https://genius.com/Maya-angelou-mrs-flowers-from-i-know-why-the-caged-bird-sings-annotated Page 1 of 3 Maya Angelou – Mrs. Flowers (from I Know Why the Caged Bird Sings) 8-10 minutes Mrs. Bertha Flowers was the aristocrat of Black Stamps. She had the grace of control to appear warm in the coldest weather, and on the Arkansas summer days it seemed she had a private breeze which swirled around, cooling her. She was thin without the taut look of wiry people, and her printed voile dresses and flowered hats were as right for her as denim overalls for a farmer. She was our side’s answer to the richest white woman in town. Her skin was a rich black that would have peeled like a plum if snagged, but then no one would have thought of getting close enough to Mrs. Flowers to ruffle her dress, let alone snag her skin. She didn’t encourage familiarity. She wore gloves too. I don’t think I ever saw Mrs. Flowers laugh, but she smiled often. A slow widening of her thin black lips to show even, small white teeth, then the slow effortless closing. When she chose to smile on me, I always wanted to thank her. The action was so graceful and inclusively benign. She was one of the few gentlewomen I have ever known, and has remained throughout my life the measure of what a human being can be. One summer afternoon, sweet-milk fresh in my memory, she stopped at the Store to buy provisions. Another Negro woman of her health and age would have been expected to carry the paper sacks home in one hand, but Momma said, “Sister Flowers, I’ll send Bailey up to your house with these things.” She smiled that slow dragging smile, “Thank you, Mrs. Henderson. I’d prefer Marguerite, though.” My name was beautiful when she said it. “I’ve been meaning to talk to her, anyway.” They gave each other age-group looks. There was a little path beside the rocky road, and Mrs. Flowers walked in front swinging her arms and picking her way over the stones. She said, without turning her head, to me, “I hear you’re doing very good schoolwork, Marguerite, but that it’s all written. The teachers report that they have trouble getting you to talk in class.” We passed the triangular farm on our left and the path widened to allow us to walk together. I hung back in the separate unasked and unanswerable questions. “Come and walk along with me, Marguerite.” I couldn’t have refused even if I wanted to. She pronounced my name so nicely. Or more correctly, she spoke each word with such clarity that I was certain a foreigner who didn’t understand English could have understood her. “Now no one is going to make you talk—possibly no one can. But bear in mind, language is man’s way of communicating with his fellow man and it is language alone which separates him from the lower animals.” That was a totally new idea to me, and I would need time to think about it. https://genius.com/Maya-angelou-mrs-flowers-from-i-know-why-the-caged-bird-sings-annotated https: ...

1. https://genius.com/Maya-angelou-mrs-flowers-from-i-know-
why-the-caged-bird-sings-annotated
Page 1 of 3
Maya Angelou – Mrs. Flowers
(from I Know Why the Caged Bird Sings)
8-10 minutes
Mrs. Bertha Flowers was the aristocrat of Black Stamps. She
had the grace of control to appear
warm in the coldest weather, and on the Arkansas summer days
it seemed she had a private
breeze which swirled around, cooling her. She was thin without
the taut look of wiry people, and
her printed voile dresses and flowered hats were as right for her
as denim overalls for a farmer.
She was our side’s answer to the richest white woman in town.
Her skin was a rich black that would have peeled like a plum if
snagged, but then no one would
have thought of getting close enough to Mrs. Flowers to ruffle
her dress, let alone snag her skin.
She didn’t encourage familiarity. She wore gloves too.
I don’t think I ever saw Mrs. Flowers laugh, but she smiled
often. A slow widening of her thin
black lips to show even, small white teeth, then the slow
effortless closing. When she chose to
smile on me, I always wanted to thank her. The action was so

