Cargando…

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'...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Tolulope, Afolabi Ibukun (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Apress, [2022]
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

LEADER 00000cam a22000007i 4500
001 OR_on1346554142
003 OCoLC
005 20231017213018.0
006 m o d
007 cr cnu---unuuu
008 221004s2022 nyua ob 001 0 eng d
040 |a ORMDA  |b eng  |e rda  |e pn  |c ORMDA  |d EBLCP  |d GW5XE  |d YDX  |d GZM  |d YDX  |d OCLCF  |d OCLCQ  |d OCLCO 
019 |a 1346534899 
020 |a 9781484286708  |q electronic book 
020 |a 1484286707  |q electronic book 
020 |z 9781484286692 
020 |z 1484286693 
024 7 |a 10.1007/978-1-4842-8670-8  |2 doi 
029 1 |a AU@  |b 000072801423 
029 1 |a AU@  |b 000072914703 
035 |a (OCoLC)1346554142  |z (OCoLC)1346534899 
037 |a 9781484286708  |b O'Reilly Media 
050 4 |a HD30.28  |b .T65 2022 
072 7 |a UN  |2 bicssc 
072 7 |a COM021000  |2 bisacsh 
072 7 |a UN  |2 thema 
082 0 4 |a 658.4/038  |2 23/eng/20221004 
049 |a UAMI 
100 1 |a Tolulope, Afolabi Ibukun,  |e author. 
245 1 0 |a Data science and analytics for SMEs :  |b consulting, tools, practical use cases /  |c Afolabi Ibukun Tolulope. 
264 1 |a New York, NY :  |b Apress,  |c [2022] 
300 |a 1 online resource (341 pages) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
504 |a Includes bibliographical references and index. 
520 |a 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. 
505 0 |a 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 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
650 0 |a Business requirements analysis. 
650 0 |a Knowledge management. 
650 0 |a Small business. 
650 6 |a Analyse des exigences fonctionnelles (Gestion d'entreprise) 
650 6 |a Gestion des connaissances. 
650 7 |a Business requirements analysis  |2 fast 
650 7 |a Knowledge management  |2 fast 
650 7 |a Small business  |2 fast 
655 0 |a Electronic books. 
776 0 8 |c Original  |z 1484286693  |z 9781484286692  |w (OCoLC)1335113366 
856 4 0 |u https://learning.oreilly.com/library/view/~/9781484286708/?ar  |z Texto completo (Requiere registro previo con correo institucional) 
938 |a ProQuest Ebook Central  |b EBLB  |n EBL7101965 
938 |a YBP Library Services  |b YANK  |n 303161151 
994 |a 92  |b IZTAP