Data preparation for analytics using SAS
By: Gerhard Svolba.
Contributors: SAS Institute.
Series: SAS Press series
Publisher: SAS Institute
Published: 2006
Place of publication: Cary, NC
Language: eng
Number of pages: 408
ISBN-13: 9781599940472
Subjects: SAS (Computer file); Enterprise Miner.; Business -- Data processing.; Electronic data processing.; Commercial analysis.; Data marts.; Data mining.; Time-series analysis.
Notes: Includes index.
Table of contents: title: Pt. 1. Data preparation: business point of view; title: ch. 1. Analytic business questions; title: Ch. 2. Characteristics of analytic business questions; title: Ch. 3. Characteristics of data sources; title: Ch. 4. Different points of view on analytic data preparation; title: Pt. 2. Data structures and data modeling; title: Ch. 5. The origin of data; title: Ch. 6. Data models; title: Ch. 7. Analysis subjects and multiple observations; title: Ch. 8. The one row-per-subject data mart; title: Ch. 9. The multiple-rows-per-subject data mart; title: Ch. 10. Data structures for longitudinal analysis; title: Ch. 11. Considerations for data marts; title: Ch. 11. Considerations for predictive modeling; title: Pt. 3. Data mart coding and content; title: Ch. 13. Accessing data; title: Ch. 14. Transposing one- and multiple-rows-per-subject data structures; title: Ch. 15. Transposing longitudinal data; title: Ch. 16. Transformations of interval-scaled variables; title: Ch. 17. Transformations of categorical variables; title: Ch. 18. Multiple interval-scaled observations per subject; title: Ch. 19. Multiple catagorical observations per subject; title: Ch. 20. Coding for predictive modeling; title: Ch. 21. Data preparation for multiple-rows-per-subject and longitudinal data marts; title: Pt. 4. Sampling, scoring, and automation; title: Ch. 22. Sampling; title: Ch. 23. Scoring and automation; title: Ch 24. Do's and don'ts when building data marts; title: Pt. 5. Case studies.