Big Data – Big Money – Become a Data Scientist | New Horizons Career Development
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November 16, 2018
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November 16, 2018

Big Data – Big Money – Become a Data Scientist

Looking to make big money working in IT? Want to become a specialist in high-demand whose work focuses on the heart and soul of the business? You can become…

  • What Claire Cain Miller of the NY Times calls, “…the magicians of the Big Data era. They crunch the data, use mathematical models to analyze it and create narratives or visualizations to explain it, then suggest how to use the information to make decisions.”
  • What Douglas Merrill, writing in Forbes describes as “…people who love data, and have their own unique way of looking at it. Diversity matters and diversity includes both the math and the intuition. Truly, art and science, intertwined.”
  • What Anjul Bhambhri, vice president of big data products at IBM, refers to as “…somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organization.”
  • What Rachel Schutt at Johnson Research Labs describes as “a hybrid computer scientist software engineer statistician. The best tend to be really curious people, thinkers who ask good questions and are OK dealing with unstructured situations and trying to find structure in them.”
  • What Harvard Business Review has referred to as “the sexiest job of the 21st Century

Become a Data Scientist

“Big Data” was not created by a sudden burst of new information magically appearing in databases all over the world.  It was created by significant advances in the ways in which we collect data and the technologies available to us. It’s the result of the proliferation of many new tools, new devices, and new ways in which we collect massive amounts of data.

The challenge is that all the data in the world has no value until it is collated, structured, organized, processed, sorted, indexed, examined, analyzed and converted into useful information that a decision-maker can easily access to help them make effective decisions that result in useful, profitable action.

Think for a moment about all that it takes to accomplish all that.

  • Collect – First someone needs to deploy the technologies and systems that will collect data, including the installation and implementation of an array of sensors often across widely diverse geographic locations.
  • Transport – All that collected data needs to be transported and gathered somewhere.
  • Storage – Depending upon the volume of data, the variety of data types involved, and the velocity with which it must be transported, sophisticated storage solutions must be designed and deployed to contain all that data, with tremendous capacity for expansion.
  • Curation – Anticipating that collected data will be reused repeatedly over its useful life, it will be critical to carefully curate available data entities to make them readily available when needed.
  • Processing – Again due to the tremendous volume, variety and velocity of data involved, the processing of all of this data requires the use of sophisticated data manipulation tools in a massively distributed architecture to extract useful information in anything resembling a reasonable amount of time.
  • Visual Analytics – Once the data has all been processed, it must be published. Old-style reports won’t work in an environment where there’s so much data to report on. The results of the processing must be reported using interactive visual displays, “dashboards” that allow the non-technical executive user to immediately, intuitively interpret what they’re looking at with the ability to easily “drill-down” into the supporting layers of facts.
  • Deeper Interpretation – An entire new multi-disciplinary field of professional study has been emerging at many universities that combines statistics, analytics, computer science, database administration, math, predictive modeling, and business strategy with the synthetic and presentation skills needed to bring it all together into meaningful, actionable information.

All of these are within the job description for a Data Scientist. To be able to collect, collate, analyze, and report with real business value requires inter-disciplinary training that goes beyond just the software and hardware required to process the data. It also includes statistics, analytics, computer science and database administration, math, predictive modeling, business strategy and other eclectic specialties related to quality synthetic construction.

People who can handle all this command top-dollar incomes.

Seek New Horizons

Data Science is the next new horizon in IT, getting deeper into the data and returning quality knowledge that provides extraordinary decision-support for your enterprise. To learn more about learning how to be a data scientist, contact your New Horizons representative today.

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