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001			7750690
003			CaAEU
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008			131111t20142014inua ob 001 0 eng d
020			_a9781118661468
040			_cTBS
041			_aeng
050		4	_aQA76.9.D343
100			_aForeman, John W. _eauthor _921275
245	1	0	_aData smart _b: using data science to transform information into insight _c/ John W. Foreman.
260			_aIndianapolis, IN : _bJohn Wiley & Sons, _c2014.
300			_axx, 409 pages, illustrations, charts, tables (black and white) ; 24 cm.
504			_aIncludes bibliographical references and index.
505			_aEverything you ever needed to know about spreadsheets but were too afraid to ask — Cluster analysis part I : using K-means to segment your customer base — Naïve Bayes and the incredible lightness of being an idiot — Optimization modeling : because that "fresh squeezed" orange juice ain't gonna blend itself — Cluster analysis part II : network graphs and community detection — The granddaddy of supervised artificial intelligence : regression — Ensemble models : a whole lot of bad pizza — Forecasting : breathe easy; you can't win — Outlier detection : just because they're odd doesn't mean they're unimportant — Moving from spreadsheets into R.
520			_aData Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions. But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope. Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet.
650		0	_aData mining _97979
650		0	_aWeb sites _xDesign _97486
650		0	_aWeb usage mining _98233
942			_2lcc
999			_c1602 _d1602