"four Industries Most in Need of Data Scientists"
Quoting from the site wsj.com
Posted by:Dr Ahmed.M.Shaban
Data scientist—a new occupation that blends business savvy, mathematical wizardry and computer know-how—is data analytics’ career of the moment. Currently boasting superstar status in the corporate world, these number crunchers find hidden insights in large, complex sets of data that don't typically mesh.
While demand for data scientists is growing across many industries, some businesses are going all out to shore up their data militia. Anyone looking for a career in data science may want to consider these four industries in particular—they’re hiring.
Agriculture
Relying on sensors in farm machinery, in soil and on planes flown over fields, precision agriculture is an emerging practice in which growing crops is directed by data covering everything from soil conditions to weather patterns to commodity pricing. “Precision agriculture helps you optimize yield and avoid major mistakes,” says Daniel Castro, director of the Center for Data Innovation, a think tank in Washington, D.C. For example, farmers traditionally have planted a crop, then applied fertilizer uniformly across entire fields. Data models allow them to instead customize the spread of fertilizer, seed, water and pesticide across different areas of their farms—even if the land rolls on for 50,000 acres.
Relying on sensors in farm machinery, in soil and on planes flown over fields, precision agriculture is an emerging practice in which growing crops is directed by data covering everything from soil conditions to weather patterns to commodity pricing. “Precision agriculture helps you optimize yield and avoid major mistakes,” says Daniel Castro, director of the Center for Data Innovation, a think tank in Washington, D.C. For example, farmers traditionally have planted a crop, then applied fertilizer uniformly across entire fields. Data models allow them to instead customize the spread of fertilizer, seed, water and pesticide across different areas of their farms—even if the land rolls on for 50,000 acres.
Finance
Big data promises to discover better models to gauge risk, which could minimize the likelihood of scenarios such as the subprime mortgage meltdown. Data scientists, though, also are charged with many less obvious tasks in the financial industry, says Bill Rand, director of the Center for Complexity in Business at the University of Maryland. He points to one experiment that analyzed keywords in financial documents to identify competitors in different niches, helping pinpoint investment opportunities.
Big data promises to discover better models to gauge risk, which could minimize the likelihood of scenarios such as the subprime mortgage meltdown. Data scientists, though, also are charged with many less obvious tasks in the financial industry, says Bill Rand, director of the Center for Complexity in Business at the University of Maryland. He points to one experiment that analyzed keywords in financial documents to identify competitors in different niches, helping pinpoint investment opportunities.
Government
Government organizations have huge stockpiles of data that can be applied against all sorts of problems, from food safety to terrorism. Joshua Sullivan, a data scientist who led the development of Booz Allen Hamilton’s The Field Guide to Data Science, cites one surprising use of analytics concerning government subsidies. “They created an amazing visualization that helped you see the disconnect between the locations of food distribution sites and the populations they served,” Sullivan says. “That's the type of thing that isn't easy to see in a pile of static reports; you need the imagination of a data scientist to depict the story in the data.”
Government organizations have huge stockpiles of data that can be applied against all sorts of problems, from food safety to terrorism. Joshua Sullivan, a data scientist who led the development of Booz Allen Hamilton’s The Field Guide to Data Science, cites one surprising use of analytics concerning government subsidies. “They created an amazing visualization that helped you see the disconnect between the locations of food distribution sites and the populations they served,” Sullivan says. “That's the type of thing that isn't easy to see in a pile of static reports; you need the imagination of a data scientist to depict the story in the data.”
Pharma
Developing a new drug can take more than a decade and cost billions. Data tools can help take some of the sting out, pinpointing the best drug candidates by scanning across pools of information, such as marketing data and adverse patient reactions. “We can model data and prioritize which experiments we take [forward],” Sullivan says. “Big data can help sort out the most promising drugs even before you do experiments on mice. Just three years ago that would have been impossible. But that's what data scientists do—they tee up the right question to ask.”
Developing a new drug can take more than a decade and cost billions. Data tools can help take some of the sting out, pinpointing the best drug candidates by scanning across pools of information, such as marketing data and adverse patient reactions. “We can model data and prioritize which experiments we take [forward],” Sullivan says. “Big data can help sort out the most promising drugs even before you do experiments on mice. Just three years ago that would have been impossible. But that's what data scientists do—they tee up the right question to ask.”
Ultimately, the industries most needing data scientists will be those facing the most complex questions, along with those wading through the greatest variety of data formats (pictures, emails, text, etc.) from assorted sources. And still, the best opportunities for data scientists may go to the most creative thinkers. For example, Rand imagines a time when law firms will perform text analytics on legal briefs, to compare them to write-ups on previous trials. The goal: to determine which arguments would land best with certain judges
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