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Dan Woods of Forbes published interesting interviews with some of the leading data scientists
in the field
- Pat Hanrahan, chief scientist and co-founder, Tableau Software on "What is a Data Scientist?"
A restrictive conception of the data scientist would be someone almost oracle-like, isolated and crunching algorithms on enterprise-scale databases. But because of the emergence of new tools and the huge influx of data from millions of connected devices - and the need for organizations to make quick, data-driven decisions - the definition of "data scientist" could be broadened to cover almost everyone who works with data in an organization - which is almost everyone, period, Hanrahan says. "At the most basic level, you are a data scientist if you have the analytical skills and the tools to 'get' data, manipulate it and make decisions with it," he says.
- Monica Rogation, Senior Data Scientist, LinkedIn on "What is a Data Scientist?"
By definition all scientists are data scientists. In my opinion, they are half hacker, half analyst, they use data to build products and find insights. It's Columbus meet Columbo - starry eyed explorers and skeptical detectives.
- Daniel Tunkelang, Principal Data Scientist, LinkedIn, on "What is a Data Scientist?"
I'm a big fan of Hilary Mason, chief scientist at bit.ly, so I'll cite her definition: a data scientist is someone who can obtain, scrub, explore, model and interpret data, blending hacking, statistics and machine learning. Data scientists not only are adept at working with data, but appreciate data itself as a first-class product. At LinkedIn, products pioneered by data scientists, such as People You May Know, harness the power of data to create value for users.
- EMC Greenplum's Steven Hillion on What Is a Data Scientist?
To Hillion, data scientists are "analytically-minded, statistically and mathematically sophisticated data engineers who can infer insights into business and other complex systems out of large quantities of data." The skill set of the data scientist goes beyond the capabilities of what many would call "traditional business intelligence (BI)." Traditional BI is interested in the "what and the where," while data scientists are interested in the "how and why," Hillion says. "They're interested in inferring things that are not already present in the data."
- Amazon's John Rauser on "What Is a Data Scientist?")
Is it possible that a 250-year-old mathematician's discovery holds the ideal template for a new, future-ready breed of technologist, one who will be capable of extracting value and wisdom from the mounting deluge of "big data" from connected devices? That is the central theory of John Rauser, principal engineer at Amazon.com, who spoke recently at O'Reilly's Strata Conference in New York.
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