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Data science for business: What you need to know about data mining and data-analytic thinking

By: Provost, Foster.
Contributor(s): Tom Fawcett.
Publisher: California, U.S. : Sebastopol O'Reilly , 2013Description: 620 p. Digital.Subject(s): CIENCIA Y TECNOLOGÍA | CIENCIA DE DATOS | CIENTÍFICO DE DATOS | MINERÍA DE DATOSDDC classification: Recurso digital Online resources: ►►DOWNLOAD EBOOK / DESCARGAR LIBRO DIGITAL◄◄
Contents:
REFERENCIA APA: Provost, F. y Fawcett, T. (2013). Data Science for Business: What you need to know about Data Mining and Data-Analytic thinking. California, U.S.: O'Reilly Media.
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 II: toward analytical engineering. Other data science tasks and techniques. Data science and business strategy. Conclusion.
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Libro Digital Libro Digital Biblioteca Lead University
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REFERENCIA APA: Provost, F. y Fawcett, T. (2013). Data Science for Business: What you need to know about Data Mining and Data-Analytic thinking. California, U.S.: O'Reilly Media.

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 II: toward analytical engineering. Other data science tasks and techniques. Data science and business strategy. Conclusion.

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