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Analyzing the Analyzers by Marck Vaisman, Sean Murphy, Harlan Harris

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Chapter 5. Data Scientists and Organizations

Let’s turn the focus around now, and consider how our survey results might inform communication and career path problems from the point of view of organizations that need data scientists.

Where Data People Come From: Science vs. Tools Education

Tools are critical to data scientists’ effectiveness. The maturity of current tools allows an individual to at least roughly perform all of the steps needed to develop insights and build data products. However, we feel that evidence points in favor of a scientific versus a tools-based education for data scientists. Along with technical expertise, the scientific mentoring process builds and rewards curiosity, storytelling, and cleverness (DJ Patil, 2011). Our survey results support this notion, with 70% of our respondents having at least a Master’s degree, and scientific fields (social or physical sciences, but not math, computer science, statistics, or engineering) making up about 40% of reported undergraduate degrees.

Furthermore, post-graduate education in the sciences provides hands-on experience working with real data, not just to describe a phenomenon, but to evaluate a theory or argue a position. Disciplines such as physics and astronomy teach rigorous statistical thinking, while systems such as particle accelerators and space telescopes provide massive streams of data requiring careful data curation. (Data from the under-construction Square Kilometer Array telescope is expected to be collected ...

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