Crafting With Data
Fall 2009
NYU, Tisch School of the Arts
Interactive Telecommunications Program
v1.0

Instructor: Rob Faludi
rob@faludi.com
2129896888
http://rob.faludi.com/teaching ^{[1]}

Course Description:
Contemporary interaction designers and artists often manipulate scientific, historical, commercial and social information. Literacy in design, art or engineering requires the complement of literacy in data. This class makes a powerful addition to your existing skill set of programming, visual design and electronics. Students will become conversant in the tools available for extracting insightful information from realworld samples. In this class we learn about the “lies, damn lies and statistics” that are encountered in our daily information feeds. Basic training is provided in a variety of handy methods for interpretation and manipulation of data, yet no math beyond some simple arithmetic is required for completing this course. Materials are visually oriented, and the focus is on concepts rather than on mechanics. Exercises include analyzing maps, building physical models and exploring information via accessible computer simulations. Short projects teach how to understand where data comes from, what it looks like and what it means. Students will earn how to transform data in ways that avoid distortions, reveal truths and grandly illuminate their ideas. Note: 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.
Goals:
Students will develop their data literacy. 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.
Class Schedule:
1. Intro and overview: introduction, introductions, overview & syllabus review.
Exercise One: 10 minute data gathering
Exercise Two: 10 minute results
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 next week.
Reading Assignment: How to Lie with Statistics, Darrell Huff & Irving Geis
2. Ethics and understanding: how to lie with statisticsDemo: Arduino data gathering
Exercise One: Article presentations
Assignment: Use an Arduino to gather 500 samples of interesting data in two different conditions (1000 samples total). Post your data online so it can be downloaded.
Readings to discuss: How to Lie with Statistics, Darrell Huff & Irving Geis
3. Thinking Scientifically: creativity, criticism and the way of knowing
Demonstration: Mastermind, hypothesis, test, reformulate, test
http://www.irt.org/games/js/mind/
Assignments:
 SelfPortrait: 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 selfportrait, using data.
Readings to Discuss: The Canon, “Thinking Scientifically”
The Scientific Method, Jose Wudka: http://phyun5.ucr.edu/~wudka/Physics7/Notes_www/node5.html
4. The Modern Science of Measuring: a world of parameters
Presentations: Selfportraits in data
Assignment: Redo data gathering assignment, with improvements
Readings to discuss: Lady Tasting Tea, chapter 1 & 2, Kuhn, Thomas S., “The Function of Measurement in Modern Physical Science”, ISIS 52(2), 161193, 1961. http://www.compilerpress.atfreeweb.com/Anno%20Kuhn%20Function%20of%20Measurement.htm
5. Thinking Quantitatively: Estimation and Confidence
Exercise One: School Busses in America
Exercise Two: Subjective Probability Intervals and Calibration
Self Portrait presentations
Assignments:
 Countries in Africa survey, at least six respondents
 Oneparagraph proposal for the following study: “Think of a question about New York and gather some data that answers it.
 Read over Nicholas Feltron’s site: http://feltron.com
6. Looking at Data: exploratory data analysis and descriptive statistics
Speaker: Nicholas Feltron
Exercise One: Countries in Africa data analysis
Assignments:
 Think of a question about New York and gather some data that answers it. Is the express train worth the wait? Is there a prime street for dogwatching? Are people in DUMBO smarter than people in RAMBO?
 IRB Tutorial and Exam. Please print your passing grade for me. http://www.nyu.edu/ucaihs/
Readings to discuss: Exploratory Data Analysis packet, Felton Annual Report http://feltron.com/index.php?/content/2007_annual_report/
7. Handson Statistics: central tendency and variance
Speaker: Caroline Brown: Data Research
Exercise Two: Measuring Heights
Exercise One: Families
Assignment:
 Question about New York assignment: revise method and gather final data. You’ll be presenting your work at the next class, taking a first pass at analysis and visualization. After your presentation, you’ll revise and represent a final view.
 Toss a coin 100 times and write down the results OR imagine tossing a coin 100 times and write down the results. You’ll be assigned to one task.
Readings to discuss: Descriptive Statistics: http://davidmlane.com/hyperstat/desc_univ.html
8. Knowing Uncertainty: Probability and Conditional Probability
Exercise Zero: Birthdays
Exercise One: Measuring
Assignment: Complete New York Data
Readings to discuss: The Canon, Probabilities
9. Presentations: Coins, New York and Africa
Exercise One: Spinning Coins
Assignment: Begin your Quincunx
Readings to discuss: none this week
10. Testing for Truth: same or different, binomial and chisquare
Assignment: Finish Your Quincunx
Readings to discuss: The Lady Tasting Tea, Chapters 35
11. Testing for Truth: confirmatory stats and your favorite spreadsheet
Exercise One: A quick measurment
12. Seeing the Future: predictive procedures and opensource stats
Exercise One: Using R
13. Graphical Persuasion: how to lie with maps.
Readings to discuss: How to Lie with Maps, Mark Monmonier;
The Ghost Map, Steven Johnson
14. Judgment: designing attraction
Readings to discuss: Lady Tasting Tea, chapter 29
 Intro and overview: introduction, introductions, overview & syllabus review.
