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130731s2014 njua 001 0 eng d |
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|a 2013946768
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|a 9781118661468
|q (pbk.)
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|a 111866146X
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|a 9781118661482
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|a 1118661486
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|a (OCoLC)858311212
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|a (OCoLC)ocn858311212
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|a UKMGB
|c DLC
|d UKMGB
|d OCLCL
|d U@S
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|a lccopycat
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|a U@SA
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|a QA76.9.D343
|b F67 2014
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4 |
|a 006.31
|2 23
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| 100 |
1 |
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|a Foreman, John W.
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| 245 |
1 |
0 |
|a Data smart :
|b using data science to transform information into insight /
|c John W. Foreman.
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| 260 |
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|a Hoboken, NJ :
|b John Wiley & Sons,
|c c2014.
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| 300 |
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|a xx, 409 p. :
|b il. ;
|c 24 cm.
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| 490 |
1 |
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|a Wrox professional guides
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| 500 |
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|a Incluye índice.
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| 505 |
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|a Introduction -- 1. Everything you ever needed to know about spreadsheets but were too afraid to ask -- 2. Cluster analysis part I : using K-means to segment your customer base -- 3. Naïve Bayes and the incredible lightness of being an idiot -- 4. Optimization modeling : because that "fresh squeezed" orange juice ain't gonna blend itself -- 5. Cluster analysis part II : network graphs and community detection -- 6. The granddaddy of supervised artificial intelligence : regression -- 7. Ensemble models : a whole lot of bad pizza -- 8. Forecasting : breathe easy; you can't win -- 9. Outlier detection : just because they're odd doesn't mean they're unimportant -- 10. Moving from spreadsheets into R -- Conclusion.
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| 520 |
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|a "Data 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." --Descripción del editor.
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| 650 |
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0 |
|a Data mining.
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| 650 |
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7 |
|a Minería de datos.
|2 UDESA
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| 830 |
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0 |
|a Wrox professional guides
|