Data science for business :
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 thinking. Data mining and data-analytic thinking. Foster Provost and Tom Fawcett. - Mumbai : O'Reilly, Shroff Publishers & Distributors, 2017. - xvii, 384 p. : ill. ; 24 cm.
Subtitle from cover.
Includes bibliographical references (pages 359-366) 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.
9789351102670
Oreilly & Associates Inc, C/O Ingram Pub Services 1 Ingram Blvd, LA Vergne, TN, USA, 37086
016444020 Uk
Data mining.
Big data.
Information science.
Business--Data processing.
Data Mining.
Information Science.
Commerce.
Automatic Data Processing.
QA76.9.D343 / P76 2013
006.312 / PRO
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 thinking. Data mining and data-analytic thinking. Foster Provost and Tom Fawcett. - Mumbai : O'Reilly, Shroff Publishers & Distributors, 2017. - xvii, 384 p. : ill. ; 24 cm.
Subtitle from cover.
Includes bibliographical references (pages 359-366) 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.
9789351102670
Oreilly & Associates Inc, C/O Ingram Pub Services 1 Ingram Blvd, LA Vergne, TN, USA, 37086
016444020 Uk
Data mining.
Big data.
Information science.
Business--Data processing.
Data Mining.
Information Science.
Commerce.
Automatic Data Processing.
QA76.9.D343 / P76 2013
006.312 / PRO