Intro
Table of Contents
About the Author
About the Technical Reviewer
Acknowledgments
Preface
Chapter 1: Introduction
1.1 Data Science
1.2 Data Science for Business
1.3 Business Analytics Journey
Events in Real Life and Description
Capturing the Data
Accessible Location and Storage
Extracting Data for Analysis
Data Analytics
Summarize and Interpret Results
Presentation
Recommendations, Strategies, and Plan
Implementation
1.4 Small and Medium Enterprises (SME)
1.5 Business Analytics in Small Business

1.6 Types of Analytics Problems in SME
1.7 Analytics Tools for SMES
1.8 Road Map to This Book
Using RapidMiner Studio
Using Gephi
1.9 Problems
1.10 References
Chapter 2: Data for Analysis in Small Business
2.1 Source of Data
Data Privacy
2.2 Data Quality and Integrity
2.3 Data Governance
2.4 Data Preparation
Summary Statistics
Example 2.1
Missing Data
Data Cleaning - Outliers
Normalization and Categorical Variables
Handling Categorical Variables
2.5 Data Visualization
2.6 Problems
2.7 References

Chapter 3: Business Analytics Consulting
3.1 Business Analytics Consulting
3.2 Managing Analytics Project
3.3 Success Metrics in Analytics Project
3.4 Billing the Analytics Project
3.5 References
Chapter 4: Business Analytics Consulting Phases
4.1 Proposal and Initial Analysis
4.2 Pre-engagement Phase
4.3 Engagement Phase
4.4 Post-Engagement Phase
4.5 Problems
4.6 References
Chapter 5: Descriptive Analytics Tools
5.1 Introduction
5.2 Bar Chart
5.3 Histogram
5.4 Line Graphs
5.5 Boxplots
5.6 Scatter Plots
5.7 Packed Bubble Charts

5.8 Treemaps
5.9 Heat Maps
5.10 Geographical Maps
5.11 A Practical Business Problem I (Simple Descriptive Analytics)
5.12 Problems
5.13 References
Chapter 6: Predicting Numerical Outcomes
6.1 Introduction
6.2 Evaluating Prediction Models
6.3 Practical Business Problem II (Sales Prediction)
6.4 Multiple Linear Regression
6.5 Regression Trees
6.6 Neural Network (Prediction)
6.7 Conclusion on Sales Prediction
6.8 Problems
6.9 References
Chapter 7: Classification Techniques
7.1 Classification Models and Evaluation

7.2 Practical Business Problem III (Customer Loyalty)
7.3 Neural Network
7.4 Classification Tree
7.5 Random Forest and Boosted Trees
7.6 K-Nearest Neighbor
7.7 Logistic Regression
7.8 Problems
7.9 References
Chapter 8: Advanced Descriptive Analytics
8.1 Clustering
8.2 K-Means
8.3 Practical Business Problem IV (Customer Segmentation)
8.4 Association Analysis
8.5 Network Analysis
8.6 Practical Business Problem V (Staff Efficiency)
8.7 Problems
8.8 References
Chapter 9: Case Study Part I
9.1 SME Ecommerce
9.2 Introduction to SME Case Study