With big data becoming an important part of the business world, there's a lot of potential for people to get involved in the field. As a result, many are considering a career in data science. However, questions remain on whether that will be enough to help companies conquer the massive amounts of data they contend with and put it to use in predictive modeling software. With machine learning on the rise, the potential for computers replacing data scientists through democratization increases by the day. However, is the fate of data scientist already mothballed, or will machine learning be complement to a strengthening workforce in the field?

"Statisticians with bachelor's degrees grew 300 percent since the 1990s."

An influx of graduates
The press around big data being a fundamental disrupter to everything in society and business inspires a lot of people. The end result is an influx of newly-minted statisticians and mathematics graduates. The American Statistical Association found that bachelor's degrees in statistics increased by 17 percent from 2013 to 2014. It represented a 15-year streak growth, totaling more than 300 percent of the levels seen in the 1990s. Academia's response is to add more statistics programs in various universities at the undergraduate and graduate levels. On top of that, there are various boot camps and MOOCs offering data science courses.

Consequently, recruiting agency Burtch Works found an influx of statistics graduates. The median and mean experience levels for analytics professionals were 9 and 10.5 years in 2015, down a year from 2014. While the number of veterans in the field was relatively the same, the amount of analysts who worked five years or less went up by nearly half.

Automation: Harbinger or supporter?
At the same time, many companies suspect that with such a large demand for data scientists and not enough labor supply, it's highly likely that they'll have to rely on automated platforms to complete many of the insightful tasks that they require. Some may see it as a complete replacement of human contributions outright.

It's not without precedent. MIT News reported on research efforts by the Massachusetts Institute of Technology to see if automation could replace human analytics. Their findings suggested that of the 906 human teams that participated in a live test, automation beat 615 of them in finding predictive patterns, often in the span of two to 12 hours.

This is not to say that taking the human element out of the equation is the answer. Data scientists may still play a role in the future of business, but they will get support from machines such as IBM's Watson and other platforms.

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