It is important to understand that preparing data for analytics is different than storing data efficiently in a data warehouse, which focuses on normal reporting / business intelligent type and/or ad hoc queries. Regular database administrators (DBAs) will need to be educated and/or convinced that analytics requires the data to be in a different form in order for the algorithms to process the data. It is especially important for the data to be prepared for analytic processing in order for the actual processing to run in a timely manner. Information Technology (IT) employees play an increasingly important role in helping organizations leverage data and analytics. While it isn't necessary for someone in IT to understand statistics or analytics, it is important for them to support the data scientist in acquiring the proper hardware and software to build an analytics platform that runs efficiently so that the business can best leverage the information locked in their...
Big Data Analytics with SAS
Big Data Analytics with SAS
Overview of this book
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.
Table of Contents (17 chapters)
Title Page
Credits
Foreword
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Dedication
Preface
Free Chapter
Setting Up the SAS Software Environment
Working with Data Using SAS Software
Data Preparation Using SAS Data Step and SAS Procedures
Analysis with SAS Software
Reporting with SAS Software
Other Programming Languages in BASE SAS Software
SAS Software Engineers the Processing Environment for You
Customer Reviews