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Business Intelligence - Prima edizione

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Data Mining and Optimization for Decision Making, eBooks, eBook Download (EPUB), 1. Auflage, Business intelligence is a broad category of applications andtechnologies for gathering, provi… Altro …

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Business Intelligence : Data Mining and Optimization for Decision Making - nuovo libro

ISBN: 9781119965473

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… Altro …

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Carlo Vercellis:
Business Intelligence - nuovo libro

2011

ISBN: 9781119965473

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… Altro …

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A digital copy of "Business Intelligence" by Versellis. Download is immediately available upon purchase! 9781119965473,1119965470,business,intelligence,versellis eBook, Vitalsource Techn… Altro …

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Business Intelligence - nuovo libro

ISBN: 9781119965473

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Business Intelligence
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1119965470

Informazioni dettagliate del libro - Business Intelligence


EAN (ISBN-13): 9781119965473
ISBN (ISBN-10): 1119965470
Anno di pubblicazione: 2011
Editore: John Wiley & Sons

Libro nella banca dati dal 2008-12-25T08:48:52+01:00 (Zurich)
Pagina di dettaglio ultima modifica in 2023-08-14T20:05:33+02:00 (Zurich)
ISBN/EAN: 1119965470

ISBN - Stili di scrittura alternativi:
1-119-96547-0, 978-1-119-96547-3
Stili di scrittura alternativi e concetti di ricerca simili:
Titolo del libro: intelligence, business, data mining

Dati dell'editore

Autore: Carlo Vercellis
Titolo: Business Intelligence - Data Mining and Optimization for Decision Making
Editore: Wiley; John Wiley & Sons
436 Pagine
Anno di pubblicazione: 2011-08-10
Lingua: Inglese
56,99 € (DE)
Not available (reason unspecified)

EA; E101; E-Book; Nonbooks, PBS / Mathematik/Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik; Wahrscheinlichkeitsrechnung und Statistik; Business & Management; Data Analysis; Data Mining; Datenanalyse; Organisations- u. Managementtheorie; Organization & Management Theory; Statistics; Statistik; Wirtschaft u. Management; Organisations- u. Managementtheorie; Datenanalyse; BB; BC

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.
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