Exercise One: 10 minute data gathering
Exercise Two: 10 minute results
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 next week.
Reading Assignment: How to Lie with Statistics, Darrell Huff & Irving Geis
 Ethics and understanding: how to lie with statistics
Demo: Arduino data gathering
Exercise One: Article presentations
Assignment: Use an Arduino to gather 500 samples of interesting data in two different conditions (1000 samples total). Post your data online so it can be downloaded.
Readings to discuss: How to Lie with Statistics, Darrell Huff & Irving Geis
 Thinking Scientifically: creativity, criticism and the way of knowing
Exercise: Mastermind, hypothesis, test, reformulate, test. http://www.irt.org/games/js/mind/
Assignments:
 SelfPortrait: 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 selfportrait, using data.
Readings to Discuss: The Canon, “Thinking Scientifically”, The Scientific Method ^{[2]}, Jose Wudka:
 The Modern Science of Measuring: a world of parameters
Speaker: Nicholas Felton
Presentations: Selfportraits in data
Assignment: Do a second iteration of your data gathering assignment, with improvements
Readings to discuss: Feltron Annual Report ^{[3]}
 Thinking Quantitatively: Estimation and Confidence
Exercise One: School Busses in America
Exercise Two: Subjective Probability Intervals and Calibration
Presentations: finish showing Self Portraits
Assignments:
 Countries in Africa survey, at least six respondents
 Discovery Seeker: The best way to have a good idea is to have a lot of ideas, so put that into practice by preparing six different project ideas to present to the class. You’ll enlist their help to choose one.
Readings to discuss: Lady Tasting Tea, chapter 1 & 2, Kuhn, Thomas S., “The Function of Measurement in Modern Physical Science ^{[4]}“, ISIS 52(2), 161193, 1961.
 Looking at Data: exploratory data analysis and descriptive statistics
Exercise One: Countries in Africa data analysis
Assignments:
 Discovery Seeker: Use data to seek out a discovery. Gather original pilot data that seeks to answer a question, unravel a mystery, solve a problem, prove a point or reveal a truth. You’ll present this to the class for critique
 IRB Tutorial and Exam. Please print your passing grade for me. http://www.nyu.edu/ucaihs/
Readings to discuss: Exploratory Data Analysis packet
 Handson Statistics: central tendency and variance
Exercise One: Families
Exercise Two: Measuring Heights
Assignment:
 Discovery Seeker: improve your technique, gather more data and explore it. You’ll share your improvements and exploration findings at the next critique.
 Toss a coin 100 times and write down the results OR imagine tossing a coin 100 times and write down the results. You’ll be assigned to one task.
Readings to discuss: Descriptive Statistics ^{[5]}: http://davidmlane.com/hyperstat/desc_univ.html
 Knowing Uncertainty: Probability and Conditional Probability
Exercise Zero: Birthdays
Exercise One: Measuring
Assignment: Discovery Seeker: Use what you’ve learned to gather sufficient samples for your purpose. Explore again, check for significances, patterns, correlations or trends to share with the class.
Readings to discuss: The Canon, Probabilities
 Presentations: Coins, Discovery and Africa
Exercise One: Spinning Coins
Assignment: Discovery Seeker: Tell your data’s story as clearly, ethically, accurately, attractively, engagingly and persuasively as you can.
Readings to discuss: none this week
 Testing for Truth: same or different, binomial and chisquare
Assignment: Begin your Quincunx
Readings to discuss: The Lady Tasting Tea, Chapters 35
 Testing for Truth: confirmatory stats and your favorite spreadsheet
Exercise One: A quick measurment
Assignment: Finish Your Quincunx
 Seeing the Future: predictive procedures and opensource stats
Exercise One: Using R
 Graphical Persuasion: how to lie with maps.
Readings to discuss: How to Lie with Maps, Mark Monmonier;
The Ghost Map, Steven Johnson
 Judgment: designing attraction
Readings to discuss: Lady Tasting Tea, chapter 29
Assignments:
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 the next week.
Arduino Data Gathering: Use an Arduino to gather 500 samples of interesting data in two different conditions (1000 samples total). Post your data online so it can be downloaded.
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 selfportrait, using data.
Africa Survey: 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.
Coin Toss: An investigation of the difference between the perception and realities of random
Quincunx: Get to know the normal distribution intimately by building your own quincunx or Galton box. You may use either physical materials or create an imaginative software simulation.
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
To Be Announced
Making the Most of It:
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
“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