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|
“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
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, 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.
- Introduction: class structure, student intros, data gathering, and discussions
Exercise: 10-minute data gathering
- Overview: syllabus preview, data presentations
Assignment Due: 10-minute data results presentations
- Ethics and understanding: statistics, facts, opinions, tricks, and tips
Readings to discuss: How to Lie with Statistics, Darrell Huff & Irving Geis
- Ethics and understanding: Article presentations, personal interview concepts
Assignment Due: Article Assignment
- Listening to People: personal interviews, surveys
Exercise: Personal interviews
Readings to Discuss: Watching the English; Pickpocket Article; Optional: Psych, Sherlock or In Treatment
- Listening to People: focus groups and ethnography
Exercise: refrigerator interviews
Assignment Due: Africa Survey, Anchoring
- 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
- Thinking Scientifically: Focus groups presentations
Assignment Due: Focus Groups Assignment
- 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
- The Modern Science of Measuring: Self-portrait presentations
Assignment Due: Self-Portrait
- 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)
- Thinking Quantitatively: Guest Lecture TBD
Exercise: Subjective Probability Intervals and Calibration
Assignment Due: Discovery Seeker-Proposals
- Looking at Data: exploratory data analysis and descriptive statistics
Readings to discuss: Exploratory Data Analysis packet; Felton Annual Report
- Ethics Redux: experimenting ethically
Exercise: Measuring heights
Assignments Due: IRB Tutorial and Exam
- Hands-on Statistics: central tendency and variance
Readings to discuss: Descriptive Statistics
- Catch-up class: student presentations, material or lecture TBA
Assignment Due: Coin Toss
- Knowing Uncertainty: probability and conditional probability
Readings to discuss: The Canon, Probabilities
- Knowing Uncertainty: Discovery Seeker pilot discussion
Assignment Due: Discovery Seeker-Initial Pilot
- Testing for Truth—same or different: binomial and chi-square
Exercise: Spinning coins
- Ring in the Bell Curve: Quincunx presentations
Assignment Due: Quincunx
- Testing for Truth–confirmation: confirmatory stats and your favorite spreadsheet
Exercise: A quick measurement
Readings to discuss: The Lady Tasting Tea, Chapters 3-5
- Discovery Seeker Presentations: final results
Assignment Due: Discovery Seeker-Final Results.
- Seeing the Future: predictive procedures and open-source stats
Exercise: Monty Hall
- Seeing the Future: R workshop
Exercise: Using R
- 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
- Graphical Persuasion: How to Lie with Maps Part 2
Exercise: Mapping the ITP floor from Sight
- Wrap-up: designing attraction – biases, heuristics, influence, and your brain
Readings to discuss: Influence, final chapter; Lady Tasting Tea, chapter 29
- Wrap-up: review of all modules and closing notes
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.
- IRB Tutorial and Exam. (https://www.nyu.edu/research/resources-and-support-offices/getting-started-withyourresearch/human-subjects-research/tutorial.html) Please turn in your passing certificate.
- 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%
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.
- How to Lie with Statistics
- The Canon
- The Lady Tasting Tea
- The Function of Measurement in Modern Physical Science
- The Structure of Scientific Revolutions
- Feltron Annual Report
- How to Lie with Maps
- The Ghost Map
- A Mathematician Reads the Newspaper
- Descriptive Statistics
- Mastermind rules
- Tfhe Scientific Method, Jose Wudka
“No number is significant in itself: its only significance is in relation to other numbers.”Zell Kravinsky, real estate genius & anonymous living kidney donor