Syllabus: Actual Factual

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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]
212-989-6888 http://rob.faludi.com/teaching

“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.

Goals:

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, signal processing, 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
  2. Overview: syllabus preview, data presentations
    Assignment Due: 10-minute data results presentations
  3. Ethics and understanding: statistics, facts, opinions, tricks, and tips
    Readings to discuss: How to Lie with Statistics, Darrell Huff & Irving Geis
  4. Ethics and understanding: Article presentations, personal interview concepts
    Assignment Due: Article Assignment
  5. Listening to People: personal interviews, surveys
    Exercise: Personal interviews
    Readings to Discuss: Watching the English; Pickpocket Article; Optional: Psych, Sherlock or In Treatment 
  6. Listening to People: focus groups and ethnography
    Exercise: refrigerator interviews
    Assignment Due: Africa Survey, Anchoring
  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
  8. Thinking Scientifically: Focus groups presentations
    Assignment Due: Focus Groups Assignment
  9. The Modern Science of Measuring: a world of parameters
    Exercise: Popcorn Norm (What Does the Normal Distribution Sound Like)
    Readings to discuss: Lady Tasting Tea, chapter 1 & 2, The Function of Measurement in Modern Physical Science, Thomas S. Kuhn
  10. The Modern Science of Measuring: Self-portrait presentations
    Assignment Due: Self-Portrait
  11. Thinking Quantitatively: estimation and confidence
    Exercise: School busses in America
    Readings to discuss: How Many Licks?: Or, How to Estimate Damn Near Anything, Aaron Santos (https://en.wikipedia.org/wiki/Fermi_problem)
  12. Thinking Quantitatively: Guest Lecture TBD
    Exercise: Subjective Probability Intervals and Calibration
    Assignment Due: Discovery Seeker-Proposals
  13. Looking at Data: exploratory data analysis and descriptive statistics
    Readings to discuss: Exploratory Data Analysis packet; Felton Annual Report
  14. Ethics Redux: experimenting ethically
    Exercise: Measuring heights
    Assignments Due: IRB Tutorial and Exam
  15. Hands-on Statistics: central tendency and variance
    Exercise: Families 
    Readings to discuss: Descriptive Statistics
  16. Catch-up class: student presentations, material or lecture TBA
    Assignment Due: Coin Toss
  17. Knowing Uncertainty: probability and conditional probability
    Exercise: Birthdays
    Readings to discuss: The Canon, Probabilities
  18. Knowing Uncertainty: Discovery Seeker pilot discussion
    Exercise: Measuring
    Assignment Due: Discovery Seeker-Initial Pilot
  19. Testing for Truth—same or different: binomial and chi-square
    Exercise: Spinning coins
  20. Ring in the Bell Curve: Quincunx presentations
    Assignment Due: Quincunx
  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
  22. Discovery Seeker Presentations: final results
    Assignment Due: Discovery Seeker-Final Results. 
  23. Seeing the Future: predictive procedures and open-source stats
    Exercise: Monty Hall
  24. Seeing the Future: R workshop
    Exercise: Using R
  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
  26. Graphical Persuasion: How to Lie with Maps Part 2
    Exercise: Mapping the ITP floor from Sight
  27. Wrap-up: designing attraction – biases, heuristics, influence, and your brain
    Readings to discuss: Influence, final chapter; Lady Tasting Tea, chapter 29
  28. Wrap-up: review of all modules and closing notes

Assignments

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
  • Africa Survey, Anchoring: Conduct a survey, as described in the assignment handout.
  • 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. 

Documentation:

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

Grading:

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