Big data analytics with SAS : get actionable insights from your Big Data using the power of SAS / David Pope.

Format:

Book

Author/Creator:

Pope, David, author.

Language:

English

Subjects (All):

SAS (Computer file).

Big data--Handbooks, manuals, etc.

Big data.

Physical Description:

1 online resource (1 volume) : illustrations

Edition:

1st edition

Place of Publication:

Birmingham, England ; Mumbai, [India] : Packt, 2017.

System Details:

text file

Summary:

Leverage the capabilities of SAS to process and analyze Big Data About This Book Combine SAS with platforms such as Hadoop, SAP HANA, and Cloud Foundry-based platforms for effecient Big Data analytics Learn how to use the web browser-based SAS Studio and iPython Jupyter Notebook interfaces with SAS Practical, real-world examples on predictive modeling, forecasting, optimizing and reporting your Big Data analysis with SAS Who This Book Is For SAS professionals and data analysts who wish to perform analytics on Big Data using SAS to gain actionable insights will find this book to be very useful. If you are a data science professional looking to perform large-scale analytics with SAS, this book will also help you. A basic understanding of SAS will be helpful, but is not mandatory. What You Will Learn Configure a free version of SAS in order do hands-on exercises dealing with data management, analysis, and reporting. Understand the basic concepts of the SAS language which consists of the data step (for data preparation) and procedures (or PROCs) for analysis. Make use of the web browser based SAS Studio and iPython Jupyter Notebook interfaces for coding in the SAS, DS2, and FedSQL programming languages. Understand how the DS2 programming language plays an important role in Big Data preparation and analysis using SAS Integrate and work efficiently with Big Data platforms like Hadoop, SAP HANA, and cloud foundry based systems. In Detail 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 ...

Contents:

Cover

Title Page

Copyright

Credits

Foreword

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Dedication

Table of Contents

Preface

Chapter 1: Setting Up the SAS® Software Environment

What does SAS do?

What is your perception of SAS?

Let's get started with your free version of SAS

History of SAS interfaces

SAS Studio web-based GUI

Describing the rest of SAS Studio

SAS Studio section - Server Files and Folders

SAS Studio section - Tasks and Utilities

SAS Studio section - Snippets

SAS Studio section - Libraries

SAS Studio section - File Shortcuts

SAS programming language

First SAS data step program

First use of a SAS PROC

Saving a SAS program

Creating a new SAS program

The AUTOEXEC file

Visual Programmer versus SAS Programmer

What's in the SAS® University Edition?

Different levels of the SAS analytic platform

SAS data storage

The SAS dataset

The SAS® Scalable Performance Data Engine

The Scalable Performance Data Server

SAS HDAT

SAS formats and informats

Date and time data

Summary

Chapter 2: Working with Data Using SAS® Software

Preparing data for analytics

Making data in SAS

Data step code to make data

PROC SQL to make data

Working with external data

Data step code for importing external data

PROC IMPORT

Referencing external files

Directly referencing external files

Indirectly referencing external files

Specialty PROCs for working with external data

PROC HADOOP and PROC HDMD

PROC JSON

Specialty PROCs for working with computer languages

PROC GROOVY

PROC LUA

Chapter 3: Data Preparation Using SAS Data Step and SAS Procedures

Data preparation for analytics

Creating indicators for the first and last observation in a by group

Transposing.

PROC TRANSPOSE

SAS Studio Transpose Data task

Statistical and mathematical data transformations

PROC MEANS

Imputation

Identifying missing values

Characterizing data

List Table Attributes

SAS macro facility

Macro variables

Macros

Chapter 4: Analysis with SAS® Software

Analytics

Descriptive and predictive analysis

Descriptive analysis

PROC FREQ

PROC CORR

PROC UNIVARIATE

Predictive analysis

Regression analysis

PROC REG

Forecasting analysis

PROC TIMEDATA

PROC ARIMA

Optimization analysis

SAS/IML

Interacting with the R programming language

PROC IML

Chapter 5: Reporting with SAS® Software

Reporting

SAS Studio tasks and snippets that generate reports and graphs

BASE procedures designed for reporting

TABULATE procedure examples

REPORT procedure example

The Output Delivery System

ODS Tagsets

ODS trace

ODS document and the DOCUMENT procedure

ODS Graphics

How to make a user-defined snippet

Chapter 6: Other Programming Languages in BASE SAS® Software

The DS2 programming language

When to use DS2

How is DS2 similar to the data step?

How are DS2 and DATA step different?

Programming in DS2

DS2 methods

DS2 system methods

DS2 user-defined methods

DS2 packages

DS2 predefined packages

DS2 user-defined packages

Running DS2 programs

The DS2 procedure

DS2 Hello World program - example 1

DS2 Hello World program - example 2

DS2 Hello World program - example 3

DS2 Hello World program - example 4

DS2 Hello World program - example 5

DS2 program with a method that returns a value

DS2 program with a user-defined package

The FedSQL programming language

How to run FedSQL programs

FedSQL program using the FEDSQL procedure

Using FedSQL with DS

Summary.

Chapter 7: SAS® Software Engineers the Processing Environment for You

Architecture

The SAS platform

Service-Oriented Architecture and microservices

Differences between SOA and microservices

SAS server versus a SAS grid

In-database processing

In-database procedures

Additonal in-database processing SAS offerings

SAS Scoring Accelerator

SAS Code Accelerator

In-memory processing

SAS High-Performance Analytics Server

SAS LASR Analytics Server

SAS Cloud Analytics Server

Dedicated hardware for in-memory processing

Open platform and open source

Running SAS from an iPython Jupyter Notebook

SAS running in a cloud

A public cloud

A private cloud

A hybrid cloud

Running SAS processing outside the SAS platform

The SAS Embedded Process

The SAS Event Stream Processing engine

SAS Viya the newest part of the SAS platform

SAS Viya programming

SAS Viya-based solutions

Chapter 8: Why SAS Programmers Love SAS

Why SAS programmers love SAS

Examples of why SAS programmers love SAS

Additional coding examples

The COMPARE procedure

The OPTIONS procedure

Analytics is a great career

Analytics Center of Excellence

The executive sponsor

The data scientist

The data manager

The business analyst

The ACE leader

Where should an ACE be located?

Analytics across industries

Analytics improving healthcare

Analytics improving government services

Analytics in financial services

Analytics in energy

Analytics in manufacturing

Analytics are great for society

Project Data Sphere®

SAS and Data4Good

GatherIQ™ - get involved in crowdsourcing to solve social issues

References

Index.

Notes:

Includes bibliographical references at the end of each chapters and index.

Description based on online resource; title from PDF title page (EBC, viewed December 23, 2017).

OCLC:

1017990318