Fluent Essays

Need Writing of 1 paragraph for (1-4) Questions with citation & 1 page for 5th question in APA Format. Please refer to the chapter 3 in the attached textbook.1. How do you describe the importance of data in analytics? Can we think of analytics without data? Explain. 2. Considering the new and broad definition of business analytics, what are the main inputs and outputs to the analytics continuum? 3. Where do the data for business analytics come from? What are the sources and the nature of those incoming data? 4. What are the most common metrics that make for analytics-ready data? 5. Go to data.gov—a U.S. government–sponsored data portal that has a very large number of data sets on a wide variety of topics ranging from healthcare to education, climate to public safety. Pick a topic that you are most passionate about. Go through the topic- specific information and explanation provided on the site. Explore the possibilities of downloading the data, and use your favorite data visualization tool to create your own meaningful information and visualizations. analytics__data_science__and_artificial_intelligence__11th_edition_compressed.pdf Unformatted Attachment Preview ELEVENTH EDITION ANALYTICS, DATA SCIENCE, & ARTIFICIAL INTELLIGENCE SYSTEMS FOR DECISION SUPPORT Ramesh Sharda Oklahoma State University Dursun Delen Oklahoma State University Efraim Turban University of Hawaii Vice President of Courseware Portfolio Management: Andrew Gilfillan Executive Portfolio Manager: Samantha Lewis Team Lead, Content Production: Laura Burgess Content Producer: Faraz Sharique Ali Portfolio Management Assistant: Bridget Daly Director of Product Marketing: Brad Parkins Director of Field Marketing: Jonathan Cottrell Product Marketing Manager: Heather Taylor Field Marketing Manager: Bob Nisbet Product Marketing Assistant: Liz Bennett Field Marketing Assistant: Derrica Moser Senior Operations Specialist: Diane Peirano Senior Art Director: Mary Seiner Interior and Cover Design: Pearson CSC Cover Photo: Phonlamai Photo/Shutterstock Senior Product Model Manager: Eric Hakanson Manager, Digital Studio: Heather Darby Course Producer, MyLab MIS: Jaimie Noy Digital Studio Producer: Tanika Henderson Full-Service Project Manager: Gowthaman Sadhanandham Full Service Vendor: Integra Software Service Pvt. 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Library of Congress Cataloging-in-Publication Data Library of Congress Cataloging in Publication Control Number: 2018051774 ISBN 10: 0-13-519201-3 ISBN 13: 978-0-13-519201-6 BRIEF CONTENTS Preface xxv About the Authors PART I Introduction to Analytics and AI Chapter 1 Chapter 2 Chapter 3 PART II Chapter 6 Chapter 7 Chapter 9 Chapter 12 Chapter 13 193 Data Mining Process, Methods, and Algorithms Machine-Learning Techniques for Predictive Analytics 251 Deep Learning and Cognitive Computing 315 Text Mining, Sentiment Analysis, and Social Analytics 388 194 459 Prescriptive Analytics: Optimization and Simulation 460 Big Data, Cloud Computing, and Location Analytics: Concepts and Tools 509 Robotics, Social Networks, AI and IoT Chapter 10 Chapter 11 PART V Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence: Systems for Decision Support 2 Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications 73 Nature of Data, Statistical Modeling, and Visualization 117 Prescriptive Analytics and Big Data Chapter 8 PART IV 1 Predictive Analytics/Machine Learning Chapter 4 Chapter 5 PART III xxxiv 579 Robotics: Industrial and Consumer Applications 580 Group Decision Making, Collaborative Systems, and