Syllabus: Actual Factual

Actual, Factual: Data Literacy for Creatives
Spring 2020 v 1.0
NYU, Tisch School of the Arts
Interactive Media Arts
Instructor: Rob Faludi
[email protected]

“We don’t receive wisdom; we must discover it for ourselves after a journey that no one can take for us or spare us.”

Marcel Proust

Course Description:

Contemporary interaction designers and artists often manipulate scientific, historical, commercial and social information. Literacy in design, art or engineering requires a complement of literacy in data. This class will make a powerful addition to your existing skill set of programming, visual design and electronics. Students will become conversant in the tools and methods for correctly collecting information and evaluating it to uncover truths about the world. In this class we learn about the “lies, damn lies and statistics” that we encounter daily. Basic training is provided in a variety of methods for interpretation and manipulation of data, yet no math beyond some simple arithmetic is required for completing this course. Exercises include various methods for gathering data, employing information to answer questions, building physical models and using some very accessible computer tools. Short projects teach how to uncover empirical data, what it looks like and what it means. Students will learn how to effectively and ethically extract information from the world, revealing the insightful stories that data have to tell.


Students will develop their data literacy and increase their empirical skills. They will gain a deeper understanding of how collections of information are properly created, examined, manipulated and presented. Assigned projects will explore data gathering, comprehension, exploratory analysis, parameters, probability, prediction, confirmation and ethics. The class is carefully structured to support your other production classes. There are a variety of weekly assignments but no final project or paper, allowing you time to apply your newfound skills.

Class Schedule

  1. Introduction: class structure, student intros, data gathering, and discussions
    Exercise: 10-minute data gathering
    Thu, Jan 28
  2. Overview: syllabus preview, data presentations
    Assignment Due: 10-minute data results presentations
    Tue, Feb 2
  3. Ethics and understanding: statistics, facts, opinions, tricks, and tips
    Exercise: Kidney cancer maps
    Exercise: Examples of lying with statistics
    Readings to discuss: How to Lie with Statistics, Darrell Huff & Irving Geis Thu, Feb 4
  4. Ethics and understanding: Article presentations, personal interview concepts
    Assignment Due: Article Assignment
    Tue, Feb 9 
  5. Listening to People: personal interviews, surveys
    Exercise: Personal interviews
    Readings to Discuss: Watching the English; Pickpocket Article; Optional: Psych, Sherlock or In Treatment
    Thu, Feb 11
  6. Listening to People: focus groups and ethnography
    Exercise: Refrigerator interviews
    Assignment Due: Africa Survey, Anchoring
    Tue, Feb 16
  7. Thinking Scientifically: creativity, criticism and the way of knowing
    Exercise: Mastermind, hypothesis, test, reformulate, test
    Readings to Discuss: The Canon, “Thinking Scientifically”
    The Scientific Method, Jose Wudka
    Tue, Feb 23
  8. Thinking Scientifically: Focus groups presentations
    Assignment Due: Focus Groups Assignment
    Thu, Feb 25
  9. The Modern Science of Measuring: a world of parameters
    Exercise: Are Dropped Coins Normal?
    Readings to discuss: Lady Tasting Tea, chapter 1 & 2, The Function of Measurement in Modern Physical Science, Thomas S. Kuhn
    Tue, Mar 2
  10. The Modern Science of Measuring: Self-portrait presentations
    Assignment Due: Self-Portrait
    Thu, Mar 4
  11. Thinking Quantitatively: estimation and confidence
    Exercise: School buses in America
    Readings to discuss: How Many Licks?: Or, How to Estimate Damn Near Anything, Aaron Santos (
    Tue, Mar 9
  12. Thinking Quantitatively: Guest Lecture TBD
    Exercise: Subjective Probability Intervals and Calibration
    Assignment Due: Discovery Seeker – proposals
    Thu, Mar 11
  13. Looking at Data: exploratory data analysis and descriptive statistics
    Exercise: Handedness of students
    Readings to discuss: Exploratory Data Analysis packet; Felton Annual Report
    Tue, Mar 16
  14. Ethics Redux: experimenting ethically
    Exercise: Experimental Redesign
    Assignments Due: IRB Tutorial and Exam
    Thu, Mar 18
  15. Hands-on Statistics: central tendency and variance
    Exercise: How large is your family?
    Readings to discuss: Descriptive Statistics
    Tue, Mar 23
  16. Catch-up class: student presentations, material or lecture TBA
    Assignment Due: Coin Toss
    Thu, Mar 25
  17. Knowing Uncertainty: probability and conditional probability
    Exercise: Birthdays
    Readings to discuss: The Canon, Chapter 2 Probabilities
    Tue, Mar 30
  18. Knowing Uncertainty: Discovery Seeker pilot discussion
    Exercise: Helicopter Design project
    Assignment Due: Discovery Seeker-Initial Pilot
    Thu, Apr 1
  19. Testing for Truth—same or different: binomial and chi-square
    Exercise: Spinning coins
    Tue, Apr 6
  20. Ring in the Bell Curve: Quincunx presentations
    Exercise: Who opposed Vietnam War?
    Assignment Due: Quincunx
    Thu, Apr 8
  21. Testing for Truth–confirmation: confirmatory stats and your favorite spreadsheet
    Exercise: A quick measurement
    Readings to discuss: The Lady Tasting Tea, Chapters 3-5
    Tue, Apr 13
  22. Discovery Seeker Presentations: final results
    Assignment Due: Discovery Seeker-Final Results
    Thu, Apr 15
  23. Seeing the Future: predictive procedures and open-source stats
    Exercise: Monty Hall
    Tue, Apr 20
  24. Seeing the Future: R workshop
    Exercise: Using R
    Thu, Apr 22
  25. Graphical Persuasion: How to Lie with Maps Part 1
    Exercise: Mapping the ITP floor from Memory
    Readings to discuss: How to Lie with Maps, Mark Monmonier; 
    The Ghost Map, Steven Johnson
    Tue, Apr 27
  26. Graphical Persuasion: How to Lie with Maps Part 2
    Exercise: Mapping the ITP floor from Sight
    Thu, Apr 29
  27. Wrap-up: designing attraction – biases, heuristics, influence, and your brain
    Readings to discuss: Influence, chapter 1; Lady Tasting Tea, chapter 29
    Tue, May 4
  28. Wrap-up: review of all modules and closing notes
    Thu, May 6