2. graceful and inclusively benign.
She was one of the few gentlewomen I have ever known, and
has remained throughout my life
the measure of what a human being can be.
One summer afternoon, sweet-milk fresh in my memory, she
stopped at the Store to buy
provisions. Another Negro woman of her health and age would
have been expected to carry the
paper sacks home in one hand, but Momma said, “Sister
Flowers, I’ll send Bailey up to your
house with these things.”
She smiled that slow dragging smile, “Thank you, Mrs.
Henderson. I’d prefer Marguerite,
though.” My name was beautiful when she said it. “I’ve been
meaning to talk to her, anyway.”
They gave each other age-group looks.
There was a little path beside the rocky road, and Mrs. Flowers
walked in front swinging her
arms and picking her way over the stones.
She said, without turning her head, to me, “I hear you’re doing
very good schoolwork,
Marguerite, but that it’s all written. The teachers report that
they have trouble getting you to talk
in class.” We passed the triangular farm on our left and the path
widened to allow us to walk
together. I hung back in the separate unasked and unanswerable
questions.
“Come and walk along with me, Marguerite.” I couldn’t have
refused even if I wanted to. She
pronounced my name so nicely. Or more correctly, she spoke

3. each word with such clarity that I
was certain a foreigner who didn’t understand English could
have understood her.
“Now no one is going to make you talk—possibly no one can.
But bear in mind, language is
man’s way of communicating with his fellow man and it is
language alone which separates him
from the lower animals.” That was a totally new idea to me, and
I would need time to think about
it.
https://genius.com/Maya-angelou-mrs-flowers-from-i-know-
why-the-caged-bird-sings-annotated
https://genius.com/Maya-angelou-mrs-flowers-from-i-know-
why-the-caged-bird-sings-annotated
Page 2 of 3
“Your grandmother says you read a lot. Every chance you get.
That’s good, but not good enough.
Words mean more than what is set down on paper. It takes the
human voice to infuse them with
the shades of deeper meaning.”
I memorized the part about the human voice infusing words. It
seemed so valid and poetic.
She said she was going to give me some books and that I not
only must read them, I must read
them aloud. She suggested that I try to make a sentence sound
in as many different ways as
possible.

4. “I’ll accept no excuse if you return a book to me that has been
badly handled.” My imagination
boggled at the punishment I would deserve if in fact I did abuse
a book of Mrs. Flowers’s. Death
would be too kind and brief.
The odors in the house surprised me. Somehow I had never
connected Mrs. Flowers with food or
eating or any other common experience of common people.
There must have been an outhouse,
too, but my mind never recorded it.
The sweet scent of vanilla had met us as she opened the door.
“I made tea cookies this morning. You see, I had planned to
invite you for cookies and lemonade
so we could have this little chat. The lemonade is in the
icebox.”
It followed that Mrs. Flowers would have ice on an ordinary
day, when most families in our
town bought ice late on Saturdays only a few times during the
summer to be used in the wooden
ice cream freezers.
She took the bags from me and disappeared through the kitchen
door. I looked around the room
that I had never in my wildest fantasies imagined I would see.
Browned photographs leered or
threatened from the walls and the white, freshly done curtains
pushed against themselves and
against the wind. I wanted to gobble up the room entire and take
it to Bailey, who would help me
analyze and enjoy it.
“Have a seat, Marguerite. Over there by the table.” She carried

5. a platter covered with a tea towel.
Although she warned that she hadn’t tried her hand at baking
sweets for some time, I was certain
that like everything else about her the cookies would be perfect.
They were flat round wafers, slightly browned on the edges and
butter-yellow in the center. With
the cold lemonade they were sufficient for childhood’s lifelong
diet. Remembering my manners,
I took nice little ladylike bites off the edges. She said she had
made them expressly for me and
that she had a few in the kitchen that I could take home to my
brother. So I jammed one whole
cake in my mouth and the rough crumbs scratched the insides of
my jaws, and if I hadn’t had to
swallow, it would have been a dream come true.
As I ate she began the first of what we later called “my lessons
in living.” She said that I must
always be intolerant of ignorance but understanding of
illiteracy. That some people, unable to go
to school, were more educated and even more intelligent than
college professors. She encouraged
https://genius.com/Maya-angelou-mrs-flowers-from-i-know-
why-the-caged-bird-sings-annotated
https://genius.com/Maya-angelou-mrs-flowers-from-i-know-
why-the-caged-bird-sings-annotated
Page 3 of 3
me to listen carefully to what country people called mother wit.
That in those homely sayings
was couched the collective wisdom of generations .

