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

“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, prediction, confirmation and ethics.Â 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

Introduction: class structure, student intros, data gathering, and discussions
Exercise: 10-minute data gathering
Thu, Jan 28
Overview: syllabus preview, data presentations
Assignment Due: 10-minute data results presentations
Tue, Feb 2
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
Ethics and understanding: Article presentations, personal interview concepts
Assignment Due: Article Assignment
Tue, Feb 9
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
Listening to People: focus groups and ethnography
Exercise: Refrigerator interviews
Assignment Due: Africa Survey, Anchoring
Tue, Feb 16
Thinking Scientifically: creativity, criticism and the way of knowing
Exercise: Mastermind, hypothesis, test, reformulate, test
Readings to Discuss: The Canon, â€œThinking Scientificallyâ€
The Scientific Method, Jose Wudka
Tue, Feb 23
Thinking Scientifically: Focus groups presentations
Assignment Due: Focus Groups Assignment
Thu, Feb 25
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
The Modern Science of Measuring: Self-portrait presentations
Assignment Due: Self-Portrait
Thu, Mar 4
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 (https://en.wikipedia.org/wiki/Fermi_problem)
Tue, Mar 9
Thinking Quantitatively: Guest Lecture TBD
Exercise: Subjective Probability Intervals and Calibration
Assignment Due: Discovery Seeker – proposals
Thu, Mar 11
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
Ethics Redux: experimenting ethically
Exercise: Experimental Redesign
Assignments Due: IRB Tutorial and Exam
Thu, Mar 18
Hands-on Statistics: central tendency and variance
Exercise: How large is your family?
Readings to discuss: Descriptive Statistics
Tue, Mar 23
Catch-up class: student presentations, material or lecture TBA
Assignment Due: Coin Toss
Thu, Mar 25
Knowing Uncertainty: probability and conditional probability
Exercise: Birthdays
Readings to discuss: The Canon, Chapter 2 Probabilities
Tue, Mar 30
Knowing Uncertainty: Discovery Seeker pilot discussion
Exercise: Helicopter Design project
Assignment Due: Discovery Seeker-Initial Pilot
Thu, Apr 1
Testing for Truthâ€”same or different: binomial and chi-square
Exercise: Spinning coins
Tue, Apr 6
Ring in the Bell Curve: Quincunx presentations
Exercise: Who opposed Vietnam War?
Assignment Due: Quincunx
Thu, Apr 8
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
Discovery Seeker Presentations: final results
Assignment Due: Discovery Seeker-Final Results
Thu, Apr 15
Seeing the Future: predictive procedures and open-source stats
Exercise: Monty Hall
Tue, Apr 20
Seeing the Future: R workshop
Exercise: Using R
Thu, Apr 22
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
Graphical Persuasion: How to Lie with Maps Part 2
Exercise: Mapping the ITP floor from Sight
Thu, Apr 29
Wrap-up: designing attraction – biases, heuristics, influence, and your brain
Readings to discuss: Influence, chapter 1; Lady Tasting Tea, chapter 29
Tue, May 4
Wrap-up: review of all modules and closing notes
Thu, May 6

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 isnâ€™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 classmateâ€™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: http://faludi.com/classes/actualfactual/resources/Countries_in_Africa_Survey.pdf

IRB Tutorial and Exam. Earlham: https://earlham.az1.qualtrics.com/SE/?SID=SV_cMbRTgf6nWL3aYJ
Please turn in your passing quiz.

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 dataâ€™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 youâ€™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 â€“ 4:30

Making the Most of It:

During remote classes, please keep your camera on throughout, unless weâ€™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