book

Practical Business Analytics Using SAS: A Hands-on Guide

Name: Practical Business Analytics Using SAS: A Hands-on Guide
ISBN: 9781484200438

by Venkat Reddy Konasani, Shailendra Kadre

February 2015

Beginner to intermediate

580 pages

12h 56m

English

Apress

Read now

Unlock full access

Cover
Title
Copyright
Dedication
Contents at a Glance
Contents
About the Authors
Acknowledgments
Preface
Part 1: Basics of SAS Programming for Analytics

Chapter 1: Introduction to Business Analytics and Data Analysis Tools
Business Analytics, the Science of Data-Driven Decision MakingBusiness Analytics DefinedIs Advanced Analytics the Solution for You?Simulation, Modeling, and OptimizationData Warehousing and Data MiningWhat Can Be Discovered Using Data Mining?Business Intelligence, Reporting, and Business AnalyticsAnalytics Techniques Used in the IndustryRegression Modeling and AnalysisTime Series ForecastingConjoint AnalysisCluster AnalysisSegmentationPrincipal Components and Factor AnalysisCorrespondence AnalysisSurvival AnalyticsSome Practical Applications of Business AnalyticsCustomer AnalyticsOperational AnalyticsSocial Media AnalyticsData Used in AnalyticsBig Data vs. Conventional Business AnalyticsIntroduction to Big DataIntroduction to Data Analysis ToolsMain Parts of SAS, SPSS, and RSelection of Analytics ToolsThe Background Required for a Successful Career in Business AnalyticsSkills Required for a Business Analytics ProfessionalConclusion
Chapter 2: SAS Introduction
Starting SAS in WindowsThe SAS Opening ScreenThe Five Main WindowsEditor WindowLog WindowOutput WindowExplorer WindowResults WindowImportant Menu Options and IconsView OptionsRun MenuSolutions MenuShortcut IconsWriting and Executing a SAS ProgramComments in the CodeYour First SAS ProgramDebugging SAS Code Using a Log FileExample for Warnings in Log FileTips for Writing, Reading the Log File, and DebuggingSaving SAS FilesExerciseConclusion
Chapter 3: Data Handling Using SAS
SAS Data SetsDescriptive Portion of SAS Data SetsData Portion of Data SetSAS LibrariesCreating the Library Using the GUIRules of Assigning a LibraryCreating a New Library Using SAS CodePermanent and Temporary LibrariesTwo Main Types of SAS StatementsImporting Data into SASData Set Creation Using the SAS ProgramUsing the Import WizardImport Using the CodeData ManipulationsMaking a Copy of a SAS Data SetCreating New VariablesUpdating the Same Data SetDrop and Keep VariablesSubsetting the DataConclusion
Chapter 4: Important SAS Functions and Procs
SAS FunctionsNumeric FunctionsCharacter FunctionsDate FunctionsImportant SAS PROCsThe Proc StepPROC CONTENTSPROC SORTGraphs Using SASPROC gplot and GchartPROC SQLData MergingAppending the DataFrom SET to MERGEBlending with ConditionMatched MergingConclusion
Part 2: Using SAS for Business Analytics
Chapter 5: Introduction to Statistical Analysis
What Is Statistics?Basic Statistical Concepts in Business AnalyticsPopulationSampleVariableVariable Types in Predictive Modeling ContextParameterStatisticExample ExerciseStatistical Analysis MethodsDescriptive StatisticsInferential StatisticsPredictive StatisticsSolving a Problem Using Statistical AnalysisSetting Up Business Objective and PlanningThe Data PreparationDescriptive Analysis and VisualizationPredictive ModelingModel ValidationModel ImplementationAn Example from the Real World: Credit Risk Life CycleBusiness Objective and PlanningData PreparationDescriptive Analysis and VisualizationPredictive ModelingModel ValidationModel ImplementationConclusion
Chapter 6: Basic Descriptive Statistics and Reporting in SAS
Rudimentary Forms of Data AnalysisSimply Print the DataPrint and Various Options of Print in SASSummary StatisticsCentral TendenciesCalculating Central Tendencies in SASWhat Is Dispersion?Calculating Dispersion Using SASQuantilesCalculating Quantiles Using SASBox PlotsCreating Boxplots Using SASBivariate AnalysisConclusion
Chapter 7: Data Exploration, Validation, and Data Sanitization
Data Exploration Steps in a Statistical Data Analysis Life CycleExample: Contact Center Call VolumesNeed for Data Exploration and ValidationIssues with the Real-World Data and How to Solve ThemMissing ValuesThe OutliersManual Inspection of the Dataset Is Not a Practical SolutionRemoving Records Is Not Always the Right WayUnderstanding and Preparing the DataData ExplorationData ValidationData CleaningData Exploration, Validation, and Sanitization Case Study: Credit Risk DataImporting the DataStep 1: Data Exploration and Validation Using the PROC CONTENTSStep 2: Data Exploration and Validation Using Data SnapshotStep 3: Data Exploration and Validation Using Univariate AnalysisStep 4: Data Exploration and Validation Using FrequenciesStep 5: The Missing Value and Outlier TreatmentConclusion
Chapter 8: Testing of Hypothesis
Testing: An Analogy from Everyday LifeWhat Is the Process of Testing a Hypothesis?State the Null Hypothesis on the Population: Null Hypothesis (H0)Alternate Hypothesis (H1)Sampling DistributionCentral Limit TheoremTest StatisticInferenceCritical Values and Critical RegionConfidence IntervalTestsT-test for MeanCase Study: Testing for the Mean in SASOther Test ExamplesTwo-Tailed and Single-Tailed TestsConclusion
Chapter 9: Correlation and Linear Regression
What Is Correlation?Pearson’s Correlation Coefficient (r)Variance and CovarianceCorrelation MatrixCalculating Correlation Coefficient Using SASCorrelation Limits and Strength of AssociationProperties and Limitations of Correlation Coefficient (r)Some Examples on Limitations of CorrelationCorrelation vs. CausationCorrelation ExampleCorrelation SummaryLinear RegressionCorrelation to RegressionEstimation ExampleSimple Linear RegressionRegression Line Fitting Using Least SquaresThe Beta Coefficients: Example 1How Good Is My Model?Regression AssumptionsWhen Linear Regression Can’t Be AppliedSimple Regression: ExampleConclusion
Chapter 10: Multiple Regression Analysis
Multiple Linear RegressionMultiple Regression LineMultiple Regression Line Fitting Using Least SquaresMultiple Linear Regression in SASExample: Smartphone Sales EstimationGoodness of FitThree Main Measures from Regression OutputMulticollinearity DefinedHow to Analyze the Output: Linear Regression Final Check ListDouble-Check for the Assumptions of Linear RegressionF-testR-squaredAdjusted R-SquaredVIFT-test for Each VariableAnalyzing the Regression Output: Final Check List ExampleConclusion
Chapter 11: Logistic Regression
Predicting Ice-Cream Sales: ExampleNonlinear RegressionLogistic RegressionLogistic Regression Using SASSAS Logistic Regression Output ExplanationOutput Part 1: Response Variable SummaryOutput Part 2: Model Fit SummaryOutput Part 3: Test for Regression CoefficientsOutput Part 4: The Beta Coefficients and Odds RatioOutput Part 5: Validation StatisticsIndividual Impact of Independent VariablesGoodness of Fit for Logistic RegressionChi-square TestConcordancePrediction Using Logistic RegressionMulticollinearity in Logistic RegressionNo VIF Option in PROC LOGISTICLogistic Regression Final Check ListLoan Default Prediction Case StudyBackground and Problem StatementObjectiveData SetModel BuildingFinal Model Equation and Prediction Using the ModelConclusion
Chapter 12: Time-Series Analysis and Forecasting
What Is a Time-Series Process?Main Phases of Time-Series AnalysisModeling MethodologiesBox–Jenkins ApproachWhat Is ARIMA?The AR ProcessThe MA ProcessARMA ProcessUnderstanding ARIMA Using an Eyesight Measurement AnalogySteps in the Box–Jenkins ApproachStep 1: Testing Whether the Time Series Is StationaryStep 2: Identifying the ModelStep 3: Estimating the ParametersStep 4: Forecasting Using the ModelCase Study: Time-Series Forecasting Using the SAS ExampleChecking the Model AccuracyConclusion
Chapter 13: Introducing Big Data Analytics
Traditional Data-Handling ToolsWalmart Customer DataFacebook DataExamples of the Growing Size of DataWhat Is Big Data?The Three Main Components of Big DataApplications of Big Data AnalyticsThe Solution for Big Data ProblemsDistributed ComputingWhat Is MapReduce?Map FunctionReduce FunctionWhat Is Apache Hadoop?Hadoop Distributed File SystemMapReduceApache HiveApache PigOther Tools in the Hadoop EcosystemCompaniesThat Use HadoopBig Data Analytics ExampleExamining the Business ProblemGetting the Data SetStarting HadoopLooking at the Hadoop ComponentsMoving Data from the Local System to HadoopViewing the Data on HDFSStarting HiveCreating a Table Using HiveExecuting a Program Using HiveViewing the MapReduce StatusThe Final ResultConclusion
Index

