Data science and analytics for SMEs : consulting, tools, practical use cases /

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'...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Tolulope, Afolabi Ibukun (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Apress, [2022]
Temas:
Business requirements analysis.
Knowledge management.
Small business.
Analyse des exigences fonctionnelles (Gestion d'entreprise)
Gestion des connaissances.
Business requirements analysis
Knowledge management
Small business
Electronic books.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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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