Fall 2016 RED Talks

Elliot Inman

Dr. Elliot Inman

“Quantification: The Art of Making Data”
Sept 28th, 7:00 p.m.
Mountains Ballroom, Talley Student Union


Abstract:  
In this Data Science Initiative talk and the DH Hill Makerspace workshops that follow, we will explore the art of making data – from the mechanics of setting up sensors and digitizing information to generating a data structure that will be useful for making sense of that information. We will experiment with a wide variety of data from recorded sound to human touch and even human emotions, building devices to help gather reliable, valid measures.

Long before there was such a thing as “Data Science” or a “Makerspace,” the engineer Herman Hollerith created an electromechanical tabulation machine, a device that allowed for the statistical summary of data via the use of paper cards with holes “punched” out to represent data. Those “punched card” systems, first built more than 100 years ago, enabled rapid, accurate tabulation of the US Census. They were extremely powerful, but extremely expensive.

These days, homebrew hobbyists, hackers, and makers have access to inexpensive microcontrollers and other electronic components necessary to build devices that can actually look and listen and digitize the output of silicon senses. For less than $50 in parts, inventors can build a device that will instantly publish a stream of binary artifacts to a global audience. But there is a significant difference between a flash-flood of timestamped binary records and a dataset with measures that will help to understand what is happening.

In this talk and the accompanying workshops, we will discuss how the technical specifications of the device/circuit affects how we collect information and, conversely, how our operational definition of the measures of interest should guide decisions about how to design those devices/circuits to gather useful data. We will discuss:

  • How quickly and how often do we need to digitize a signal to capture what we want to measure?
  • How should we structure output data differently if the output is to be read by a person or a machine?
  • How much (or how little) do we need to save to a digital record to make sense of the data?
  • What kinds of additional data will we want to match to those records and how can we structure our output to allow us to do so?

This RED talk will coincide with the following three workshops in Makerspace at DH Hill Library:

  1. The Art of Making Data:  Quantifying Touch – Sept 29th, 1:00 – 3:00
    • We will use an Arduino and sensors to gather data on simple human hand gestures: pressing a button, turning a dial, and waving a hand in front of an electronic eye. We will setup the Arduino to save data in a manner that allows us to use the digitized records for statistical analysis.
  2. The Art of Making Data:  Quantifying Sound – Sept 29th, 3:30 – 5:30
    • We will build a simple contact microphone and record sounds using Audacity. We will set up Audacity to save sound data so that the digitized records can be used for statistical analysis.
  3. The Art of Making Data:  Quantifying Attitudes and Emotions – Sept 30th, 10:00 – 12:00
    • We will build an audience response meter using an Arduino to capture audience emotional responses to a video. We will match those data to the content of the video so that we can conduct a statistical analysis of the resulting data.

Note: For all workshops, all materials and software, including a no-cost online SAS Studio account for statistical analysis, will be provided to participating NCSU students. Please see the DH Hill Makerspace website to register for a particular session.

About Dr. Inman:  Elliot Inman, Ph.D., is a Manager of Software Development for SAS® Solutions OnDemand. Over the past 25 years, he has analyzed a wide variety of data in areas as diverse as the effectiveness of print and digital advertising, social service outcomes analysis, healthcare claims analysis, employee safety, educational achievement, clinical trial monitoring, sales forecasting, risk-scoring and fraud analytics, general survey analysis, performance benchmarking, product pricing, text processing, and basic scientific research on human memory and cognitive processes. After completing his undergraduate degree at North Carolina State University, he went on to earn a Ph.D. in Experimental Psychology from the University of Kentucky in 1997.

Reception to immediately follow in the Piedmont Ballroom.