Weekly Round-Up: NSA, Data Science History, Best Practices, and Robot Writers

Welcome back to the round-up, an overview of the most interesting data science, statistics, and analytics articles of the past week. This week, we have 4 fascinating articles ranging in topics from the NSA's data collection practices to machines writing for the CIA. In this week's round-up:

  • Under the Covers of the NSA’s Big Data Effort
  • A Very Short History Of Data Science
  • 7 Habits of Highly Successful Big Data Pioneers
  • CIA Invests in Narrative Science and Its Automated Writers

Under the Covers of the NSA’s Big Data Effort

This is an interesting article about the types of technologies the NSA is using in their data collection practices and what they can and can't do with those technologies. The article also hypothesizes as to how much data they are able to collect and analyze.

A Very Short History Of Data Science

For those interested in how data science originated and has progressed up until current day, this Forbes article should be a worthwhile read. The article starts off in 1962 with John W. Tukey's paper titled "The Future of Data Analysis" and walks you through major milestones in the field up through September of 2012 when Tom Davenport and DJ Patil declared data scientist the sexiest job of the 21st century.

7 Habits of Highly Successful Big Data Pioneers

In the spirit of the 7 Habits of Highly Effective People, this Smart Data Collective article lists 7 habits for succeeding as a big data practitioner. The habits listed range from planning properly and making wise financial decisions when evaluating technologies to being flexible and adaptable when obstacles present themselves.

CIA Invests in Narrative Science and Its Automated Writers

This is an interesting article about a company called Narrative Science and their services that will be used by the CIA and the broader intelligence community in the near future. The company's product is able to transform data into sentences automatically and is currently being used to write up sports summaries from box scores and earnings reports from stock data.

That's it for this week. Make sure to come back next week when we’ll have some more interesting articles! If there's something we missed, feel free to let us know in the comments below.

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