University of Cumberlands Definition Importance and Sources of Big Data Question Briefly Explain with minimum of 1 parapgrah for each (1-6) Questions with

University of Cumberlands Definition Importance and Sources of Big Data Question Briefly Explain with minimum of 1 parapgrah for each (1-6) Questions with citation in APA Format. Please refer to the chapter 9 in the attached textbook.

1. What is Big Data? Why is it important? Where does BigData come from?

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2. What do you think the future of Big Data will be? Will it lose its popularity to something else? If so, what will it be?

3. What is Big Data analytics? How does it differ from regular analytics?

4. What are the critical success factors for Big Data analytics?

5. What are the big challenges that one should be mindful of when considering implementation of Big Data analytics?

6. At teradatauniversitynetwork.com, go to the Sports Analytics page. Find applications of Big Data in sports. Summarize your findings. ELEVENTH EDITION
ANALYTICS, DATA SCIENCE, &
ARTIFICIAL INTELLIGENCE
SYSTEMS FOR DECISION SUPPORT
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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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