SAS DM数据准备读书笔记（目录）

计划从今天开始整理SAS DM的读书笔记，计划一天2-3篇，争取3个月左右时间，在小学期前完成。今天倒腾了一段宏变量加总的代码，发现学海无涯。还是要通过整理读书笔记把数据分析处理的能力系统巩固下。本读书笔记主要以分析代码为主。考虑读者已经具备基本的SAS BASE,MACRO,SQL的知识。

 

学习书籍：Data Preparation for DataMining Using SAS

网络上很容易下载到电子版，发现百度网盘和新浪爱问的电子资源很丰富，基本上常用的电子教材上面都可以找到。另外感谢新西兰的王同学，提供了这本教材的代码和数据。

 

教材总体评价：

自己硬盘里面有关SAS的教材估计下载了大几个G，大部分是有关于统计分析的，基本大同小异。而这本教材是我看过的最系统、最有价值的定位于分析前的数据准备的教材，这部分工作往往会受到部分不是很严谨的科研工作者的忽略，因为，他们很多研究成果都是为了发文章而炮制结果，纯属于自娱自乐。而业界应用则不一样，一旦数据分析结果产生偏差，将导致决策失误，损失的将是白花花的银子和宝贵的声誉，因此，业界非常非常注重分析前的数据准备工作。综上，无论对于科研工作人员，还是业界实务分析人员，这项工作都是非常重要的。这本书里面大量使用了BASE,MACRO和SQL，这和大部分SAS分析人员日常使用的工具是高度吻合的，读懂这些代码，并能熟练应用，应该可以说是精通SAS BASE了。对于BASE MACRO SQL还不是很熟悉的用户来说，有一定难度。可以先学习另外一门教材，SAS.Publishing.Data.Preparation.for.Analytics.Using.SAS 这本的内容相对简单一些，代码编程量和MACRO的应用会少很多。也是一本以实务应用为导向的教材。这两本书的内容都掌握的话，应该来说，贯穿统计分析、数据挖掘全过程的数据处理的基本功是具备了。

 

教材目录

 

1. Introduction

* setting the context of data mining

2. Tasks and Data Flow

* describes what data mining can do and where data preparation fits in

3. Review of Data Mining Modeling Techniques

* an overview of data mining techniques

4. SAS Macros: A Quick Start

* just in case you haven't worked with SAS macros

5. Data Acquisition and Integration

* where you get your data from and how it's pulled together

6. Integrity Checks

* how to make sure the data is correct and even what "correct" means

7. Exploratory Data Analysis

* get to know your data

8. Sampling and Partition

* dealing with large data sets as well as getting ready to validate the models you build

9. Data Transformations

* rarely is your source data in the form most effective for modeling - this chapter describes what can be done to produce the most effective models

10. Binning and Reduction of Cardinality

* make your variables less complex and often times, more presentable and understandable

11. Treatment of Missing Values

* you will have missing values in your data - here are several approaches for dealing with them

12. Predictive Power and Variable Reduction I

* introduces the concept of identifying usefulness of input variables and reducing the required number of variables

13. Analysis of Nominal and Ordinal Variables

* how to evaluate relationships with discrete variables

14. Analysis of Continuous Variables

* how to evaluate relationships with continuous variables

15. Principal Component Analysis

* how to use PCA for variable reduction during data preparation

16. Factor Analysis

* how to use Factor Analysis for variable reduction during data preparation

17. Predictive Power and Variable Reduction II

* defines methods of simplifying and reducing input variables with respect to the target variable

18. Putting It All Together

* a case study showing the application of all these techniques for data preparation in a realistic example

Appendix. Listing of SAS Macros