| |
|
|
|
|
|
|
|
|
1. |
Record Nr. |
UNINA9910616397403321 |
|
|
Autore |
Tolulope Afolabi Ibukun |
|
|
Titolo |
Data science and analytics for SMEs : consulting, tools, practical use cases / / Afolabi Ibukun Tolulope |
|
|
|
|
|
|
|
Pubbl/distr/stampa |
|
|
New York, NY : , : Apress, , [2022] |
|
©2022 |
|
|
|
|
|
|
|
|
|
ISBN |
|
|
|
|
|
|
Descrizione fisica |
|
1 online resource (341 pages) |
|
|
|
|
|
|
Disciplina |
|
|
|
|
|
|
Soggetti |
|
Business requirements analysis |
Knowledge management |
Small business |
|
|
|
|
|
|
|
|
Lingua di pubblicazione |
|
|
|
|
|
|
Formato |
Materiale a stampa |
|
|
|
|
|
Livello bibliografico |
Monografia |
|
|
|
|
|
Nota di contenuto |
|
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 -- 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. |
|
|
|
|
|
|
Sommario/riassunto |
|
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. |
|
|
|
|
|
|
|
| |