MPG.eBooks - Table of Contents: Data preparation for analytics using SAS

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Bibliographic Details
Main Author:	Svolba, Gerhard
Format:	eBook
Language:	English
Published:	Cary, NC SAS Institute 2006
Series:	SAS Press series
Subjects:	Electronic Data Processing / Fast Data Mining / Fast Commercial Analysis / Blmlsh Data Mining Business & Economics / Management Science / Bisacsh Time-series Analysis / Http://id.loc.gov/authorities/subjects/sh85135430 Business & Economics / Management / Bisacsh Sas (computer File) / Http://id.loc.gov/authorities/names/n88028236 Business / Data Processing / Http://id.loc.gov/authorities/subjects/sh85018264 Business / Data Processing / Fast Data Mining / Http://id.loc.gov/authorities/subjects/sh97002073 Sas (computer File) / Fast Datamarts Business / Data Processing / Blmlsh Time-series Analysis / Fast Data Mining / Blmlsh Gestion / Informatique Business & Economics / Organizational Behavior / Bisacsh Sas (computer File) / Blmlsh Série Chronologique Data Marts / Http://id.loc.gov/authorities/subjects/sh97004780 Enterprise Miner / Http://id.loc.gov/authorities/names/n00074578 Time-series Analysis / Blmlsh Data Marts / Blmlsh Electronic Data Processing / Blmlsh Electronic Data Processing / Http://id.loc.gov/authorities/subjects/sh85042288 Data Marts / Fast Enterprise Miner / Fast Exploration De Données (informatique) Enterprise Miner / Blmlsh Business & Economics / Industrial Management / Bisacsh
Online Access:	https://learning.oreilly.com/library/view/~/978159...
Collection:	O'Reilly - Collection details see MPG.ReNa

Table of 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