Data preparation for analytics using SAS /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Svolba, Gerhard
Autor Corporativo: SAS Institute
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cary, NC : SAS Institute, 2006.
Colección:SAS Press series.
Temas:
SAS (Computer file)
Enterprise miner.
Enterprise Miner.
Enterprise miner
Business > Data processing.
Electronic data processing.
Data marts.
Data mining.
Time-series analysis.
Data Mining
Gestion > Informatique.
Datamarts.
Exploration de données (Informatique)
Série chronologique.
BUSINESS & ECONOMICS > Management Science.
BUSINESS & ECONOMICS > Organizational Behavior.
BUSINESS & ECONOMICS > Industrial Management.
BUSINESS & ECONOMICS > Management.
Commercial analysis.
Business > Data processing
Data marts
Data mining
Electronic data processing
Time-series analysis
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • pt. 1. Data preparation: business point of view
  • ch. 1. Analytic business questions
  • Ch. 2. Characteristics of analytic business questions
  • Ch. 3. Characteristics of data sources
  • Ch. 4. Different points of view on analytic data preparation
  • pt. 2. Data structures and data modeling
  • Ch. 5. The origin of data
  • Ch. 6. Data models
  • Ch. 7. Analysis subjects and multiple observations
  • Ch. 8. The one row-per-subject data mart
  • Ch. 9. The multiple-rows-per-subject data mart
  • Ch. 10. Data structures for longitudinal analysis
  • Ch. 11. Considerations for data marts
  • Ch. 11. Considerations for predictive modeling
  • pt. 3. Data mart coding and content
  • Ch. 13. Accessing data
  • Ch. 14. Transposing one- and multiple-rows-per-subject data structures
  • Ch. 15. Transposing longitudinal data
  • Ch. 16. Transformations of interval-scaled variables
  • Ch. 17. Transformations of categorical variables
  • Ch. 18. Multiple interval-scaled observations per subject
  • Ch. 19. Multiple catagorical observations per subject
  • Ch. 20. Coding for predictive modeling
  • Ch. 21. Data preparation for multiple-rows-per-subject and longitudinal data marts
  • pt. 4. Sampling, scoring, and automation
  • Ch. 22. Sampling
  • Ch. 23. Scoring and automation
  • Ch 24. Do's and don'ts when building data marts
  • pt. 5. Case studies.

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