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|a Data preparation for analytics using SAS /
|c Gerhard Svolba.
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|a Cary, NC :
|b SAS Institute,
|c 2006.
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| 300 |
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|a 1 online resource (xxii, 408 pages) :
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|a 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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| 590 |
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|a O'Reilly
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| 630 |
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|a SAS (Computer file)
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| 630 |
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|a Enterprise miner.
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|a Time-series analysis.
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| 650 |
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2 |
|a Data Mining
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| 650 |
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6 |
|a Gestion
|x Informatique.
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| 650 |
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6 |
|a Datamarts.
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| 650 |
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6 |
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| 650 |
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6 |
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|t Data preparation for analytics using SAS.
|d Cary, NC : SAS Institute, 2006
|z 9781599940472
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