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Beautiful Data by Toby Segaran, Jeff Hammerbacher

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The Data Scientist

In a recent interview, Hal Varian, Google's chief economist, highlighted the need for employees able to extract information from the Information Platforms described earlier. As Varian puts it, "find something where you provide a scarce, complementary service to something that is getting ubiquitous and cheap. So what's getting ubiquitous and cheap? Data. And what is complementary to data? Analysis."

At Facebook, we felt that traditional titles such as Business Analyst, Statistician, Engineer, and Research Scientist didn't quite capture what we were after for our team. The workload for the role was diverse: on any given day, a team member could author a multistage processing pipeline in Python, design a hypothesis test, perform a regression analysis over data samples with R, design and implement an algorithm for some data-intensive product or service in Hadoop, or communicate the results of our analyses to other members of the organization in a clear and concise fashion. To capture the skill set required to perform this multitude of tasks, we created the role of "Data Scientist."

In the financial services domain, large data stores of past market activity are built to serve as the proving ground for complex new models developed by the Data Scientists of their domain, known as Quants. Outside of industry, I've found that grad students in many scientific domains are playing the role of the Data Scientist. One of our hires for the Facebook Data team came from a bioinformatics ...

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