AI Support 610 Knowledge Systems: Expert Systems, Recommenders, Chatbots, Virtual Personal Assistants, and Robo Advisors 648 The Internet of Things as a Platform for Intelligent Applications 687 Caveats of Analytics and AI 725 Chapter 14 Implementation Issues: From Ethics and Privacy to Organizational and Societal Impacts 726 Glossary 770 Index 785 iii CONTENTS Preface xxv About the Authors PART I xxxiv Introduction to Analytics and AI 1 Chapter 1 Overview of Business Intelligence, Analytics, Data Science, and Artificial Intelligence: Systems for Decision Support 2 1.1 1.2 1.3 Opening Vignette: How Intelligent Systems Work for KONE Elevators and Escalators Company 3 Changing Business Environments and Evolving Needs for Decision Support and Analytics 5 Decision-Making Process 6 The Influence of the External and Internal Environments on the Process 6 Data and Its Analysis in Decision Making 7 Technologies for Data Analysis and Decision Support 7 Decision-Making Processes and Computerized Decision Support Framework 9 Simon’s Process: Intelligence, Design, and Choice 9 The Intelligence Phase: Problem (or Opportunity) Identification 10 0 APPLICATION CASE 1.1 Making Elevators Go Faster! 11 The Design Phase 12 The Choice Phase 13 The Implementation Phase 13 The Classical Decision Support System Framework 14 A DSS Application 16 Components of a Decision Support System 18 The Data Management Subsystem 18 The Model Management Subsystem 19 0 APPLICATION CASE 1.2 SNAP DSS Helps OneNet Make Telecommunications Rate Decisions 20 1.4 iv The User Interface Subsystem 20 The Knowledge-Based Management Subsystem 21 Evolution of Computerized Decision Support to Business Intelligence/Analytics/Data Science 22 A Framework for Business Intelligence 25 The Architecture of BI 25 The Origins and Drivers of BI 26 Data Warehouse as a Foundation for Business Intelligence 27 Transaction Processing versus Analytic Processing 27 A Multimedia Exercise in Business Intelligence 28 Contents 1.5 Analytics Overview 30 Descriptive Analytics 32 0 APPLICATION CASE 1.3 Silvaris Increases Business with Visual Analysis and Real-Time Reporting Capabilities 32 0 APPLICATION CASE 1.4 Siemens Reduces Cost with the Use of Data Visualization 33 Predictive Analytics 33 0 APPLICATION CASE 1.5 Analyzing Athletic Injuries 34 Prescriptive Analytics 34 0 APPLICATION CASE 1.6 A Specialty Steel Bar Company Uses Analytics to Determine Available-to-Promise Dates 35 1.6 Analytics Examples in Selected Domains 38 Sports Analytics—An Exciting Frontier for Learning and Understanding Applications of Analytics 38 Analytics Applications in Healthcare—Humana Examples 43 0 APPLICATION CASE 1.7 Image Analysis Helps Estimate Plant Cover 50 1.7 Artificial Intelligence Overview What Is Artificial Intelligence? 52 The Major Benefits of AI 52 The Landscape of AI 52 52 0 APPLICATION CASE 1.8 AI Increases Passengers’ Comfort and Security in Airports and Borders 54 The Three Flavors of AI Decisions 55 Autonomous AI 55 Societal Impacts 56 0 APPLICATION CASE 1.9 Robots Took the Job of Camel-Racing Jockeys for Societal Benefits 58 1.8 Convergence of Analytics and AI 59 Major Differences between Analytics and AI 59 Why Combine Intelligent Systems? 