This is the code repository for Big Data Analytics with SAS, published by Packt. It contains all the supporting project files necessary to work through the book from start to finish.
SAS has been recognized by Money Magazine and Payscale as one of the top business skills to learn in order to advance one’s career. Through innovative data management, analytics, and business intelligence software and services, SAS helps customers solve their business problems by allowing them to make better decisions faster. This book introduces the reader to the SAS and how they can use SAS to perform efficient analysis on any size data, including Big Data.
The reader will learn how to prepare data for analysis, perform predictive, forecasting, and optimization analysis and then deploy or report on the results of these analyses. While performing the coding examples within this book the reader will learn how to use the web browser based SAS Studio and iPython Jupyter Notebook interfaces for working with SAS. Finally, the reader will learn how SAS’s architecture is engineered and designed to scale up and/or out and be combined with the open source offerings such as Hadoop, Python, and R.
By the end of this book, you will be able to clearly understand how you can efficiently analyze Big Data using SAS.
All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.
The code will look like the following:
/* This is one way to add comments to your code */
data _null_;
text="Hello World";
put text;
run;
* here is another way to add a comment or to comment out code;
The reader should be curious about how SAS can be used to analyze data of any size and have a PC or macOS that meets the requires to run the ;SAS® University Edition as a virtual application or a compatible web browser that can run the SAS® University Edition via an AWS. Chapter 1, Setting Up the SAS® Software Environment, provides more details on the specifics needed to run the SAS® University Edition.
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