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Data science for business : what you need to know about data mining and data-analytic thinking Foster Provost and Tom Fawcett

By: Contributor(s): Publication details: Shroff Publishers and Distributors New Mumbai 2014Description: xvii, 384 p PaperISBN:
  • 978-9351102670
Subject(s): DDC classification:
  • 006.312
Contents:
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.
Summary: 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.
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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
Book Book Main Library ON SHELF Informatio 006.312/ Pro/Faw/ 29911 (Browse shelf(Opens below)) Available 11129911
Total holds: 0
Browsing Main Library shelves, Shelving location: ON SHELF Close shelf browser (Hides shelf browser)
004.6/ Rav/ 30636 Ergonomics and IT 004.6/ Rav/ 30713 Ergonomics and IT 006.312/ Pro/Far/ 31976 Data science for business: 006.312/ Pro/Faw/ 29911 Data science for business : 006.7/ And/Tha/ 30277 Multimedia systems design 384/ Pra/Sri/ 29882 The dynamics of spectrum management : legacy, technology, and economics

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.

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