“Data science” is hot right now. The number of undergraduate degrees in statistics has tripled in the past decade, and as a statistics professor, I can tell you that it isn’t because freshmen love statistics.
Way back in 2009, economist Hal Varian of Google dubbed statistician the “next sexy job.” Since then, statistician, data scientist and actuary have topped various “best jobs” lists. Not to mention the enthusiastic press coverage of industry applications: Machine learning! Big data! AI! Deep learning!
But is it good advice? I’m going to voice an unpopular opinion for the sake of starting a conversation. Stats is indeed useful, but not in the way that the popular media – and all those online data science degree programs – seem to suggest….
While all the press tends to go to the sensationalist applications – computers that watch cat videos, anyone? – the data science boom reflects a broad increase in demand for data literacy, as a baseline requirement for modern jobs.
The “big data era” doesn’t just mean large amounts of data; it also means increased ease and ability to collect data of all types, in all walks of life. Although the big five tech companies – Google, Apple, Amazon, Facebook and Microsoft – represent about 10 percent of the U.S. market cap and dominate the public imagination, they employ only one-half of one percent of all employees.
Therefore, to be a true revolution, data science will need to infiltrate nontech industries. And it is. The U.S. has seen its impact on political campaigns. I myself have consulted in the medical devices sector. A few years back, Walmart held a data analysis competition as a recruiting tool. The need for people that can dig into the data and parse it is everywhere.
In a speech at the National Academy of Sciences in 2015, Steven “Freakonomics” Levitt related his insights about the need for data-savvy workers, based on his experience as a sought-after consultant in fields ranging from the airline industry to fast food….(More)”.