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Data science for business :[what you...

Fawcett, Tom.

Data science for business :[what you need to know about data mining and data-analytic thinking] /

Record Type:	Language materials, printed : Monograph/item
[NT 15000414]:	006.312
Title/Author:	Data science for business : : [what you need to know about data mining and data-analytic thinking] // Foster Provost and Tom Fawcett.
remainder title:	What you need to know about data mining and data-analytic thinking
Author:	Provost, Foster,
other author:	Fawcett, Tom.
Published:	Sebastopol, Calif. : : O'Reilly,, 2013.
Description:	xxi, 386 p. : : ill. ;; 24 cm.
Notes:	Subtitle from cover.
Subject:	Data mining.
Subject:	Big data.
Subject:	Information science.
Subject:	Business - Data processing.
Subject:	Data Mining.
Subject:	Information Science.
Subject:	Commerce.
Subject:	Automatic Data Processing.
ISBN:	9781449361327 (pbk.) :
ISBN:	1449361323 (pbk.)
[NT 15000227]:	Includes bibliographical references (p. 361-368) and index.
[NT 15000228]:	Introduction : data-analytic thinking -- Business problems and data science solutions -- Introduction to predictive modeling : from correlation to supervised segmentation -- Fitting a model to data -- Overfitting and its avoidance -- Similarity, neighbors, and clusters -- Decision analytic thinking I : what is a good model? -- Visualizing model performance -- Evidence and probabilities -- Representing and mining text -- Decision analytic thinking II : toward analytical engineering -- Other data science tasks and techniques -- Data science and business strategy -- Conclusion.
[NT 15000229]:	Provides an introduction to the fundamental principles of data science, walking the reader through the "data-analytic thinking" necessary for extracting useful knowledge and business value from collected data.

Data science for business :[what you need to know about data mining and data-analytic thinking] /
Provost, Foster,1964-

Data science for business :[what you need to know about data mining and data-analytic thinking] /What you need to know about data mining and data-analytic thinkingFoster Provost and Tom Fawcett. - 1st ed. - Sebastopol, Calif. :O'Reilly,2013. - xxi, 386 p. :ill. ;24 cm.

Subtitle from cover.

Includes bibliographical references (p. 361-368) and index.

Introduction : data-analytic thinking -- Business problems and data science solutions -- Introduction to predictive modeling : from correlation to supervised segmentation -- Fitting a model to data -- Overfitting and its avoidance -- Similarity, neighbors, and clusters -- Decision analytic thinking I : what is a good model? -- Visualizing model performance -- Evidence and probabilities -- Representing and mining text -- Decision analytic thinking II : toward analytical engineering -- Other data science tasks and techniques -- Data science and business strategy -- Conclusion.

Provides an introduction to the fundamental principles of data science, walking the reader through the "data-analytic thinking" necessary for extracting useful knowledge and business value from collected data.

ISBN: 9781449361327 (pbk.) :NTD 1,285Subjects--Topical Terms:

337740
Data mining.

LC Class. No.: QA76.9.D343 / P76 2013

Dewey Class. No.: 006.312

Data science for business :[what you need to know about data mining and data-analytic thinking] /
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245 1 0 $a Data science for business : $b [what you need to know about data mining and data-analytic thinking] / $c Foster Provost and Tom Fawcett.
246 1 4 $a What you need to know about data mining and data-analytic thinking
250 $a 1st ed.
260 $a Sebastopol, Calif. : $b O'Reilly, $c 2013.
300 $a xxi, 386 p. : $b ill. ; $c 24 cm.
500 $a Subtitle from cover.
504 $a Includes bibliographical references (p. 361-368) and index.
505 0 $a Introduction : data-analytic thinking -- Business problems and data science solutions -- Introduction to predictive modeling : from correlation to supervised segmentation -- Fitting a model to data -- Overfitting and its avoidance -- Similarity, neighbors, and clusters -- Decision analytic thinking I : what is a good model? -- Visualizing model performance -- Evidence and probabilities -- Representing and mining text -- Decision analytic thinking II : toward analytical engineering -- Other data science tasks and techniques -- Data science and business strategy -- Conclusion.
520 $a Provides an introduction to the fundamental principles of data science, walking the reader through the "data-analytic thinking" necessary for extracting useful knowledge and business value from collected data.
650 0 $a Data mining. $3 337740
650 0 $a Big data. $3 571002
650 0 $a Information science. $3 342336
650 0 $a Business $x Data processing. $3 338369
650 2 $a Data Mining. $3 559047
650 2 $a Information Science. $3 422941
650 2 $a Commerce. $3 339478
650 2 $a Automatic Data Processing. $3 558661
700 1 $a Fawcett, Tom. $3 596854