This business analytics (BA) text discusses the models based on fact-based data to measure past business performance to guide an organization in visualizing and predicting future business performance and outcomes.It provides a comprehensive overview of analytics in general with an emphasis on predictive analytics. Given the booming interest in analytics and data science, this book is timely and informative. It brings many terms, tools, and methods of analytics together.
The first three chapters provide an introduction to BA, importance of analytics, types of BA-descriptive, predictive, and prescriptive-along with the tools and models. Business intelligence (BI) and a case on descriptive analytics are discussed. Additionally, the book discusses on the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics-machine learning, neural networks, and artificial intelligence. The concluding chapter discusses on the current state, job outlook, and certifications in analytics.
Author(s): Amar Sahay
Publisher: Business Expert Press
Year: 2019
Language: English
Pages: 404
City: New York
Cover
Business Analytics: A Data-Driven Decision-Making Approach for Business Volume II
Contents
Preface
Acknowledgments
Chapter 1: Business Analytics at a Glance
Introduction to Business Analytics—What Is It?
Analytics and Business Analytics
Business Analytics and Its Importance in Modern Business Decision
Types of Business Analytics
Tools of Business Analytics
Most Widely Used Predictive Analytics Models
Background and Prerequisites to Predictive Analytics Tools
Other Areas Associated with Predictive Analytics
Recent Applications and Tools of Predictive Modeling
Prescriptive Analytics and Tools of Prescriptive Analytics
Types of Models
Glossary of Terms Related to Analytics
Chapter 2: Business Analytics and Business Intelligence
Business Analytics and Business Intelligence—Overview
Types of Business Analytics and Their Objectives
Input to Business Analytics, Types of Business Analytics, and Their Purpose
Tools of Each Type of Analytics and Their Objectives
Business Intelligence and Business Analytics: Differences
Business Intelligence and Business Analytics: A Comparison
Summary
Chapter 3: Analytics, Business Analytics, Data Analytics, and How They Fit into the Broad Umbrella of Business Intelligence
Introduction: Analytics, Business Analytics, and Data Analytics
Business Intelligence—Defined
Origin of Business Intelligence
How Does Business Intelligence Fit into Overall Analytics?
Business Intelligence and Support Systems
Applications of Business Intelligence
Tools of Business Intelligence
Business Intelligence Functions and Applications Explained
More Applications Areas of Analytics
Purpose of Analytics
Analytics as Applied to Different Areas
Advanced Analytics
Business Intelligence Programs in Companies
Specific Areas of Business Intelligence Applications in an Enterprise
Success Factors for Business Intelligence Implementation
Comparing Business Intelligence with Business Analytics
Where Does the Business Analytics Fit in the Scope of Business Intelligence?
Difference between Business Analytics and Business Intelligence
Summary
Glossary of Terms Related to Business Intelligence
Chapter 4: Descriptive Analytics—Overview, Applications, and a Case
Overview: Descriptive Analytics
Descriptive Analytics Applications: A Business Analytics Case
Case Study: Buying Pattern of Online Customers in a Large Department Store
Summary
Chapter 5: Descriptive versus Predictive Analytics
What Is Predictive Analytics and How Is It Different from Descriptive Analytics?
Exploring the Relationships between the Variables—Qualitative Tools
An Example of Logic-Driven Model—Cause-and-Effect Diagram
Data-Driven Predictive Models and Their Applications—Quantitative Models
Prerequisites and Background for Predictive Analytics
Summary
Appendix A–D
Chapter 6: Key Predictive Analytics Models (Predicting Future Business Outcomes Using Analytic Models)
Key Predictive Analytics Models and Their Description and Applications
Summary
Chapter 7: Regression Analysis and Modeling
Introduction to Regression and Correlation
Linear Regression
The Estimated Equation of Regression Line
The Method of Least Squares
Illustration of Least Squares Regression Method
Analysis of a Simple Regression Problem
Constructing a Scatterplot of the Data
Finding the Equation of the Best Fitting Line (Estimated Line)
Interpretation of the Fitted Regression Line
Making Predictions Using the Regression Line
The Standard Error of the Estimate(s)
Assessing the Fit of the Simple Regression Model: The Coefficient of Determination (r2) and Its Meaning
The Coefficient of Correlation (r) and Its Meaning
Summary of the Main Features of the Simple Regression Model Discussed Above
Regression Analysis Using Computer
The Coefficient of Determination (r2) Using EXCEL
Multiple Regression: Computer Analysis and Results
The Least Squares Multiple Regression Model
Models with Two Quantitative Independent Variables x1 and x2
Assumptions of Multiple Regression Model
Computer Analysis of Multiple Regression
Constructing Scatter Plots and Matrix Plots
Matrix of Plots: Simple
Multiple Linear Regression Model
The Regression Equation
Interpreting the Regression Equation
Standard Error of the Estimate(s) and Its Meaning
The Coefficient of Multiple Determination (r2)
Test the Overall Significance of Regression for the Example Problem at a 5 Percent Level of Significance
Test the Hypothesis That Each of the Three Independent Variables Is Significant at a 5 Percent Level of Significance
Alternate Way of Testing the above Hypothesis
Multicollinearity and Autocorrelation in Multiple Regression
Effects of Multicollinearity
Detecting Multicollinearity
Summary of the Key Features of Multiple Regression Model
Model Building and Computer Analysis
Another Example: Quadratic (Second-Order) Model
Summary of Model Building
Models with Qualitative Independent (Dummy) Variables
One Qualitative Independent Variable at Two Levels
Model with One Qualitative Independent Variable at Three Levels
Example: Dummy Variables
Overview of Regression Models
Implementation Steps and Strategy for Regression Models
Chapter 8: Time Series Analysis and Forecasting
Introduction to Forecasting
Forecasting Methods: An Overview
Time Series Forecasting
Associative Forecasting
Some Common Patterns in Forecasting
Measuring Forecast Accuracy
Forecasting Methods
Forecasting Models Based on Averages
Simple Moving Average
Weighted Moving Averages
Simple Exponential Smoothing Method
Example of Moving Average with a Trend or Double Moving Average
Forecasting Data Using Different Methods and Comparing Forecasts to Select the Best Forecasting Method
Summary
Chapter 9: Data Mining: Tools and Applications in Predictive Analytics
Introduction to Data Mining
Summary
Chapter 10: Wrap-Up, Overview, Notes on Implementation, and Current State of Business Analytics
Overview
Business Intelligence
Statistical Analysis
Data Analytics
Types of Data Analytics Applications
Artificial Intelligence, Machine Learning, and Deep Learning
Machine Learning
Deep Learning
Background and Prerequisites to Predictive Analytics
Future of Data Analytics and Business Analytics
Certification and Online Courses in Business Analytics
Summary
Appendices: Background and Prerequisite for Predictive Analytics
Appendix A: Probability Concepts: Role of Probability in Decision Making
Appendix B: Sampling, Sampling Distribution, and Inference Procedure
Appendix C: Review of Estimation, Confidence Intervals, and Hypothesis Testing
Appendix D: Hypothesis Testing for One and Two Population Parameters
Additional Readings
About the author
Index
Ad Page
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