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001450009 001__ 1450009
001450009 003__ OCoLC
001450009 005__ 20230310004503.0
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001450009 008__ 221004s2022\\\\nyua\\\\ob\\\\001\0\eng\d
001450009 019__ $$a1346534899
001450009 020__ $$a9781484286708$$qelectronic book
001450009 020__ $$a1484286707$$qelectronic book
001450009 020__ $$z9781484286692
001450009 020__ $$z1484286693
001450009 0247_ $$a10.1007/978-1-4842-8670-8$$2doi
001450009 035__ $$aSP(OCoLC)1346554142
001450009 040__ $$aORMDA$$beng$$erda$$epn$$cORMDA$$dEBLCP$$dGW5XE$$dYDX$$dGZM$$dYDX$$dOCLCF$$dOCLCQ
001450009 049__ $$aISEA
001450009 050_4 $$aHD30.28$$b.T65 2022
001450009 08204 $$a658.4/038$$223/eng/20221004
001450009 1001_ $$aTolulope, Afolabi Ibukun,$$eauthor.
001450009 24510 $$aData science and analytics for SMEs :$$bconsulting, tools, practical use cases /$$cAfolabi Ibukun Tolulope.
001450009 264_1 $$aNew York, NY :$$bApress,$$c[2022]
001450009 300__ $$a1 online resource (341 pages) :$$billustrations
001450009 336__ $$atext$$btxt$$2rdacontent
001450009 337__ $$acomputer$$bc$$2rdamedia
001450009 338__ $$aonline resource$$bcr$$2rdacarrier
001450009 504__ $$aIncludes bibliographical references and index.
001450009 5050_ $$aIntro -- 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
001450009 5058_ $$a1.6 Types of Analytics Problems in SME -- 1.7 Analytics Tools for SMES -- 1.8 Road Map to This Book -- Using RapidMiner Studio -- Using Gephi -- 1.9 Problems -- 1.10 References -- Chapter 2: Data for Analysis in Small Business -- 2.1 Source of Data -- Data Privacy -- 2.2 Data Quality and Integrity -- 2.3 Data Governance -- 2.4 Data Preparation -- Summary Statistics -- Example 2.1 -- Missing Data -- Data Cleaning - Outliers -- Normalization and Categorical Variables -- Handling Categorical Variables -- 2.5 Data Visualization -- 2.6 Problems -- 2.7 References
001450009 5058_ $$aChapter 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
001450009 5058_ $$a5.8 Treemaps -- 5.9 Heat Maps -- 5.10 Geographical Maps -- 5.11 A Practical Business Problem I (Simple Descriptive Analytics) -- 5.12 Problems -- 5.13 References -- Chapter 6: Predicting Numerical Outcomes -- 6.1 Introduction -- 6.2 Evaluating Prediction Models -- 6.3 Practical Business Problem II (Sales Prediction) -- 6.4 Multiple Linear Regression -- 6.5 Regression Trees -- 6.6 Neural Network (Prediction) -- 6.7 Conclusion on Sales Prediction -- 6.8 Problems -- 6.9 References -- Chapter 7: Classification Techniques -- 7.1 Classification Models and Evaluation
001450009 5058_ $$a7.2 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
001450009 506__ $$aAccess limited to authorized users.
001450009 520__ $$aMaster 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.
001450009 650_0 $$aBusiness requirements analysis.
001450009 650_0 $$aKnowledge management.
001450009 650_0 $$aSmall business.
001450009 655_0 $$aElectronic books.
001450009 77608 $$iPrint version: $$z1484286693$$z9781484286692$$w(OCoLC)1335113366
001450009 852__ $$bebk
001450009 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-1-4842-8670-8$$zOnline Access$$91397441.1
001450009 909CO $$ooai:library.usi.edu:1450009$$pGLOBAL_SET
001450009 980__ $$aBIB
001450009 980__ $$aEBOOK
001450009 982__ $$aEbook
001450009 983__ $$aOnline
001450009 994__ $$a92$$bISE