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Posted by James Kobielus on November 17, 2011
Data scientists don’t work in isolation. As with any scientists, they rely on a wide range of people in adjacent roles to help them do their jobs as effectively as possible.
Think about science generally. In the historical development of modern science, the specialization of roles continues to proliferate. But today’s professional science establishment is a relatively recent phenomenon. Back in the Middle Ages — and even well into the modern era — scientists often had to be jacks of all trades in order to carry on their investigations. Until the 19th century, there were few professional scientists, research universities, or commercial labs. There were no eager, underpaid graduate students to press into service. Until the 20th century, most professional scientists had to build and maintain their own laboratories, invent and calibrate their own instruments, painstakingly record their own observations, and concoct and promote their own theories.
Today’s professional scientists — of which data scientists are a key category — have it much easier. Whether they work with particle accelerators or linear regression models, scientists know they don’t need to be their own chief cooks and bottle washers. They can make science their day job and rely on a host of others for all of the necessary supporting tools and infrastructure. We find the following broad division of labor in all of today’s scientific disciplines, including data science:
Increasingly, today’s data scientists realize they must stand on the giant shoulders of social networks and other online forums to pool their collective brainpower.