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Data preparation for analytics using SAS [electronic resource] / Gerhard Svolba.

By: Svolba, Gerhard.
Contributor(s): SAS Institute.
Material type: TextTextSeries: SAS Press series: Publisher: Cary, NC : SAS Institute, 2006Description: 1 online resource (xxii, 408 p.) : ill.ISBN: 9781599943367 (electronic bk.); 1599943360 (electronic bk.).Subject(s): SAS (Computer file) | Enterprise miner | Business -- Data processing | Electronic data processing | Data marts | Data mining | Time-series analysis | BUSINESS & ECONOMICS -- Management Science | BUSINESS & ECONOMICS -- Organizational Behavior | BUSINESS & ECONOMICS -- Industrial Management | BUSINESS & ECONOMICS -- ManagementGenre/Form: Electronic books.Additional physical formats: Print version:: Data preparation for analytics using SAS.DDC classification: 658/.05 Online resources: EBSCOhost
Contents:
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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Includes index.

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.

Description based on print version record.

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