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Data sciences for business / Foster Provost, Tom Fawcett.

Por: Provost, FosterColaborador(es): Fawcett, Tom (Autor)Tipo de material: TextoTextoSeries Data science / businessEditor: Sebastopol: O'Reilly Media, 2013Descripción: 386 páginas: ilustracionesISBN: 9781449361327Tema(s): Minería de datos | Análisis de datos | Procesamiento de datos -- NegociosClasificación CDD: 006.312
Contenidos parciales:
1. Introduction: Data-Analytic Thinking.--2. Business Problems anda Data Science Solutions.--3. Introduction to Predictive Modeling: From Correlation to Supervised Segmentation.-- 4. Fitting and Model ti Data.-- 5. Overfitting and Its Avoidance.-- 6. Similarity, Neighbors, and Clusters.--7. Decision Analytic Thinking I: What Is a Good Model?.-- 8. Visualizing Model Performance.-- 9. Evidence and Probabilities.-- 10. Representing and Mining Text.-- 11. Decision Analytic Thinking II: Toward Analytical Engineering.-- 12.Other Data Science Task and Techniques.-- 13. Data Science and Business Strategy.-- 14. Conclusion.
Resumen: This broad, deep, but not-too-technical guide introduces you to the fundamental principles of data science and walks you through the "data-analytic thinking" necessary for extracting useful knowlegde and business value from the data you collect. By learning data science principles, you will understand the many data mining techniques in use today.
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Libros Libros Biblioteca Central
CIENCIAS PURAS 		006.312 P969d (Navegar estantería) Ej.1 En catalogación Procesos técnicos B0587

Incluye índice de materias.

1. Introduction: Data-Analytic Thinking.--2. Business Problems anda Data Science Solutions.--3. Introduction to Predictive Modeling: From Correlation to Supervised Segmentation.-- 4. Fitting and Model ti Data.-- 5. Overfitting and Its Avoidance.-- 6. Similarity, Neighbors, and Clusters.--7. Decision Analytic Thinking I: What Is a Good Model?.-- 8. Visualizing Model Performance.-- 9. Evidence and Probabilities.-- 10. Representing and Mining Text.-- 11. Decision Analytic Thinking II: Toward Analytical Engineering.-- 12.Other Data Science Task and Techniques.-- 13. Data Science and Business Strategy.-- 14. Conclusion.

This broad, deep, but not-too-technical guide introduces you to the fundamental principles of data science and walks you through the "data-analytic thinking" necessary for extracting useful knowlegde and business value from the data you collect. By learning data science principles, you will understand the many data mining techniques in use today.

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