Data preparation for analytics using SAS

Data preparation for analytics using SAS

Written for anyone involved in the data preparation process for analytics, Gerhard Svolba's Data Preparation for Analytics Using SAS offers practical advice in the form of SAS coding tips and tricks, and provides the reader with a conceptual background on data structures and considerations from...

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Detalles Bibliográficos
Autor Corporativo: SAS Institute Content Provider (content provider)
Otros Autores: Svolba, Gerhard Author (author), SAS Institute Contributor (contributor)
Formato: Libro electrónico
Idioma:Inglés
Publicado: [Place of publication not identified] SAS Institute 2006
Edición:1st edition
Colección:SAS Press series Data preparation for analytics using SAS
Materias:
SAS (Computer file)
Enterprise Miner.
Business > Data processing
Electronic data processing
Commercial analysis
Data marts
Data mining
Time-series analysis
Commerce
Business & Economics
Marketing & Sales
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009627591106719
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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