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Carlo Vercellis:

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

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… mais…

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Carlo Vercellis:
Business Intelligence - nuovo livro

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… mais…

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Business Intelligence - nuovo livro

ISBN: 9781119965473

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1119965470

Dados detalhados do livro - Business Intelligence


EAN (ISBN-13): 9781119965473
ISBN (ISBN-10): 1119965470
Ano de publicação: 2011
Editor/Editora: John Wiley & Sons

Livro na base de dados desde 2008-12-25T05:48:52-02:00 (Sao Paulo)
Página de detalhes modificada pela última vez em 2023-08-14T15:05:33-03:00 (Sao Paulo)
Número ISBN/EAN: 1119965470

Número ISBN - Ortografia alternativa:
1-119-96547-0, 978-1-119-96547-3
Ortografia alternativa e termos de pesquisa relacionados:
Título do livro: intelligence, business, data mining

Dados da editora

Autor: Carlo Vercellis
Título: Business Intelligence - Data Mining and Optimization for Decision Making
Editora: Wiley; John Wiley & Sons
436 Páginas
Ano de publicação: 2011-08-10
Língua: Inglês
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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