation, Information, and Data. MIT Press, 2016.
• Lima, Manuel. Visual Complexity: Mapping Patterns of Information. Princeton: Princeton Architectural Press, 2013.
• Meeks, Elijah. D3.js In Action. Manning Publications, 2015.
• Munzner, Tamara. Visualization Analysis and Design. AK Peters / CRC Press, 2014.
  
• Murray, Scott. Data Visualization For The Web. O’Reilly, 2013.
• Telea, Alexandru. Data Visualization: Principles and Practice, Second Edition. AK Peters / CRC Press, 2014.
• Ware, Colin. Information Visualization, Third Edition: Perception For Design. Morgan Kaufman, 2012.
• Wong, Dona. The Wall Street Journal Guide to Information Graphics: The Dos and Don’ts of Presenting Data, Facts, and Figures. W.W. Norton & Company, 2013.
• Yau, Nathan: Visualize This: The Flowing Data Guide to Design, Visualization, and Statistics. Wiley, 2011.

Historical / Foundational Books

• Burke, Colin. Information and Intrigue: For Index Cards to Dewey Decimals to Alger Hiss. MIT Press, 2014.
  
• Brinton, Willard Cope: Graphic Methods for Presenting Facts. New York: The Engineering Magazine Company, 1914.
• Brinton, Willard Cope: Graphic Presentation. New York: Brinton Associates, 1939.
• Bertin, Jacques: Semiologie Graphique (Semiology of Graphics). ESRI Press, 2010 (first edition 1967).
• Drucker, Joanna. Graphesis: Visual Forms of Knowledge Production. Cambridge: Harvard University Press, 2014
• Halpern, Orit. Beautiful Data: A History of Vision and Reason Since 1945. Duke University Press, 2015.
• Kitchin, Rob. The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. SAGE Publications: 2014.
• Mireilles, Isabel. Design for Information: An Introduction to the Histories, Theories, and Best Practices Behind Effective Information Visualizations. Rockport Publishers, 2013.
• Rumsey, Abby Smith. When We Are No More: How Digital Memory is Shaping Our Future. Bloomsbury Press, 2016.
  
• Strosberg, Eliane. Art and Science. Abbeville Press, 2015.
• Tufte, Edward. The Visual Display of Quantitative Information. Graphics Press, 1983.
• Tufte, Edward. Envisioning Information. Graphics Press, 1990.
• Tufte, Edward. Visual Explanations. Graphics Press, 1997.
• Zielinsky, Siegfried. Deep Time of Media: Toward an Archeology of Hearing and Seeing by Technical Means. MIT Press, 2008.

Office Hours

Thursday by appointment. E-mail arlduc [at] nyu.edu to make an appointment.

Grading

• 20% Phase 1 Project: Demonstration of prototype & brief write-up.
  
• 20% Phase 2 Paper: Research article & MLA-formatted bibliography.
  
• 25% Phase 3 Final: An ethnographic project drawing on skills and concepts developed in Phase 1 and 2.
  
• 20% Class participation.
• 15% Blog posts based on class discussion and project development. At least nine posts are required for the semester (three posts per class phase).
  
• Encouraged extra credit options:
  • Expanded blogging
  • Video documentation
  • Project web site
  • Conference paper

Attendance

Attendance to all class sessions is mandatory. Class starts at 3:30 sharp. Excused absence requests, i.e. for a religious holiday or a conference, must be made at least 3 business days ahead of the scheduled absence. Emergency absences must be accompanied by official documentation, i.e. a doctor’s note or MTA notice. One letter grade drop will occur for every two unexcused late arrivals or one unexcused absence. For additional NYU School of Engineering Academic Policies and Requirements, please consult this link.