6. When I finished the cookies she brushed off the table and
brought a thick, small book from the
bookcase. I had read A Tale of Two Cities and found it up to my
standards as a romantic novel.
She opened the first page and I heard poetry for the first time in
my life.
“It was the best of times, it was the worst of times. . . .” Her
voice slid in and curved down
through and over the words. She was nearly singing. I wanted to
look at the pages. Were they the
same that I had read? Or were there notes, music, lined on the
pages, as in a hymn book? Her
sounds began cascading gently. I knew from listening to a
thousand preachers that she was
nearing the end of her reading, and I hadn’t really heard, heard
to understand, a single word.
“How do you like that?”
It occurred to me that she expected a response. The sweet
vanilla flavor was still on my tongue
and her reading was a wonder in my ears. I had to speak.
I said, “Yes, ma’am.” It was the least I could do, but it was the
most also.
“There’s one more thing. Take this book of poems and
memorize one for me. Next time you pay
me a visit, I want you to recite.”
I have tried often to search behind the sophistication of years
for the enchantment I so easily
found in those gifts. The essence escapes but its aura remains.
To be allowed, no, invited, into

7. the private lives of strangers, and to share their joys and fears,
was a chance to exchange the
Southern bitter wormwood for a cup of mead with Beowulf or a
hot cup of tea and milk with
Oliver Twist. When I said aloud, “It is a far, far better thing
that I do, than I have ever done . . .”
tears of love filled my eyes at my selflessness.
On that first day, I ran down the hill and into the road (few cars
ever came along it) and had the
good sense to stop running before I reached the Store.
I was liked, and what a difference it made. I was respected not
as Mrs. Henderson’s grandchild
or Bailey’s sister but for just being Marguerite Johnson.
Childhood’s logic never asks to be proved (all conclusi ons are
absolute). I didn’t question why
Mrs. Flowers had singled me out for attention, nor did it occur
to me that Momma might have
asked her to give me a little talking-to. All I cared about was
that she had made tea cookies for
me and read to me from her favorite book. It was enough to
prove that she liked me.
https://genius.com/Maya-angelou-mrs-flowers-from-i-know-
why-the-caged-bird-sings-annotated
ANALYTICS, DATA SCIENCE, &
ARTIFICIAL INTELLIGENCE

8. SYSTEMS FOR DECISION SUPPORT
E L E V E N T H E D I T I O N
Ramesh Sharda
Oklahoma State University
Dursun Delen
Oklahoma State University
Efraim Turban
University of Hawaii
Microsoft and/or its respective suppliers make no
representations about the suitability of the information
contained in the documents and related graphics published as
part of the services for any purpose. All such
documents and related graphics are provided “as is” without
warranty of any kind. Microsoft and/or its respective
suppliers hereby disclaim all warranties and conditions with
regard to this information, including all warranties
and conditions of merchantability, whether express, implied or
statutory, fitness for a particular purpose, title and
non-infringement. In no event shall Microsoft and/or its
respective suppliers be liable for any special, indirect
or consequential damages or any damages whatsoever resulting
from loss of use, data or profits, whether in an
action of contract, negligence or other tortious action, arising
out of or in connection with the use or performance
of information available from the services. The documents and
related graphics contained herein could include
technical inaccuracies or typographical errors. Changes are
periodically added to the information herein. Microsoft

9. and/or its respective suppliers may make improvements and/or
changes in the product(s) and/or the program(s)
described herein at any time. Partial screen shots may be viewed
in full within the software version specified.
Microsoft® Windows® and Microsoft Office® are registered
trademarks of Microsoft Corporation in the U.S.A. and
other countries. This book is not sponsored or endorsed by or
affiliated with the Microsoft Corporation.
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