60 How Convergence Can Help? 60 Big Data Is Empowering AI Technologies 60 The Convergence of AI and the IoT 61 The Convergence with Blockchain and Other Technologies 62 0 APPLICATION CASE 1.10 Amazon Go Is Open for Business 62 IBM and Microsoft Support for Intelligent Systems Convergence 63 1.9 Overview of the Analytics Ecosystem 63 1.10 Plan of the Book 65 1.11 Resources, Links, and the Teradata University Network Connection 66 Resources and Links 66 Vendors, Products, and Demos 66 Periodicals 67 The Teradata University Network Connection 67 v vi Contents The Book’s Web Site 67 Chapter Highlights 67 Questions for Discussion References • 68 Key Terms 68 • Exercises 69 70 Chapter 2 Artificial Intelligence: Concepts, Drivers, Major Technologies, and Business Applications 2.1 2.2 2.3 73 Opening Vignette: INRIX Solves Transportation Problems 74 Introduction to Artificial Intelligence 76 Definitions 76 Major Characteristics of AI Machines 77 Major Elements of AI 77 AI Applications 78 Major Goals of AI 78 Drivers of AI 79 Benefits of AI 79 Some Limitations of AI Machines 81 Three Flavors of AI Decisions 81 Artificial Brain 82 Human and Computer Intelligence 83 What Is Intelligence? 83 How Intelligent Is AI? 84 Measuring AI 85 0 APPLICATION CASE 2.1 How Smart Can a Vacuum Cleaner Be? 2.4 Major AI Technologies and Some Derivatives Intelligent Agents 87 Machine Learning 88 86 87 0 APPLICATION CASE 2.2 How Machine Learning Is Improving Work in Business 89 2.5 Machine and Computer Vision 90 Robotic Systems 91 Natural Language Processing 92 Knowledge and Expert Systems and Recommenders 93 Chatbots 94 Emerging AI Technologies 94 AI Support for Decision Making 95 Some Issues and Factors in Using AI in Decision Making 96 AI Support of the Decision-Making Process 96 Automated Decision Making 97 0 APPLICATION CASE 2.3 How Companies Solve Real-World Problems Using Google’s Machine-Learning Tools 97 Conclusion 98 Contents 2.6 AI Applications in Accounting 99 AI in Accounting: An Overview 99 AI in Big Accounting Companies 100 Accounting Applications in Small Firms 100 0 APPLICATION CASE 2.4 How EY, Deloitte, and PwC Are Using AI 2.7 Job of Accountants 101 AI Applications in Financial Services AI Activities in Financial Services 101 AI in Banking: An Overview 101 Illustrative AI Applications in Banking 102 Insurance Services 103 100 101 0 APPLICATION CASE 2.5 US Bank Customer Recognition and Services 104 2.8 AI in Human Resource Management (HRM) AI in HRM: An Overview 105 AI in Onboarding 105 105 0 APPLICATION CASE 2.6 How Alexander Mann Solutions (AMS) Is Using AI to Support the Recruiting Process 106 2.9 Introducing AI to HRM Operations 106 AI in Marketing, Advertising, and CRM Overview of Major Applications 107 AI Marketing Assistants in Action 108 Customer Experiences and CRM 108 107 0 APPLICATION CASE 2.7 Kraft Foods Uses AI for Marketing and CRM 109 Other Uses of AI in Marketing 110 2.10 AI Applications in Production-Operation Management (POM) 110 AI in Manufacturing 110 Implementation Model 111 Intelligent Factories 111 Logistics and Transportation 112 Chapter Highlights 112 Questions for Discussion References • Key Terms 113 113 • Exercises 114 114 Chapter 3 Nature of Data, Statistical Modeling, and Visualization 117 3.1 Opening Vignette: SiriusXM Attracts and Engages a New Generation of Radio Consumers with Data-Driven Marketing 118 3.2 Nature of Data 121 3.3 Simple Taxonomy of Data 125 0 APPLICATION CASE 3.1 Verizon Answers the Call for Innovation: The Nation’s Largest Network Provider uses Advanced Analytics to Bring the Future to its Customers 127 vii viii Contents 3.4 Art and Science of Data Preprocessing 129 0 APPLICATION CASE 3.2 Improving Student Retention with Data-Driven Analytics 133 3.5 Statistical Modeling for Business Analytics Descriptive Statistics for Descriptive Analytics 140 139 Measures of Centrality Tendency (Also Called Measures of Location or Centrality) 140 Arithmetic Mean 140 Median 141 Mode 141 Measures of Dispersion (Also Called Measures of Spread or Decentrality) 142 Range 142 Variance 142 Standard Deviation 143 Mean Absolute Deviation 143 Quartiles and Interquartile Range 143 Box-and-Whiskers Plot 143 Shape of a Distribution 145 0 APPLICATION CASE 3.3 Town of Cary Uses Analytics to Analyze Data from Sensors, Assess Demand, and Detect Problems 150 3.6 Regression Modeling for Inferential Statistics 151 How Do We Develop the Linear Regression Model? 