When All Science becomes Data Science

Ed Lazowska, who holds the Bill & Melinda Gates Chair in Computer Science & Engineering at UW, believes that data-driven discovery will become the norm, as he told Science Careers in a recent interview. This new environment, he says, will create and reward researchers (like Loebman) who are well versed in both the methodologies of their specific fields and the applications of data science. He calls such people “pi-shaped” because they have two full legs, one in each camp.

“All science is fast becoming what is called data science,” says Bill Howe of UW’s eScience Institute. Today, there are sensors in gene sequencers, telescopes, forest canopies, roads, bridges, buildings, and point-of-sale terminals. Every ant in a colony can be tagged. The challenge is to extract knowledge from this vast quantity of data and transform it into something of value. Lately, Lazowska says, he has been hearing this refrain from researchers in engineering, the sciences, the social sciences, law, medicine, and even the humanities: “I am drowning in data and need help analyzing and managing it.”

Learning to code, and becoming comfortable with large datasets, may soon be a necessity in many traditional scientific fields. Many scientists already write scripts for the “plumbing” that automates routine data-related tasks and moves data around among various analysis tools. Those basic skills—and that basic infrastructure—sets the stage for more rapid, automated data management. But, to make optimal use of that rapidly accumulating data, they need additional computer expertise, in databases, visualization, machine learning, and parallel systems.

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