Finding Long-Term Solutions to the Data Scientist Shortage
Client: Continuum Analytics
As we learned in the first part of this series, the gap between demand for skilled data scientists and supply is driving salaries north of $200,000 in some areas of the country. If big data analytics is to be democratized, steps must be taken to ensure that this short-term misalignment doesn’t turn into a long-term problem. Here are several ways the data scientist shortage is being addressed.
Perhaps the most straightforward way to approach the data scientist shortage is simply to train more of them. To that end, universities around the country are ramping up data science graduate programs. In just a few short years, dozens of major universities–from Kennesaw State University and USC to NYU and University of Tennessee-Knoxville–have launched two-year PhD- and Master’s level programs, and interest among college graduates is reported to be high.
If big data analytics is here for the long term—and there’s every reason to think that it is—then we need to ramp up data science education in a big way. That means we should all be thinking predictively, as the Dean of Big Data Bill Schmarzo encourages. But it also means doing stuff now to prepare children to be the next-generation of data scientists and business people with data science awareness.
There’s one simple change that the American educational system could make to address this: teach programming.
Oliphant, who used to teach at a university, also thinks linear algebra and probability theory should be taught in high school. Instead of exposing young minds to those ideas, kids are conditioned to be scared of math. “I’d actually be driving education in the schools differently, because you really have to start the pipeline,” he says. “I was an academic for years. I taught at university for a long time. I see the gaps. I know what they’re learning, and I know it’s not quite right. It needs to be more practical.
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