Book Image

Hands-On SAS for Data Analysis

By : Harish Gulati
Book Image

Hands-On SAS for Data Analysis

By: Harish Gulati

Overview of this book

SAS is one of the leading enterprise tools in the world today when it comes to data management and analysis. It enables the fast and easy processing of data and helps you gain valuable business insights for effective decision-making. This book will serve as a comprehensive guide that will prepare you for the SAS certification exam. After a quick overview of the SAS architecture and components, the book will take you through the different approaches to importing and reading data from different sources using SAS. You will then cover SAS Base and 4GL, understanding data management and analysis, along with exploring SAS functions for data manipulation and transformation. Next, you'll discover SQL procedures and get up to speed on creating and validating queries. In the concluding chapters, you'll learn all about data visualization, right from creating bar charts and sample geographic maps through to assigning patterns and formats. In addition to this, the book will focus on macro programming and its advanced aspects. By the end of this book, you will be well versed in SAS programming and have the skills you need to easily handle and manage your data-related problems in SAS.
Table of Contents (17 chapters)
Free Chapter
1
Section 1: SAS Basics
4
Section 2: Merging, Optimizing, and Descriptive Statistics
7
Section 3: Advanced Programming
10
Section 4: SQL in SAS
13
Section 5: Data Visualization and Reporting

Summary

In this chapter, we learned about connecting data steps using Proc SQL instead of using data steps. We explored the various types of join that help connect datasets using Proc SQL. Having reviewed the pros and cons of connecting datasets in Proc SQL and data steps, we found that sorting is essential in the latter method of connecting datasets. This may mean that data step merging could be a good alternative for smaller datasets but it may lead to processing delays on a large dataset due to the sorting requirement.

We also reviewed how we can create data subsets and summarize data. We used an example where the WHERE, GROUP BY and HAVING clauses were used together to highlight the role of each of these clauses. In previous chapters, we touched upon the concept of Dictionary tables and Columns. In this chapter, we looked at an exhaustive list of options available to leverage...