152 How Do We Know If the Model Is Good Enough? 153 What Are the Most Important Assumptions in Linear Regression? 154 Logistic Regression 155 Time-Series Forecasting 156 0 APPLICATION CASE 3.4 Predicting NCAA Bowl Game Outcomes 157 3.7 Business Reporting 163 0 APPLICATION CASE 3.5 Flood of Paper Ends at FEMA 3.8 165 Data Visualization 166 Brief History of Data Visualization 167 0 APPLICATION CASE 3.6 Macfarlan Smith Improves Operational Performance Insight with Tableau Online 169 3.9 Different Types of Charts and Graphs 171 Basic Charts and Graphs 171 Specialized Charts and Graphs 172 Which Chart or Graph Should You Use? 174 3.10 Emergence of Visual Analytics 176 Visual Analytics 178 High-Powered Visual Analytics Environments 180 3.11 Information Dashboards 182 Contents 0 APPLICATION CASE 3.7 Dallas Cowboys Score Big with Tableau and Teknion 184 Dashboard Design 184 0 APPLICATION CASE 3.8 Visual Analytics Helps Energy Supplier Make Better Connections 185 What to Look for in a Dashboard 186 Best Practices in Dashboard Design 187 Benchmark Key Performance Indicators with Industry Standards 187 Wrap the Dashboard Metrics with Contextual Metadata 187 Validate the Dashboard Design by a Usability Specialist 187 Prioritize and Rank Alerts/Exceptions Streamed to the Dashboard 188 Enrich the Dashboard with Business-User Comments 188 Present Information in Three Different Levels 188 Pick the Right Visual Construct Using Dashboard Design Principles 188 Provide for Guided Analytics 188 Chapter Highlights 188 Questions for Discussion References PART II • Key Terms 190 189 • Exercises 190 192 Predictive Analytics/Machine Learning 193 Chapter 4 Data Mining Process, Methods, and Algorithms 194 4.1 Opening Vignette: Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Fight Crime 195 4.2 Data Mining Concepts 198 0 APPLICATION CASE 4.1 Visa Is Enhancing the Customer Experience while Reducing Fraud with Predictive Analytics and Data Mining 199 Definitions, Characteristics, and Benefits 201 How Data Mining Works 202 0 APPLICATION CASE 4.2 American Honda Uses Advanced Analytics to Improve Warranty Claims 203 4.3 Data Mining Versus Statistics 208 Data Mining Applications 208 0 APPLICATION CASE 4.3 Predictive Analytic and Data Mining Help Stop Terrorist Funding 210 4.4 Data Mining Process 211 Step 1: Business Understanding 212 Step 2: Data Understanding 212 Step 3: Data Preparation 213 Step 4: Model Building 214 0 APPLICATION CASE 4.4 Data Mining Helps in Cancer Research 214 Step 5: Testing and Evaluation 217 ix x Contents 4.5 Step 6: Deployment 217 Other Data Mining Standardized Processes and Methodologies 217 Data Mining Methods 220 Classification 220 Estimating the True Accuracy of Classification Models 221 Estimating the Relative Importance of Predictor Variables 224 Cluster Analysis for Data Mining 228 0 APPLICATION CASE 4.5 Influence Health Uses Advanced Predictive Analytics to Focus on the Factors That Really Influence People’s Healthcare Decisions 229 4.6 Association Rule Mining 232 Data Mining Software Tools 236 0 APPLICATION CASE 