Data Visualization In The Community
[Formerly Data Visualization from 2D to 4D]
DM-GY 9103-D, Fall 2017
Prof. Arlene Ducao, arlduc [at] nyu.edu
Thursdays, 3:30-6:20 PM
2 Metrotech, Room 816
- 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.
Note: Guest lecturers and trips are subject to change.
Phase I: Let’s Visualize.
- Session 1: September 7. Class Overview and Toolkit.
- Session 2: September 14. Toolkit Deep Dives. Guest: Julia Kim, Library of Congress.
- NO CLASS (Session 3): September 21. Self-Guided Sessions.
- Session 4: September 28. Field trip to the Associated Press.
- Session 5: October 5. Phase 1 visualization presentations.
Phase 2: Whose Data Is It, Anyway?
- Session 6: October 12. Guest: Farhan Mustafa, Grafiti; Lenina Nadal, Global Action Project.
- NO CLASS: October 19. Self-guided Field Trip to New York Hall of Science (discuss with class)
- Session 7: October 26. Guest speaker on R and data analysis: Kevin Miklasz, Brainpop.
- Session 8: November 2. Book and community updates.
- Session 9: November 9. Phase 2 paper presentations.
Phase 3: Visualization for Actual People.
- Session 10: November 16. Discussion on color. Phase 3 one-on-one project discussions.
- NO CLASS: November 23, Thanksgiving.
- Session 11: November 30, Final project studio time.
- Session 12: December 7. Field Trip to Viacom.
- Session 13: December 14. 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.
Thursday by appointment. E-mail arlduc [at] nyu.edu to make an appointment.
- 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 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.