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Data preparation for analytics using SAS / Gerhard Svolba.
- Format:
- Book
- Author/Creator:
- Svolba, Gerhard, author.
- Series:
- SAS Press series Data preparation for analytics using SAS
- Language:
- English
- Subjects (All):
- SAS (Computer file).
- Enterprise miner.
- Business--Data processing.
- Business.
- Electronic data processing.
- Commercial analysis.
- Data marts.
- Data mining.
- Time-series analysis.
- Physical Description:
- 1 online resource (xxii, 408 p. ) ill. ;
- Edition:
- First edition.
- Place of Publication:
- Cary, North Carolina : SAS Institute Inc., [2006]
- Language Note:
- English
- System Details:
- text file
- Summary:
- 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 a business point of view. The tasks addressed include viewing analytic data preparation in the context of its business environment, identifying the specifics of predictive modeling for data mart creation, understanding the concepts and considerations of data preparation for time series analysis, using various SAS procedures and SAS Enterprise Miner for scoring, creating meaningful derived variables for all data mart types, using powerful SAS macros to make changes among the various data mart structures, and more!
- 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.
- Notes:
- Bibliographic Level Mode of Issuance: Monograph
- Description based on print version record.
- ISBN:
- 9781599943367
- 1599943360
- OCLC:
- 428738427
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