Afolabi Ibukun Tolulope - Data Science and Analytics for SMEs: Consulting, Tools, Practical Use Cases

Data Science andAnalytics for SMEsConsulting, Tools,Practical Use CasesAfolabi Ibukun TolulopeData Science and Analytics for SMEs: Consulting, Tools, PracticalUse Cases Afolabi Ibukun Tolulope London, United Kingdom ISBN-13 (pbk): 978-1-4842-8669-2 ISBN-13 (electronic): 978-1-4842-8670-8 https://doi.org/10.1007/978-1-4842-8670-8 Copyright 2022 by Afolabi Ibukun Tolulope This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Trademarked names, logos, and images may appear in this book. Rather than use a trademark symbol with every occurrence of a trademarked name, logo, or image we use the names, logos, and images only in an editorial fashion and to the benefit of the trademark owner, with no intention of infringement of the trademark. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made.

The publisher makes no warranty, express or implied, with respect to the material contained herein. Managing Director, Apress Media LLC: Welmoed Spahr Acquisitions Editor: Shiva Ramachandran Development Editor: James Markham Coordinating Editor: Jessica Vakili Distributed to the book trade worldwide by Springer Science+Business Media New York, 1 New York Plaza, New York, NY 10004. Phone 1-800-SPRINGER, fax (201) 348-4505, e-mail orders-ny@springer-sbm.com, or visit www.springeronline.com. Apress Media, LLC is a California LLC and the sole member (owner) is Springer Science + Business Media Finance Inc (SSBM Finance Inc). SSBM Finance Inc is a Delaware corporation. For information on translations, please e-mail booktranslations@springernature.com; for reprint, paperback, or audio rights, please e-mail bookpermissions@springernature.com.

Apress titles may be purchased in bulk for academic, corporate, or promotional use. eBook versions and licenses are also available for most titles. For more information, reference our Print and eBook Bulk Sales web page at http://www.apress.com/bulk-sales. Printed on acid-free paper Table of Contents About the Author ix About the Technical Reviewer xi Acknowledgments xiii Preface xv Chapter 1: Introduction1 1.1 Data Science ...................................................................................................1 1.2 Data Science for Business ..............................................................................2 1.3 Business Analytics Journey ............................................................................4 Events in Real Life and Description .................................................................5 Capturing the Data...........................................................................................9 Accessible Location and Storage ..................................................................10 Extracting Data for Analysis ..........................................................................10 Data Analytics ................................................................................................12 Summarize and Interpret Results ..................................................................14 Presentation ..................................................................................................15 Recommendations, Strategies, and Plan .......................................................15 Implementation .............................................................................................16 1.4 Small and Medium Enterprises (SME) ...........................................................16 1.5 Business Analytics in Small Business ...........................................................17 1.6 Types of Analytics Problems in SME .............................................................19 iii Table of ConTenTs 1.7 Analytics Tools for SMES ...............................................................................21 1.8 Road Map to This Book .................................................................................21 Using RapidMiner Studio ...............................................................................23 Using Gephi ...................................................................................................24 1.9 Problems .......................................................................................................25 1.10 References ..................................................................................................26 Chapter 2: Data for Analysis in Small Business 29 2.1 Source of Data ..............................................................................................29 Data Privacy ...................................................................................................33 2.2 Data Quality and Integrity .............................................................................34 2.3 Data Governance ...........................................................................................36 2.4 Data Preparation ...........................................................................................37 Summary Statistics .......................................................................................38 Missing Data ..................................................................................................43 Data Cleaning Outliers ................................................................................47 Normalization and Categorical Variables .......................................................51 Handling Categorical Variables ......................................................................51 2.5 Data Visualization ..........................................................................................53 2.6 Problems .......................................................................................................55 2.7 References ....................................................................................................55 Chapter 3: Business Analytics Consulting 59 3.1 Business Analytics Consulting ......................................................................59 3.2 Managing Analytics Project ...........................................................................62 3.3 Success Metrics in Analytics Project ............................................................65 3.4 Billing the Analytics Project ..........................................................................66 3.5 References ....................................................................................................69 iv Table of ConTenTs Chapter 4: Business Analytics Consulting Phases 71 4.1 Proposal and Initial Analysis .........................................................................71 4.2 Pre-engagement Phase ................................................................................75 4.3 Engagement Phase .......................................................................................78 4.4 Post-Engagement Phase ...............................................................................80 4.5 Problems .......................................................................................................81 4.6 References ....................................................................................................82 Chapter 5: Descriptive Analytics Tools 83 5.1 Introduction ...................................................................................................83 5.2 Bar Chart .......................................................................................................84 5.3 Histogram......................................................................................................87 5.4 Line Graphs ...................................................................................................90 5.5 Boxplots ........................................................................................................91 5.6 Scatter Plots ..................................................................................................93 5.7 Packed Bubble Charts ...................................................................................96 5.8 Treemaps.......................................................................................................97 5.9 Heat Maps .....................................................................................................98 5.10 Geographical Maps ...................................................................................100 5.11 A Practical Business Problem I (Simple Descriptive Analytics) .................101 5.12 Problems ...................................................................................................109 5.13 References ................................................................................................111 Chapter 6: Predicting Numerical Outcomes 113 6.1 Introduction .................................................................................................113 6.2 Evaluating Prediction Models ......................................................................115 6.3 Practical Business Problem II (Sales Prediction) ........................................117 6.4 Multiple Linear Regression .........................................................................126 v Table of ConTenTs 6.5 Regression Trees .........................................................................................135 6.6 Neural Network (Prediction) ........................................................................143 6.7 Conclusion on Sales Prediction ...................................................................151 6.8 Problems .....................................................................................................152 6.9 References ..................................................................................................153 Chapter 7: Classification Techniques 155 7.1 Classification Models and Evaluation..........................................................155 7.2 Practical Business Problem III (Customer Loyalty) ......................................159 7.3 Neural Network ...........................................................................................164 7.4 Classification Tree .......................................................................................169 7.5 Random Forest and Boosted Trees .............................................................174 7.6 K-Nearest Neighbor ....................................................................................179 7.7 Logistic Regression .....................................................................................187 7.8 Problems .....................................................................................................195 7.9 References ..................................................................................................196 Chapter 8: Advanced Descriptive Analytics 199 8.1 Clustering ....................................................................................................199 8.2 K-Means ......................................................................................................203 8.3 Practical Business Problem IV (Customer Segmentation) ...........................207 8.4 Association Analysis ....................................................................................222 8.5 Network Analysis ........................................................................................231 8.6 Practical Business Problem V (Staff Efficiency) ..........................................253 8.7 Problems .....................................................................................................261 8.8 References ..................................................................................................261 vi Table of ConTenTs Chapter 9: Case Study Part I 265 9.1 SME Ecommerce .........................................................................................265 9.2 Introduction to SME Case Study ..................................................................268 9.3 Initial Analysis .............................................................................................272 9.4 Analytics Approach ......................................................................................274 9.5 Pre-engagement .........................................................................................276 9.6 References ..................................................................................................281 Chapter 10: Case Study Part II 283 10.1 Goal 1: Increase Website Traffic ................................................................283 10.2 Goal 2: Increase Website Sales Revenue ..................................................288 10.3 Problems ...................................................................................................320 10.4 References ................................................................................................321 Data Files 323 Index 327 vii