Data Science and Analytics for SMEs: Consulting, Tools, Practical Use Cases

Description

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 LearnCreate and measure the success of their analytics projectStart your business analytics consulting careerUse 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 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 9.3 Initial Analysis 9.4 Analytics Approach 9.5 Pre-engagement 9.6 References Chapter 10: Case Study Part II 10.1 Goal 1: Increase Website Traffic 10.2 Goal 2: Increase Website Sales Revenue 10.3 Problems 10.4 References Data Files Index