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I just came across this blog and thought it was an interesting point. i disagree. but its worth a discussion :)


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Comment by Scott Edwards on May 28, 2014 at 12:51pm

@Amy, why do you want to remove P-values?

Comment by Richard Ordowich on March 7, 2014 at 2:27am

Interesting debate that cannot be solved with data. And herein lies an interesting challenge. How valuable is data to decision making in business, government and everyday life? I think that is a question "real" data scientist should ponder and research. Others who work with data remain statisticians, business analysts and data pushers.

Those employed in the manipulation of data of course have to protect their brand. Vendors of products need to sell their wares and writers, consultants, conference speakers also have a bias to keep the marketing of the criticalness of data alive.

Suggesting that companies will perish and die without data and data scientist is the use of fear to convince people. The other method, the opportunity sell is also over played in the data sphere. "look what you can do with data?" "You have to be an idiot not to be taking advantage of data", are some of the shrill cries we see.

 Are data scientists dead? The first question is "who's asking" and why. Like most questions there is a desired outcome or hypothesis and a "data scientist" would ponder the question and questioner before collecting data to answer the question. The answer lies somewhere perhaps but there are few that present data to support their claims.  The answer we get is yes and no based solely on opinion with little data to back up either argument.

Seems kind of ironic that those who profess expertise with data, don't use data to support their arguments. Perhaps there are no data scientists out there?

Comment by Vincent Granville on March 5, 2014 at 2:49pm

For those companies that can't identify great data sources (usually external data), process it efficiently or outsource to carefully selected vendors, yes, data science is dead. And maybe these dinosaurs will soon die too. In fact, data science is just at its beginning. The potential applications are countless, and we will see the emergence of new companies leveraging big data that does not even exist yet (they'll create it).

Examples include:

  • fraud detection (credit card fraud in real time)
  • designing customized drugs based on patient feedback (the data does not even exist yet)
  • local, long-term weather forecasts (they use graph databases more so than hadoop)
  • signal processing (astronony, sensor data, traffic monitoring and traffic alerts, automated piloting, air and water pollution)
  • high frequency trading
  • econometrics models that could leverage billions of data points / events / metrics
  • competitive intelligence (analysing what users says about your product, competitor products, and finding industry trends to predict booms and busts)
  • detecting new hot topics, predicting epidemics as soon as possible, including how fast/where they spread
  • customized pricing (hotels, air tickets, books) and detection of best deals by browsing billions of web pages
  • plagiarism, spam and copyright infringement detection
  • automated text summaries and sentiment extraction (helping lawyers or recommendation engines)
  • anti-terrorism efforts 
  • mobile data
  • electric grids / energy optimization (more a graph database problem)
  • automated detection of errors and fraud in tax filings (combing IRS data with other sources of data such bank transactions, Twitter discussions etc.)



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