Contents
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 Making
Is Advanced Analytics the Solution for You?
Simulation, Modeling, and Optimization
Data Warehousing and Data Mining
What Can Be Discovered Using Data Mining?
Business Intelligence, Reporting, and Business Analytics
Analytics Techniques Used in the Industry
Regression Modeling and Analysis
Principal Components and Factor Analysis
Some Practical Applications of Business Analytics
Big Data vs. Conventional Business Analytics
Introduction to Data Analysis Tools
Main Parts of SAS, SPSS, and R
The Background Required for a Successful Career in Business Analytics
Skills Required for a Business Analytics Professional
Important Menu Options and Icons
Writing and Executing a SAS Program
Debugging SAS Code Using a Log File
Example for Warnings in Log File
Tips for Writing, Reading the Log File, and Debugging
Chapter 3: Data Handling Using SAS
Descriptive Portion of SAS Data Sets
Creating the Library Using the GUI
Creating a New Library Using SAS Code
Permanent and Temporary Libraries
Two Main Types of SAS Statements
Data Set Creation Using the SAS Program
Making a Copy of a SAS Data Set
Chapter 4: Important SAS Functions and Procs
Part 2: Using SAS for Business Analytics
Chapter 5: Introduction to Statistical Analysis
Basic Statistical Concepts in Business Analytics
Variable Types in Predictive Modeling Context
Solving a Problem Using Statistical Analysis
Setting Up Business Objective and Planning
Descriptive Analysis and Visualization
An Example from the Real World: Credit Risk Life Cycle
Business Objective and Planning
Descriptive Analysis and Visualization
Chapter 6: Basic Descriptive Statistics and Reporting in SAS
Rudimentary Forms of Data Analysis
Print and Various Options of Print in SAS
Calculating Central Tendencies in SAS
Calculating Dispersion Using SAS
Calculating Quantiles Using SAS
Chapter 7: Data Exploration, Validation, and Data Sanitization
Data Exploration Steps in a Statistical Data Analysis Life Cycle
Example: Contact Center Call Volumes
Need for Data Exploration and Validation
Issues with the Real-World Data and How to Solve Them
Manual Inspection of the Dataset Is Not a Practical Solution
Removing Records Is Not Always the Right Way
Understanding and Preparing the Data
Data Exploration, Validation, and Sanitization Case Study: Credit Risk Data
Step 1: Data Exploration and Validation Using the PROC CONTENTS
Step 2: Data Exploration and Validation Using Data Snapshot
Step 3: Data Exploration and Validation Using Univariate Analysis
Step 4: Data Exploration and Validation Using Frequencies
Step 5: The Missing Value and Outlier Treatment
Chapter 8: Testing of Hypothesis
Testing: An Analogy from Everyday Life
What Is the Process of Testing a Hypothesis?
State the Null Hypothesis on the Population: Null Hypothesis (H0)
Critical Values and Critical Region
Case Study: Testing for the Mean in SAS
Two-Tailed and Single-Tailed Tests
Chapter 9: Correlation and Linear Regression
Pearson’s Correlation Coefficient (r)
Calculating Correlation Coefficient Using SAS
Correlation Limits and Strength of Association
Properties and Limitations of Correlation Coefficient (r)
Some Examples on Limitations of Correlation
Regression Line Fitting Using Least Squares
The Beta Coefficients: Example 1
When Linear Regression Can’t Be Applied
Chapter 10: Multiple Regression Analysis
Multiple Regression Line Fitting Using Least Squares
Multiple Linear Regression in SAS
Example: Smartphone Sales Estimation
Three Main Measures from Regression Output
How to Analyze the Output: Linear Regression Final Check List
Double-Check for the Assumptions of Linear Regression
Analyzing the Regression Output: Final Check List Example
Chapter 11: Logistic Regression
Predicting Ice-Cream Sales: Example
SAS Logistic Regression Output Explanation
Output Part 1: Response Variable Summary
Output Part 2: Model Fit Summary
Output Part 3: Test for Regression Coefficients
Output Part 4: The Beta Coefficients and Odds Ratio
Output Part 5: Validation Statistics
Individual Impact of Independent Variables
Goodness of Fit for Logistic Regression
Prediction Using Logistic Regression
Multicollinearity in Logistic Regression
No VIF Option in PROC LOGISTIC
Logistic Regression Final Check List
Loan Default Prediction Case Study
Background and Problem Statement
Final Model Equation and Prediction Using the Model
Chapter 12: Time-Series Analysis and Forecasting
What Is a Time-Series Process?
Main Phases of Time-Series Analysis
Understanding ARIMA Using an Eyesight Measurement Analogy
Steps in the Box–Jenkins Approach
Step 1: Testing Whether the Time Series Is Stationary
Step 3: Estimating the Parameters
Step 4: Forecasting Using the Model
Case Study: Time-Series Forecasting Using the SAS Example
Chapter 13: Introducing Big Data Analytics
Traditional Data-Handling Tools
Examples of the Growing Size of Data
The Three Main Components of Big Data
Applications of Big Data Analytics
The Solution for Big Data Problems
Hadoop Distributed File System
Other Tools in the Hadoop Ecosystem
Examining the Business Problem
Looking at the Hadoop Components
Moving Data from the Local System to Hadoop