d 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.