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ISBN: 9780470511398
Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better b… Plus…
ISBN: 9780470511398
Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better b… Plus…
ISBN: 9780470511398
Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better b… Plus…
2009, ISBN: 0470511397
Kartoniert / Broschiert, mit Schutzumschlag 11, [PU:John Wiley & Sons]
ISBN: 9780470511398
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Données bibliographiques du meilleur livre correspondant
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Informations détaillées sur le livre - Business Intelligence: Data Mining and Optimization for Decision Making Carlo Vercellis Author
EAN (ISBN-13): 9780470511398
ISBN (ISBN-10): 0470511397
Livre de poche
Date de parution: 2009
Editeur: Wiley Core >1 >T
436 Pages
Poids: 0,612 kg
Langue: eng/Englisch
Livre dans la base de données depuis 2008-05-14T17:17:14+02:00 (Zurich)
Page de détail modifiée en dernier sur 2024-01-16T17:33:19+01:00 (Zurich)
ISBN/EAN: 9780470511398
ISBN - Autres types d'écriture:
0-470-51139-7, 978-0-470-51139-8
Autres types d'écriture et termes associés:
Auteur du livre: carlo may, vercelli
Titre du livre: business intelligence, data mining, decision, data science for business
Données de l'éditeur
Auteur: Carlo Vercellis
Titre: Business Intelligence - Data Mining and Optimization for Decision Making
Editeur: Wiley; John Wiley & Sons
436 Pages
Date de parution: 2009-03-20
Poids: 0,616 kg
Langue: Anglais
67,90 € (DE)
Not available (reason unspecified)
163mm x 228mm x 22mm
BC; Hardcover, Softcover / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik; Wahrscheinlichkeitsrechnung und Statistik; Data Mining; Data Mining Statistics; Statistics; Statistik; Data Mining; Data Mining; BB
Preface. I. COMPONENTS OF THE DECISION MAKING PROCESS. 1. Business intelligence. 1.1 Effective and timely decisions. 1.2 Data, information and knowledge. 1.3 The role of mathematical models. 1.4 Business intelligence architectures. 1.5 Ethics and business intelligence. 1.6 Notes and readings. 2. Decision support systems. 2.1 Definition of system. 2.2 Representation of the decision making process. 2.3 Evolution of information. 2.4 Definition of decision support system. 2.5 Development of a decision support system. 2.6 Notes and readings. 3. Data warehousing. 3.1 Definition of data warehouse. 3.2 Data warehouse architecture. 3.3 Cubes and multidimensional analysis. 3.4 Notes and readings. II. MATHEMATICAL MODELS AND METHODS. 4. Mathematical models for decision making. 4.1 Structure of mathematical models. 4.2 Development of a model. 4.3 Classes of models. 4.4 Notes and readings. 5. Data mining. 5.1 Definition of data mining. 5.2 Representation of input data. 5.3 Data mining process. 5.4 Analysis methodologies. 5.5 Notes and readings. 6. Data preparation. 6.1 Data validation. 6.2 Data transformation. 6.3 Data reduction. 7. Data exploration. 7.1 Univariate analysis. 7.2 Bivariate analysis. 7.3 Multivariate analysis. 7.4 Notes and readings. 8. Regression. 8.1 Structure of regression models. 8.2 Simple linear regression. 8.3 Multiple linear regression. 8.4 Validation of regression models. 8.5 Selection of predictive variables. 8.6 Notes and readings. 9. Time series. 9.1 Definition of time series. 9.2 Evaluating time series models. 9.3 Analysis of the components of time series. 9.4 Exponential smoothing models. 9.5 Autoregressive models. 9.6 Combination of predictive models. 9.7 The forecasting process. 9.8 Notes and readings. 10. Classification. 10.1 Classification problems. 10.2 Evaluation of classification models. 10.3 Classification trees. 10.4 Bayesian methods. 10.5 Logistic regression. 10.6 Neural networks. 10.7 Support vector machines. 10.8 Notes and readings. 11. Association rules. 11.1 Motivation and structure of association rules. 11.2 Single-dimension association rules. 11.3 Apriori algorithm. 11.4 General association rules. 11.5 Notes and readings. 12. Clustering. 12.1 Clustering methods. 12.2 Partition methods. 12.3 Hierarchical methods. 12.4 Evaluation of clustering models. 12.5 Notes and readings. III. BUSINESS INTELLIGENCE APPLICATIONS. 13. Marketing models. 13.1 Relational marketing. 13.2 Salesforce management. 13.3 Business cases. 13.4 Notes and readings. 14. Logistic and production models. 14.1 Supply chain optimization. 14.2 Optimization models for logistics planning. 14.3 Revenue management systems. 14.4 Business cases. 14.5 Notes and readings. 15. Data envelopment analysis. 15.1 Efficiency measures. 15.2 Efficient frontier. 15.3 The CCR model. 15.4 Identification of good operating practices. 15.5 Other models. 15.6 Notes and readings. A Software tools. B Dataset repositories. References. Index.Autres livres qui pourraient ressembler au livre recherché:
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8580000688269 Business Intelligence: Data Mining and Optimization for Decision Making (Vercellis, Carlo)
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