formatics. NC-DSA rests on three pillars: new interdisciplinary academic programs in data science, state-of-the-art training in big data for working executives and managers, and a industry-university partnership that leverages academic research for business and industry innovation.
“We are in the process of creating a professional science master’s in data science and business analytics,” explains Ott. “We hope to offer it in Charlotte in a couple of years.” A new interdisciplinary professional degree in health informatics, a partnership among the College of Computing and Informatics, the College of Health and Human Services and the University Graduate School, is already being offered this year. Together these education and training programs will produce over 200 grads each year in coming years.
Writing in the October issue of Harvard Business Review, Tom Davenport, who keynoted the May conference in Charlotte, said that currently there are no university programs offering degrees in data science. He noted that North Carolina State is “busy adding big data exercises and coursework” to its master of science in analytics, reflecting the surging demand of talents in this area.
Data science is the new discipline linking informatics and practical applications. “Its practitioners are a new breed,” writes Davenport. “They are a hybrid data hacker, analyst, communicator and trusted advisor.” They focus on the “I” in IT and the “D” in R&D. All are college educated at the bachelor’s level and beyond and conversant with social media.
Some like Andrew Pole at Target are oriented toward the retail sector where they focus on determining what customers need before they know it themselves. Others are car nuts with an eye toward monitoring engine performance, customer satisfaction, social media and shop statistics to reduce repeat repairs.
In short, data scientists will have what Davenport calls “the sexiest job of the 21st century.” He equates this new profession with the “Wall Street quants” (quantitative analysts), physicists and mathematicians who shunned academia for careers with investment firms in the 1980s and ’90s.
When academically trained data scientists reach the marketplace in three to four years, they will join a self-made corps of practical data wranglers. These “grandparents” include D.J. Patil, who co-authored the Harvard Business Review article with Tom Davenport. He is an executive in residence at Greylock Partners in Silicon Valley.
So is Jake Klamka, a physicist who created Insight Data Science Fellowships, a six-week post-doctoral program based in Palo Alto, California. Klamka’s short course bridges the gap between academia and a career in data science. Add Jonathan Goldman, the data scientist that devised the “people you may know” feature on LinkedIn, a virtual space where thousands of data scientists hang out.
For parents needing guidance on post-high school education for their children, Carol Fodell has some advice. She is program director for global university programs at IBM and spoke at the May informatics conference in Charlotte on the topic. “Tell your kids to major in analytics!” she says, “That’s the bedrock of data science—the software and statistical methods organizations use to understand data and manage risk, performance and decisions.”
Even with new programs, more graduates and increased motivation for studying statistics, math, experimental design and visualization, there will continue to be a gap in the United States between high demand for big data talent and low supply. McKinsey Global Institute estimates the gap to be in the area of 50 to 60 percent in 2018.
Whether they come from do-it-yourself post-doctorate programs or UNC Charlotte, data scientists and their big data spinoff occupations are here to stay. A world without computers, smart phones and dozens of yet-to-be-invented devices may be the only aspect of big data that is unimaginable.