4.6 Data Mining goes to Hollywood: Predicting Financial Success of Movies 239 4.7 Data Mining Privacy Issues, Myths, and Blunders 242 0 APPLICATION CASE 4.7 Predicting Customer Buying Patterns—The Target Story 243 Data Mining Myths and Blunders 244 Chapter Highlights 246 Questions for Discussion References • 247 Key Terms 247 • Exercises 248 250 Chapter 5 Machine-Learning Techniques for Predictive Analytics 5.1 5.2 251 Opening Vignette: Predictive Modeling Helps Better Understand and Manage Complex Medical Procedures 252 Basic Concepts of Neural Networks 255 Biological versus Artificial Neural Networks 256 0 APPLICATION CASE 5.1 Neural Networks are Helping to Save Lives in the Mining Industry 258 5.3 Neural Network Architectures 259 Kohonen’s Self-Organizing Feature Maps 259 Hopfield Networks 260 0 APPLICATION CASE 5.2 Predictive Modeling Is Powering the Power Generators 261 5.4 Support Vector Machines 263 0 APPLICATION CASE 5.3 Identifying Injury Severity Risk Factors in Vehicle Crashes with Predictive Analytics 264 Mathematical Formulation of SVM 269 Primal Form 269 Dual Form 269 Soft Margin 270 Nonlinear Classification 270 Kernel Trick 271 Contents 5.5 5.6 Process-Based Approach to the Use of SVM 271 Support Vector Machines versus Artificial Neural Networks 273 Nearest Neighbor Method for Prediction 274 Similarity Measure: The Distance Metric 275 Parameter Selection 275 0 APPLICATION CASE 5.4 Efficient Image Recognition and Categorization with knn 277 5.7 Naïve Bayes Method for Classification 278 Bayes Theorem 279 Naïve Bayes Classifier 279 Process of Developing a Naïve Bayes Classifier 280 Testing Phase 281 0 APPLICATION CASE 5.5 Predicting Disease Progress in Crohn’s Disease Patients: A Comparison of Analytics Methods 282 5.8 5.9 Bayesian Networks 287 How Does BN Work? 287 How Can BN Be Constructed? 288 Ensemble Modeling 293 Motivation—Why Do We Need to Use Ensembles? 293 Different Types of Ensembles 295 Bagging 296 Boosting 298 Variants of Bagging and Boosting 299 Stacking 300 Information Fusion 300 Summary—Ensembles are not Perfect! 301 0 APPLICATION CASE 5.6 To Imprison or Not to Imprison: A Predictive Analytics-Based Decision Support System for Drug Courts 304 Chapter Highlights 306 Questions for Discussion Internet Exercises 312 • Key Terms 308 • 308 • Exercises References 309 313 Chapter 6 Deep Learning and Cognitive Computing 315 6.1 Opening Vignette: Fighting Fraud with Deep Learning and Artificial Intelligence 316 6.2 Introduction to Deep Learning 320 0 APPLICATION CASE 6.1 Finding the Next Football Star with Artificial Intelligence 323 6.3 Basics of “Shallow” Neural Networks 325 0 APPLICATION CASE 6.2 Gaming Companies Use Data Analytics to Score Points with Players 328 0 APPLICATION CASE 6.3 Artificial Intelligence Helps Protect Animals from Extinction 333 xi xii Contents 6.4 6.5 Process of Developing Neural Network–Based Systems 334 Learning Process in ANN 335 Backpropagation for ANN Training 336 Illuminating the Black Box of ANN 340 0 APPLICATION CASE 6.4 Sensitivity Analysis Reveals Injury Severity Factors in Traffic Accidents 341 6.6 Deep Neural Networks 343 Feedforward Multilayer Perceptron (MLP)-Type Deep Networks 343 Impact of Random Weights in Deep MLP 344 More Hidden Layers versus More Neurons? 345 0 APPLICATION CASE 6.5 Georgia DOT Variable Speed Limit Analytics Help Solve Traffic Congestions 346 6.7 Convolutional Neural Networks 349 Convolution Function 349 Pooling 352 Image Processing Using Convolutional Networks 353 0 APPLICATION ... 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