There is a shortage of data scientists. In the United States, some estimates are that there are only enough qualified data scientists to fill 50% of the open data science jobs. This large gap is driven by the massive impact that data science is having on modern business and how a growing number of companies that adopt data science and artificial intelligence (AI) build significant competitive advantages in crowded markets.
In response to this gap, some companies have begun considering methods to apply “self-service data science” with the goal of finding ways to allow their existing teams to utilize the power of data science without requiring the mathematics training and expertise that a professional data scientist requires.
Is self-service data science possible? This is not a crazy idea since many of the products that business professionals use today started out as high-touch applications. Email, file sharing and CRMs originally required deep expertise or an administrator to set up and use but have since become self-service commodities. When was the last time you spoke with a human agent when checking in for a flight at the airport? Today, all or the majority of the check-in process is handled online on a computer, via a mobile phone or at an in-airport check-in kiosk.
What exactly is self-service data science, and what would such an application do? How would it work? To make it easier to understand, let us break down exactly what we should expect from this new kind of application by looking at the phrase itself.
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