Data science and analytics for SMEs : consulting, tools, practical use cases / Afolabi Ibukun Tolulope.
Tolulope, Afolabi Ibukun, author.
2022
HD30.28 .T65 2022
Available Online

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Title Data science and analytics for SMEs : consulting, tools, practical use cases / Afolabi Ibukun Tolulope.
Author Tolulope, Afolabi Ibukun, author.
ISBN 9781484286708 electronic book
1484286707 electronic book
9781484286692
1484286693
DOI https://doi.org/10.1007/978-1-4842-8670-8
Published New York, NY : Apress, [2022]
Language English
Description 1 online resource (341 pages) : illustrations
Other Standard Identifiers 10.1007/978-1-4842-8670-8 doi
Call Number HD30.28 .T65 2022
Dewey Decimal Classification 658.4/038
Summary Master the tricks and techniques of business analytics consulting, specifically applicable to small-to-medium businesses (SMEs). Written to help you hone your business analytics skills, this book applies data science techniques to help solve problems and improve upon many aspects of a business' operations. SMEs are looking for ways to use data science and analytics, and this need is becoming increasingly pressing with the ongoing digital revolution. The topics covered in the books will help to provide the knowledge leverage needed for implementing data science in small business. The demand of small business for data analytics are in conjunction with the growing number of freelance data science consulting opportunities; hence this book will provide insight on how to navigate this new terrain. This book uses a do-it-yourself approach to analytics and introduces tools that are easily available online and are non-programming based. Data science will allow SMEs to understand their customer loyalty, market segmentation, sales and revenue increase etc. more clearly. Data Science and Analytics for SMEs is particularly focused on small businesses and explores the analytics and data that can help them succeed further in their business.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Available in Other Form Print version: 9781484286692
Linked Resources Online Access
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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

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