- We will send in 10–14 business days.
/images/books/11341694/9781599940472.jpg)
- Author: Gerhard Svolba
- Publisher: SAS Institute
- Year: 2006
- Pages: 440
- ISBN-10: 1599940477
- ISBN-13: 9781599940472
- Format: 20.8 x 27.8 x 2 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
Reviews
Description
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!
EXTRA 10 % discount with code: EXTRA
The promotion ends in 23d.14:23:28
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Gerhard Svolba
- Publisher: SAS Institute
- Year: 2006
- Pages: 440
- ISBN-10: 1599940477
- ISBN-13: 9781599940472
- Format: 20.8 x 27.8 x 2 cm, minkšti viršeliai
- Language: English English
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!
Reviews