Overview

Practical Business Analytics Using SAS: A Hands-on Guide shows SAS users and businesspeople how to analyze data effectively in real-life business scenarios.

The book begins with an introduction to analytics, analytical tools, and SAS programming. The authors—both SAS, statistics, analytics, and big data experts—first show how SAS is used in business, and then how to get started programming in SAS by importing data and learning how to manipulate it. Besides illustrating SAS basic functions, you will see how each function can be used to get the information you need to improve business performance. Each chapter offers hands-on exercises drawn from real business situations.

The book then provides an overview of statistics, as well as instruction on exploring data, preparing it for analysis, and testing hypotheses. You will learn how to use SAS to perform analytics and model using both basic and advanced techniques like multiple regression, logistic regression, and time series analysis, among other topics. The book concludes with a chapter on analyzing big data. Illustrations from banking and other industries make the principles and methods come to life.

Readers will find just enough theory to understand the practical examples and case studies, which cover all industries. Written for a corporate IT and programming audience that wants to upgrade skills or enter the analytics field, this book includes:

More than 200 examples and exercises, including code and datasets for practice.

Relevant examples for all industries.

Case studies that show how to use SAS analytics to identify opportunities, solve complicated problems, and chart a course.

Practical Business Analytics Using SAS: A Hands-on Guide gives you the tools you need to gain insight into the data at your fingertips, predict business conditions for better planning, and make excellent decisions. Whether you are in retail, finance, healthcare, manufacturing, government, or any other industry, this book will help your organization increase revenue, drive down costs, improve marketing, and satisfy customers better than ever before.

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Data Preparation for Analytics Using SAS

Publisher Resources

ISBN: 9781484200438Purchase book

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills