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What is Data Science?

By   /  November 13, 2017  /  No Comments

Data ScienceData Science is a combination of scientific disciplines “to build predictive models that explore data content patterns.”

Data Science, formerly known as applied statistics:

“Integrates methods from mathematical, statistical, computer science, signal processing, probability modelling, pattern recognition machine learning, uncertainty modeling and data visualization towards gaining insights and predictive behaviors on Big Data sets.”

According to Kelle O’Neal and Charles Roe:

“Data Science allows enterprises the ability to turn their data assets into a narrative…Data Science allows that narrative to be expanded across timelines, in different data spaces that trace from the past into the future, with much more involved questions and answers about an enterprise, different potential outcomes, and repercussions based on recommendations. Data Science employs a range of mathematical, business, and scientific techniques to solve complex problems about an organization’s data assets.”

Other Definitions of Data Science Include:

  • A discipline “extracting insight from information assets for “big data” initiatives and requiring a broad combination of skills.” (Gartner IT Glossary)
  • “The autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI) to discover deeper insights, make predictions or generate recommendations.” (Kelle O’Neal)
  • “An enabler of Data Products…. including most business functions required to design build, and deploy data products.” (Steve Miller).
  • “Analytical and managerial talent to make the most of Big Data.” (University of Berkeley)
  • “How technology has evolved past simple spreadsheet functions or word processing… harvesting insights and analytics from… data collected.” (Scott Matteson)
  • “Data driven decision making.” (Forbes)

Businesses Use Data Science to:


Photo Credit: Panchenko Vladimir /Shutterstock.com

About the author

Michelle Knight enjoys putting her information specialist background to use by writing technical articles on enhancing Data Quality, lending to useful information. Michelle has written articles on W3C validator for SiteProNews, SEO competitive analysis for the SLA (Special Libraries Association), Search Engine alternatives to Google, for the Business Information Alert, and Introductions on the Semantic Web, HTML 5, and Agile, Seabourne INC LLC, through AboutUs.com. She has worked as a software tester, a researcher, and a librarian. She has over five years of experience, contracting as a quality assurance engineer at a variety of organizations including Intel, Cigna, and Umpqua Bank. During that time Michelle used HTML, XML, and SQL to verify software behavior through databases Michelle graduated, from Simmons College, with a Masters in Library and Information with an Outstanding Information Science Student Award from the ASIST (The American Society for Information Science and Technology) and has a Bachelor of Arts in Psychology from Smith College. Michelle has a talent for digging into data, a natural eye for detail, and an abounding curiosity about finding and using data effectively.

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