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This seems to be a response to Amy's discussion Data Scientist vs. Statistician.

Some short thoughts:

  • Does keyword popularity define the usefulness of a discipline?
  • Often the statistician needs to create tools in order to get the most out of small samples. The techniques that are developed are probably less useful for big data.
  • Those statisticians that are developing techniques without data are often creating the tools that will be used in the future. Think of how number theory was thought of as a purely theoretical pursuit and is used in every cryptographic application.
  • I'm not even sure that big-data is a statistical problem unless the 'sub-populations' become small in sample-size.

Enjoy...

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Comment by Milla Bakhareva on October 6, 2013 at 12:58pm

Well, have similar thoughts, thanks. I think that big-data analysis is not a statistical problem, however efficient Big Data analysis means exactly this - reducing Big Data into the sample-size selected data-sets. Those sample sets are by-products of complex prior big data filtering that is based on all kinds of data-mining techniques that are relevant to the task in hand, including statistical analysis when relevant. As for does keyword popularity define the usefulness of a discipline. It does not, of course or it does but only for a text mining purpose. Thanks.

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