Data preparation for analytics using SAS / Gerhard Svolba.
Material type: TextSeries: SAS Press seriesPublication details: Cary, NC : SAS Institute, 2006Description: xxii, 408 p. : ill. ; 28 cmISBN: 9781599940472 (pbk.); 1599940477 (pbk.)Subject(s): SAS (Computer file) | Enterprise miner | Business -- Data processing | Electronic data processing | Commercial analysis | Data marts | Data mining | Time-series analysisLOC classification: HF5548.2 | .S87 2006Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
---|---|---|---|---|---|---|---|
General Circulation | APU Library Open Shelf | Book | HF5548.2 .S87 2006 c.1 (Browse shelf (Opens below)) | 1 | Lost and Paid for Withdrawn | 00012522 |
Browsing APU Library shelves, Shelving location: Open Shelf, Collection: Book Close shelf browser (Hides shelf browser)
HF5548.2 .S73 2001 c.4 Business data communications / | HF5548.2 .S73 2001 c.5 Business data communications / | HF5548.2 .S74 1993 c.1 Computing in the information age / | HF5548.2 .S87 2006 c.1 Data preparation for analytics using SAS / | HF5548.2 .V36 2020 c.1 The organisation of tomorrow : how AI, blockchain, and analytics turn your business into a data organisation / | HF5548.2 .V36 2020 c.2 The organisation of tomorrow : how AI, blockchain, and analytics turn your business into a data organisation / | HF5548.2 .W35 1990 c.1 Network system architecture / |
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.
There are no comments on this title.