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A Taxonomy of Data Science

Hilary Mason[1] and Chris Wiggins[2]:

We’ve variously heard it said that data science requires some command-line fu for data procurement and preprocessing, or that one needs to know some machine learning or stats, or that one should know how to `look at data’. All of these are partially true, so we thought it would be useful to propose one possible taxonomy — we call it the Snice* taxonomy — of what a data scientist does, in roughly chronological order: Obtain, Scrub, Explore, Model, and iNterpret (or, if you like, OSEMN, which rhymes with possum).

The clearest list of what a modern data scientist is supposed to know and do.


  1. Hilary Mason: Chief Scientist at bit.ly  

  2. Chris Wiggins: Associate Professor in the Department of Applied Physics and Applied Mathematics at Columbia  

Original title and link: A Taxonomy of Data Science (NoSQL databases © myNoSQL)

via: http://www.dataists.com/2010/09/a-taxonomy-of-data-science/