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