10. Pvt. Ltd.
Manufacturing Buyer: LSC Communications,
Maura Zaldivar-Garcia
Text Printer/Bindery: LSC Communications
Cover Printer: Phoenix Color
ISBN 10: 0-13-519201-3
ISBN 13: 978-0-13-519201-6
Copyright © 2020, 2015, 2011 by Pearson Education, Inc. 221
River Street, Hoboken, NJ 07030. All
rights reserved. Manufactured in the United States of America.
This publication is protected by Copyright, and
permission should be obtained from the publisher prior to any
prohibited reproduction, storage in a retrieval
system, or transmission in any form or by any means,
electronic, mechanical, photocopying, recording, or likewise.
For information regarding permissions, request forms and the
appropriate contacts within the Pearson Education
Global Rights & Permissions Department, please visit
www.pearsoned.com/permissions. Acknowledgments
of third-party content appear on the appropriate page within the
text, which constitutes an extension of this
copyright page. Unless otherwise indicated herein, any third-
party trademarks that may appear in this work are
the property of their respective owners and any references to
third-party trademarks, logos or other trade dress
are for demonstrative or descriptive purposes only. Such
references are not intended to imply any sponsorship,
endorsement, authorization, or promotion of Pearson’s products
by the owners of such marks, or any relationship
between the owner and Pearson Education, Inc. or its affiliates,
authors, licensees or distributors.
Library of Congress Cataloging-in-Publication Data

11. Library of Congress Cataloging in Publication Control Number:
2018051774
http://www.pearsoned.com/permissions
iii
Preface xxv
About the Authors xxxiv
PART I Introduction to Analytics and AI 1
Chapter 1 Overview of Business Intelligence, Analytics,
Data Science, and Artificial Intelligence: Systems
for Decision Support 2
Chapter 2 Artificial Intelligence: Concepts, Drivers, Major
Technologies, and Business Applications 73
Chapter 3 Nature of Data, Statistical Modeling, and
Visualization 117
PART II Predictive Analytics/Machine Learning 193
Chapter 4 Data Mining Process, Methods, and Algorithms 194
Chapter 5 Machine-Learning Techniques for Predictive
Analytics 251
Chapter 6 Deep Learning and Cognitive Computing 315
Chapter 7 Text Mining, Sentiment Analysis, and Social
Analytics 388

12. PART III Prescriptive Analytics and Big Data 459
Chapter 8 Prescriptive Analytics: Optimization and
Simulation 460
Chapter 9 Big Data, Cloud Computing, and Location Analytics:
Concepts and Tools 509
PART IV Robotics, Social Networks, AI and IoT 579
Chapter 10 Robotics: Industrial and Consumer Applications 580
Chapter 11 Group Decision Making, Collaborative Systems,
and
AI Support 610
Chapter 12 Knowledge Systems: Expert Systems,
Recommenders,
Chatbots, Virtual Personal Assistants, and Robo
Advisors 648
Chapter 13 The Internet of Things as a Platform for Intelligent
Applications 687
PART V Caveats of Analytics and AI 725
Chapter 14 Implementation Issues: From Ethics and Privacy to
Organizational and Societal Impacts 726
Glossary 770
Index 785
BRIEF CONTENTS

13. iv
CONTENTS
Preface xxv
About the Authors xxxiv
PART I 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 Opening Vignette: How Intelligent Systems Work for
KONE Elevators and Escalators Company 3
1.2 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
1.3 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

14. 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
The User Interface Subsystem 20
The Knowledge-Based Management Subsystem 21
1.4 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

15. Transaction Processing versus Analytic Processing 27
A Multimedia Exercise in Business Intelligence 28
Contents v
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

16. 0 APPLICATION CASE 1.7 Image Analysis Helps Estimate
Plant Cover 50
1.7 Artificial Intelligence Overview 52
What Is Artificial Intelligence? 52
The Major Benefits of AI 52
The Landscape of AI 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

17. 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
vi Contents
The Book’s Web Site 67
Chapter Highlights 67 • Key Terms 68
Questions for Discussion 68 • Exercises 69
References 70
Chapter 2 Artificial Intelligence: Concepts, Drivers, Major
Technologies, and Business Applications 73

