3 skills a data scientist needs

LinkedIn's Pete Skomoroch on the key capabilities of data scientists.

By David Sims
January 24, 2011

To prepare for next week’s Strata Conference, we’re continuing our series of conversations with big data innovators. Today, we talk with LinkedIn senior research scientist Pete Skomoroch about the core skills of data scientists.

The first skill, as you might expect, is a base in statistics, algorithms, machine learning, and mathematics. “You need to have a solid grounding in those principles to actually extract signals from this data and build things with it,” Skomoroch said.

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Second, a good data scientist is handy with a collection of open-source tools — Hadoop, Java, Python, among others. Knowing when to use those tools, and how to code, are prerequisites.

The third set of skills focus on making products real and making data available to users. “That might mean data visualization, building web prototypes, using external APIs, and integrating with other services,” Skomoroch said. In other words, this one’s a combination of coding skills, an ability to see where data can add value, and collaborating with teams to make these products a reality.

Skomorich’s position gives him insight into the job market, what jobs are being posted, and who is hiring for which roles. He said he’s glad to see new startups adding a data scientist or engineer to the founding staff. “That’s a good sign.”

Skomorich discusses data science skill sets and related topics in the following video:


Post topics: Data