Data Science and Analytics for SMEs

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. 

What You’ll Learn

• Create and measure the success of their analytics project
• Start your business analytics consulting career
• Use solutions taught in the book in practical uses cases and problems 

Who This Book Is For

Business analytics enthusiasts who are not particularly programming inclined, small business owners and data science consultants, data science and business students, and SME (small-to-medium enterprise) analysts. 

Table of Contents

Chapter 1: Introduction

Chapter 2: Data for Analysis in Small Business

Chapter 3: Business Analytics Consulting

Chapter 4: Business Analytics Consulting Phases

Chapter 5: Descriptive Analytics Tools

Chapter 6: Predicting Numerical Outcomes

Chapter 7: Classification Techniques

Chapter 8: Advanced Descriptive Analytics

Chapter 9: Case Study Part I

Chapter 10: Case Study Part II

About the Author

Afolabi Ibukun is a Data Scientist and is currently a Senior Lecturer in the Department of Computer and Information Sciences, Covenant University. She holds a B.Sc in Engineering Physics, an M.Sc and Ph.D in Computer Science. Afolabi Ibukun has over 15 years working experience in Computer Science research, teaching and mentoring. Her specific areas of interest are Data & Text Mining, Programming and Business Analytics. She has supervised several undergraduate and postgraduate students and published several articles in international journals and conferences. Afolabi Ibukun is also a Data Science Nigeria Mentor and currently runs a Business Analytics Consulting and Training firm named I&F Networks Solutions.