18. 2.1 Opening Vignette: INRIX Solves Transportation
Problems 74
2.2 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
2.3 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? 86

19. 2.4 Major AI Technologies and Some Derivatives 87
Intelligent Agents 87
Machine Learning 88
0 APPLICATION CASE 2.2 How Machine Learning Is
Improving Work
in Business 89
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
2.5 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

20. Contents vii
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 100
Job of Accountants 101
2.7 AI Applications in Financial Services 101
AI Activities in Financial Services 101
AI in Banking: An Overview 101
Illustrative AI Applications in Banking 102
Insurance Services 103
0 APPLICATION CASE 2.5 US Bank Customer Recognition
and
Services 104
2.8 AI in Human Resource Management (HRM) 105
AI in HRM: An Overview 105
AI in Onboarding 105
0 APPLICATION CASE 2.6 How Alexander Mann

21. Solution
s (AMS) Is
Using AI to Support the Recruiting Process 106
Introducing AI to HRM Operations 106
2.9 AI in Marketing, Advertising, and CRM 107
Overview of Major Applications 107
AI Marketing Assistants in Action 108
Customer Experiences and CRM 108
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

22. Management (POM) 110
AI in Manufacturing 110
Implementation Model 111
Intelligent Factories 111
Logistics and Transportation 112
Chapter Highlights 112 • Key Terms 113
Questions for Discussion 113 • Exercises 114
References 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

23. 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
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 139
Descriptive Statistics for Descriptive Analytics 140
Measures of Centrality Tendency (Also Called Measures of
Location or
Centrality) 140
Arithmetic Mean 140

24. 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

25. 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 165
3.8 Data Visualization 166
Brief History of Data Visualization 167
0 APPLICATION CASE 3.6 Macfarlan Smith Improves
Operational

26. 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 ix
0 APPLICATION CASE 3.7 Dallas Cowboys Score Big with
Tableau
and Teknion 184

27. 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

28. Pick the Right Visual Construct Using Dashboard Design
Principles 188
Provide for Guided Analytics 188
Chapter Highlights 188 • Key Terms 189
Questions for Discussion 190 • Exercises 190
References 192
PART II 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

29. How Data Mining Works 202
0 APPLICATION CASE 4.2 American Honda Uses Advanced
Analytics to
Improve Warranty Claims 203
Data Mining Versus Statistics 208
4.3 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

30. Cancer Research 214
Step 5: Testing and Evaluation 217
x Contents
Step 6: Deployment 217
Other Data Mining Standardized Processes and Methodologies
217
4.5 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

31. Analytics to Focus on the Factors That Really Influence
People’s
Healthcare Decisions 229
Association Rule Mining 232
4.6 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 • Key Terms 247
Questions for Discussion 247 • Exercises 248
References 250

32. Chapter 5 Machine-Learning Techniques for Predictive
Analytics 251
5.1 Opening Vignette: Predictive Modeling Helps
Better Understand and Manage Complex Medical
Procedures 252
5.2 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

33. 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 xi
5.5 Process-Based Approach to the Use of SVM 271
Support Vector Machines versus Artificial Neural Networks 273

34. 5.6 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 Bayesian Networks 287

35. How Does BN Work? 287
How Can BN Be Constructed? 288
5.9 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

36. Drug Courts 304
Chapter Highlights 306 • Key Terms 308
Questions for Discussion 308 • Exercises 309
Internet Exercises 312 • References 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

37. 0 APPLICATION CASE 6.3 Artificial Intelligence Helps
Protect Animals
from Extinction 333
xii Contents
6.4 Process of Developing Neural Network–Based
Systems 334
Learning Process in ANN 335
Backpropagation for ANN Training 336
6.5 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

38. 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 CASE 6.6 From Image Recognition to Face
Recognition 356
Text Processing Using Convolutional Networks 357
6.8 Recurrent Networks and Long Short-Term Memory
Networks 360
0 APPLICATION CASE 6.7 Deliver Innovation by

