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    The Data Science Report captures the most compelling news, videos, books and events related to hiring and careers within "big data" science and advanced analytics.  If it's important, you'll find it here!

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Thursday, June 29th, 2017

Today's New Articles

Explaining a host of opportunities in data science, Gaurav Vohra busts some related myths. Data Science has emerged as one of the most exciting fields. Since it is a new field there is both excitement and confusion about it. Data science is a beautiful combination of technology, business, and mathematics that impacts every facet of our life...

Book Review: "Big Data" By Bill Kist is a professor at Kent State University.  "You may have been introduced to the concept of "big data" during the last days of the 2012 presidential election, when The New York Times' Nate Silver predicted President Obama's victory as a statistical certainty. In "Big Data," Viktor Mayer-Schonberger, a professor, and Kenneth Cukier, an editor for The Economist, explain how huge data sets and some fast computers can predict not only election results but whether you're going to commit credit card fraud, throw a wrestling match, or get pregnant."

Tony Cosentino of writes, "Big data analytics is being offered as the key to addressing a wide array of management and operational needs across business and IT. But the label “big data analytics” is used in a variety of ways, confusing people about its usefulness and value and about how best to implement to drive business value. The uncertainty this causes poses a challenge for organizations that want to take advantage of big data in order to gain competitive advantage, comply with regulations, manage risk and improve profitability."

WSJ’S BIG DATA SPECIAL REPORT:  Read several articles on Big Data including: "The Era of Big Data", "Big Data, Big Blunders",  "The New Shape of Big Data",  "Drive Into the Future", and "Big Data Help Wanted"...

Big Data Analytics for Dummies is a valuable resource that addresses the practical dilemmas surrounding Big Data and provides a step-by-step approach on how to sidestep common pitfalls, including:

  • How do you give Big Data the right context so that it isn't stuck in isolation?
  • Who can create the analytics you need to make the most of Big Data's potential?
  • What do you need to do to make Big Data usable by decision makers on my team - TODAY?

The yhat blog lists 10 R packages they wish they'd known about earlier. Drew Conway calls them "10 reasons to always start your analysis in R". They're all very useful R packages that every data scientist should be aware of. They are:

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