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Data Preparation for Analytics using SAS(英文版)
【目录】 Part 1 Data Preparation: Business Point of View Chapter 1 Analytic Business Questions Chapter 2 Characteristics of Analytic Business Questions Chapter 3 Characteristics of Data Sources Chapter 4 Different Points of View on Analytic Data Preparation Part 2 Data Structures and Data Modeling Chapter 5 The Origin of Data Chapter 6 Data Models Chapter 7 Analysis Subjects and Multiple Observations Chapter 8 The One Row-per-Subject Data Mart Chapter 9 The Multiple-Rows-per-Subject Data Mart Chapter 10 Data Structures for Longitudinal Analysis Chapter 11 Considerations for Data Marts Chapter 12 Considerations for Predictive Modeling Part 3 Data Mart Coding and Content Chapter 13 Accessing Data Chapter 14 Transposing One- and Multiple-Rows-per-Subject Data Structures Chapter 15 Transposing Longitudinal Data Chapter 16 Transformations of Interval-Scaled Variables Chapter 17 Transformations of Categorical Variables Chapter 18 Multiple Interval-Scaled Observations per Subject Chapter 19 Multiple Categorical Observations per Subject Chapter 20 Coding for Predictive Modeling Chapter 21 Data Preparation for Multiple-Rows-per-Subject and Longitudinal Data Marts Part 4 Sampling, Scoring, and Automation Chapter 22 Sampling Chapter 23 Scoring and Automation Chapter 24 Do’s and Don’ts When Building Data Marts Part 5 Case Studies Chapter 25 Case Study 1—Building a Customer Data Mart 305 Chapter 26 Case Study 2—Deriving Customer Segmentation Measures from Transactional Data Chapter 27 Case Study 3—Preparing Data for Time Series Analysis Chapter 28 Case Study 4—Data Preparation in SAS Enterprise Miner |
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