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

DM-GY 9103, Fall 2015
Prof. Arlene Ducao, arlduc [at] nyu.edu
Thursdays, 6:30-9:20 PM
2 Metrotech, Room 811

Overview

What is data visualization? Why and how do we do it? 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 implementing the techniques used to create 2D, 3D, and 4D visualizations. 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 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 audience-centric design principles including significance, relevance, and usability.

Class Format

• First part (60-90 minutes): Lecture, discussion, critique.
• Second part (90-110 minutes): Hands-on building & testing. Early sessions will offer technical how-tos and labs, later sessions will offer open work time for your projects.

Schedule

Note: Guest lecturers and critics are subject to change.

• Class 1: September 3
  • Lecture: A Brief History of Data Visualization.
  • Activity: Introductions and Group Exercise.
• Class 2: September 10
  • Lecture: Data Vis Toolbox.
  • Activity: Book sharing; Tabular and Query tools.
• Class 3: September 17
  • Lecture: Catherine Cramer, New York Hall of Science.
  • Activity: Finish Tabular and Query tools; Lit Review exercise. 
• Class 4: September 24
  • Lecture: Chris Willard, Guidewire Software.
  • Activity: Start brainstorming for midterm.
• Class 5: October 1
  • Lecture: Catherine D’Ignazio, Emerson Engagement Lab.
  • Activity: Geo-spatial vis tools. 
• Class 6: October 8
  • Lecture: Ekene Ijeoma, multi-dimensional cartographies.
  • Lecture: Kevin Miklasz, Function vs. aesthetics in data visualization: some case studies.
  • Activity: Student-student critiques and open work time.
• Class 7: October 15
  • MIDTERM with guest critics De Angela Duff and Holly Orr, NYU.
• Class 8: October 22
  • Lecture: Bex Hurwitz, Research Action Design. 
  • Activity: Human-centered design exercise (design principles).
• No Class October 29.
• Class 9: November 5
  • Lecture: Austin Lee, Microsoft and Carnegie Mellon.
  • Activity: CAD and Motion Graphics Tools. 
• Class 10: November 12
  • Lecture: Rafi Santo, Mozilla Hive. 
  • Lesson: Kevin Miklasz on R and statistical significance. 
  • Activity: Additional tutorials and troubleshooting as needed.
• Class 11: November 19
  • Lecture/lab: Richard The, Google and SVA.
  • Lecture/lab: Peiqi Su, NYU ITP.
• Class 12: December 3.
  • Lecture: Sha Hwang, Healthcare.gov, Gifpop, more.
  • Activity: Prepare for FINAL
• Class 13: December 10
  • FINAL with guest critics Amy Yu (Viacom) and Birago Jones (Indicator Ventures, UBQ)

Recommended Tools

• A tabular software environment (Excel, Google sheet, Zoho sheet, etc)
• A relational software environment or interface (MySQL, Tableau, SODA)
• Chrome (for javascript development)
• Processing
• A cartographic package (i.e. TileMill, CartoDB, QGIS)
• Cytoscape or other network graph package (D3 could also work)
• A free 3D CAD tool (I recommend trying OpenSCAD and TinkerCAD)
• Quartz Composer (for Mac only)

Recommended Books (to be discussed in Class 1)

Foundational Books

• 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).
• 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.

Recent Books

• Börner, Katy. Atlas of Science: Visualizing What We Know. Cambridge: MIT Press, 2010.
• Lima, Manuel. Visual Complexity: Mapping Patterns of Information. Princeton: Princeton Architectural Press, 2013.
• Börner, Katy and David E. Polley. Visual Insights: A Practical Guide to Making Sense of Data. Cambridge: MIT Press, 2014.
• Drucker, Joanna. Graphesis: Visual Forms of Knowledge Production. Cambridge: Harvard University Press, 2014.
• Börner, Katy. Atlas of Knowledge: Anyone Can Map. Cambridge: MIT Press, 2015.
• Strosberg, Eliane. Art and Science. Abbeville Press, 2015.
• Halpern, Orit. Beautiful Data: A History of Vision and Reason Since 1945. Duke University Press, 2015.

Office Hours

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

Grading

Note: Working in groups is strongly encouraged.

• 25% Midterm: Demonstration of prototype & two-page paper with MLA-formatted bibliography.
• 35% Final: Demonstration of prototype & four-page paper with MLA-formatted bibliography.
• 20% Class participation.
• 20% Blog posts based on class discussion and project development. At least ten posts are required from the entire semester (five by midterms, five by finals).
• Encouraged extra credit options:
  • Expanded blogging
  • Video documentation
  • Project web site
  • Conference paper

Attendance

Attendance to all class sessions is mandatory. Class starts at 6:30 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.