Assignments are due in the class for which they were assigned. No credit can be given for work turned in late.

  • 10-minute Data Presentation: Make a short presentation about the data that you gathered. You can use pictures, charts, graphs or however you think best tells your data’s story
  • Article Assignment: Locate an article that apparently uses data or statistics well, and one that appears to be misleading. Note your thoughts on each so you can quickly present them to your group in class.
  • Self Portrait: Data is factual information; science finds its story. But data isn’t only about science so we can turn that on its head. Find data for your own story. Create a self-portrait, using data. 
  • Focus Groups Assignment: Learn about your classmate’s cultures and cultural experiences as described in the assignment handout
  • Countries in Africa Survey: Have at least eight people answer the survey. Remember that there’s two versions of the survey, one with the number 10 on it and the other with the number 65. Chose one at random for each person you poll. We’ll compile the results in class. Survey forms:
  • Discovery Seeker: Use data to seek out a discovery. Gather original data that seeks to answer a question, unravel a mystery, solve a problem, prove a point or reveal a truth. The best way to have a good idea is to have a lot of ideas, so put that into practice by presenting six different ideas to the class and enlisting their help to choose one. Gather your own pilot data, share for critique, improve your technique and gather more. Explore your data and share it for another critique, then use what you learn to gather sufficient samples for your purpose. Explore again, check for significances, patterns, correlations or trends. Finally, tell your data’s story clearly, ethically, accurately, attractively, engagingly and persuasively.
    • Discovery Seeker-Proposal: For the discovery seeker assignment below, create a proposal covering what you want to study, what you expect to find and how you will collect and analyze the data.
    • Discovery Seeker-Initial Pilot: Bring in data from your first pilot run that we can look at, with initial summary analysis that we can discuss as a class. What worked, what gave you trouble, what complexities turned up? You’ll run a second pilot with these things in mind.
    • Discovery Seeker-Final Results: What changed, how did the outcome change, what did you learn? Real science studies typically take 6 months to a year, what would you do next?
  • Coin Toss: An investigation of the difference between the perception and realities of random. Complete ONE of these tasks, as assigned: Toss a coin 100 times and write down the results OR imagine tossing a coin 100 times and write down the results
  • Quincunx: Get to know the normal distribution intimately by building a quincunx or Galton box. Build either a physical quincunx OR make a software program to simulate a quincunx. If you’re feeling inspired, build any device or program that incorporates the probability density distribution (normal curve) in its fundamental operations. 


Links to documentation of every project must be submitted for credit.


Class participation & attendance 30%
Presentations and assignments 40%
Projects and documentation 30%

Office Hours

Monday 3:30 – 4:30

Making the Most of It:

During remote classes, please keep your camera on throughout, unless we’re on a break. For best results, come to class early, participate in discussions, ask lots of questions, offer copious and constructive feedback, stretch yourself and have fun. 

Selected Readings

“No number is significant in itself: its only significance is in relation to other numbers.”

Zell Kravinsky, real estate genius & anonymous living kidney donor

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