39. Understanding
Customer Sentiments 363
LSTM Networks Applications 365
6.9 Computer Frameworks for Implementation of Deep
Learning 368
Torch 368
Caffe 368
TensorFlow 369
Theano 369
Keras: An Application Programming Interface 370
6.10 Cognitive Computing 370
How Does Cognitive Computing Work? 371
How Does Cognitive Computing Differ from AI? 372

40. Cognitive Search 374
IBM Watson: Analytics at Its Best 375
0 APPLICATION CASE 6.8 IBM Watson Competes against
the
Best at Jeopardy! 376
How Does Watson Do It? 377
What Is the Future for Watson? 377
Chapter Highlights 381 • Key Terms 383
Questions for Discussion 383 • Exercises 384
References 385
Contents xiii
Chapter 7 Text Mining, Sentiment Analysis, and Social
Analytics 388
7.1 Opening Vignette: Amadori Group Converts Consumer

41. Sentiments into Near-Real-Time Sales 389
7.2 Text Analytics and Text Mining Overview 392
0 APPLICATION CASE 7.1 Netflix: Using Big Data to Drive
Big
Engagement: Unlocking the Power of Analytics to Drive
Content and Consumer Insight 395
7.3 Natural Language Processing (NLP) 397
0 APPLICATION CASE 7.2 AMC Networks Is Using
Analytics to
Capture New Viewers, Predict Ratings, and Add Value for
Advertisers
in a Multichannel World 399
7.4 Text Mining Applications 402
Marketing Applications 403
Security Applications 403
Biomedical Applications 404
0 APPLICATION CASE 7.3 Mining for Lies 404

42. Academic Applications 407
0 APPLICATION CASE 7.4 The Magic Behind the Magic:
Instant Access
to Information Helps the Orlando Magic Up their Game and the
Fan’s
Experience 408
7.5 Text Mining Process 410
Task 1: Establish the Corpus 410
Task 2: Create the Term–Document Matrix 411
Task 3: Extract the Knowledge 413
0 APPLICATION CASE 7.5 Research Literature Survey with
Text
Mining 415
7.6 Sentiment Analysis 418
0 APPLICATION CASE 7.6 Creating a Unique Digital
Experience to

43. Capture Moments That Matter at Wimbledon 419
Sentiment Analysis Applications 422
Sentiment Analysis Process 424
Methods for Polarity Identification 426
Using a Lexicon 426
Using a Collection of Training Documents 427
Identifying Semantic Orientation of Sentences and Phrases 428
Identifying Semantic Orientation of Documents 428
7.7 Web Mining Overview 429
Web Content and Web Structure Mining 431
7.8 Search Engines 433
Anatomy of a Search Engine 434
1. Development Cycle 434

44. 2. Response Cycle 435
Search Engine Optimization 436
Methods for Search Engine Optimization 437
xiv Contents
0 APPLICATION CASE 7.7 Delivering Individualized Content
and
Driving Digital Engagement: How Barbour Collected More
Than 49,000
New Leads in One Month with Teradata Interactive 439
7.9 Web Usage Mining (Web Analytics) 441
Web Analytics Technologies 441
Web Analytics Metrics 442
Web Site Usability 442

45. Traffic Sources 443
Visitor Profiles 444
Conversion Statistics 444
7.10 Social Analytics 446
Social Network Analysis 446
Social Network Analysis Metrics 447
0 APPLICATION CASE 7.8 Tito’s Vodka Establishes Brand
Loyalty with
an Authentic Social Strategy 447
Connections 450
Distributions 450
Segmentation 451
Social Media Analytics 451
How Do People Use Social Media? 452

46. Measuring the Social Media Impact 453
Best Practices in Social Media Analytics 453
Chapter Highlights 455 • Key Terms 456
Questions for Discussion 456 • Exercises 456
References 457
PART III Prescriptive Analytics and Big Data 459
Chapter 8 Prescriptive Analytics: Optimization and Simulation
460
8.1 Opening Vignette: School District of Philadelphia Uses
Prescriptive Analytics to Find Optimal