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DS WK2 This week’s reading centered around how Big Data analytics can be used with Smart Cities. This is exciting and can provide many benefits to individu

DS WK2 This week’s reading centered around how Big Data analytics can be used with Smart Cities. This is exciting and can provide many benefits to individu

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DS WK2 This week’s reading centered around how Big Data analytics can be used with Smart Cities. This is exciting and can provide many benefits to individuals as well as organizations. For this week’s research assignment, you are to search the Internet for other uses of Big Data in RADICAL platforms. Please pick an organization or two and discuss the usage of big data in RADICAL platforms including how big data analytics is used in those situations as well as with Smart Cities. Be sure to use scholarly research. Google Scholar is the 2nd best option to use for research.
Your paper should meet these requirements:

Be approximately 5 pages in length, not including the required cover page and reference page.
Follow APA 7 guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion.
Support your answers with the readings from the course and at least two scholarly journal articles to support your positions, claims, and observations, in addition to your textbook. And atleast 3 peer reviewed articles
Be clearly and well-written, concise, and logical, using excellent grammar and style techniques. You are being graded in part on the quality of your writing.

 
Required Reading
Required ReadingPsomakelis, E., Aisopos, F., Litke, A., Tserpes, K., Kardara, M., & Campo, P. M. (2016). Big IoT and social networking data for smart cities: Algorithmic improvements on Big Data Analysis in the context of RADICAL city applications. Data Science &
Big Data Analytics

Discovering, Analyzing, Visualizing
and Presenting Data

EMC Education Services

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Data Science & Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data

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Project Editor

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Manager of Content Development

and Assembly

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Marketing Manager

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Project Coordinator, Cover

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About the Key Contributors

David Dietrich heads the data science education team within EMC Education Services, where he leads the

curriculum, strategy and course development related to Big Data Analytics and Data Science. He co-au-
thored the first course in EMC’s Data Science curriculum, two additional EMC courses focused on teaching
leaders and executives about Big Data and data science, and is a contributing author and editor of this

book. He has filed 14 patents in the areas of data science, data privacy, and cloud computing.
David has been an advisor to severa l universities looking to develop academic programs related to data

analytics, and has been a frequent speaker at conferences and industry events. He also has been a a guest lecturer at universi-
ties in the Boston area. His work has been featured in major publications including Forbes, Harvard Business Review, and the
2014 Massachusetts Big Data Report, commissioned by Governor Deval Patrick.

Involved with analytics and technology for nearly 20 years, David has worked with many Fortune 500 companies over his
career, holding mu lti ple roles involving analytics, including managing ana lytics and operations teams, delivering analytic con-

sulting engagements, managing a line of analytical software products for regulating the US banking industry, and developing
Sohware-as-a-Service and BI-as-a-Service offerings. Additionally, David collaborated with the U.S. Federal Reserve in develop-

ing predictive models for monitoring mortgage portfolios.
Barry Heller is an advisory technical education consultant at EMC Education Services. Barry is a course developer and cu r-

riculum advisor in the emerging technology areas of Big Data and data science. Prior to his current role, Barry was a consul-
tant research scientist leadi ng numerous analytical initiatives within EMC’s Total Customer Experience
organization. Early in his EMC career, he managed the statistical engineering group as well as led the

data warehousing efforts in an Enterprise Resource Planning (ERP) implementation. Prior to joining EMC,

Barry held managerial and analytical roles in reliability engineering functions at medical diagnostic and
technology companies. During his career, he has applied his quantitative skill set to a myriad of business
applications in the Customer Service, Engineering, Ma nufacturing, Sales/Marketing, Finance, and Legal

arenas. Underscoring the importance of strong executive stakeholder engagement, many of his successes

have resulted from not only focusing on the technical details of an analysis, but on the decisions that will be resulting from
the analysis. Barry earned a B.S. in Computational Mathematics from the Rochester Institute ofTechnology and an M.A. in

Mathematics from the State University of New York (SUNY) New Paltz.
Beibei Yang is a Technical Education Consultant of EMC Education Services, responsible for developing severa l open courses

at EMC related to Data Science and Big Data Analytics. Beibei has seven years of experi ence in the IT industry. Prior to EMC she
worked as a sohware engineer, systems manager, and network manager for a Fortune 500 company where she introduced

new technologies to improve efficiency and encourage collaboration. Beibei has published papers to

prestigious conferences and has filed multiple patents. She received her Ph.D. in computer science from
the University of Massachusetts Lowell. She has a passion toward natural language processing and data

mining, especially using various tools and techniques to find hidden patterns and tell storie s with data.
Data Science and Big Data Analytics is an exciting domain where the potential of digital information is
maximized for making intelligent business decisions. We believe that this is an area that will attract a lot of
talented students and professiona ls in the short, mid, and long term.

Acknowledgments

EMC Education Services embarked on learning this subject with the intent to develop an “open” curriculum and
certification. It was a challenging journey at the time as not many understood what it would take to be a true

data scientist. After initial research (and struggle), we were able to define what was needed and attract very
talented professionals to work on the project. The course, “Data Science and Big Data Analytics,” has become

well accepted across academia and the industry.
Led by EMC Education Services, this book is the result of efforts and contributions from a number of key EMC
organizations and supported by the office of the CTO, IT, Global Services, and Engi neering. Many sincere

thanks to many key contributors and subject matter experts David Dietrich, Barry Heller, and Beibei Yang
for their work developing content and graphics for the chapters. A special thanks to subject matter experts
John Cardente and Ganesh Rajaratnam for their active involvement reviewing multiple book chapters and

providing valuable feedback throughout the project.

We are also grateful to the fol lowing experts from EMC and Pivotal for their support in reviewing and improving
the content in this book:

Aidan O’Brien Joe Kambourakis

Alexander Nunes Joe Milardo

Bryan Miletich John Sopka

Dan Baskette Kathryn Stiles

Daniel Mepham Ken Taylor

Dave Reiner Lanette Wells

Deborah Stokes Michael Hancock

Ellis Kriesberg Michael Vander Donk

Frank Coleman Narayana n Krishnakumar

Hisham Arafat Richard Moore

Ira Sch ild Ron Glick

Jack Harwood Stephen Maloney

Jim McGroddy Steve Todd

Jody Goncalves Suresh Thankappan

Joe Dery Tom McGowa n

We also thank Ira Schild and Shane Goodrich for coordinating this project, Mallesh Gurram for the cover design, Chris Conroy
and Rob Bradley for graphics, and the publisher, John Wiley and Sons, for timely support in bringing this book to the

industry.

Nancy Gessler

Director, Education Services, EMC Corporation

Alok Shrivastava

Sr. Direc tor, Education Services, EMC Corporation

Contents
Introduction ……………. . .. . …..• . •.. … …. •….. .. .. . .. . ………. .. … . ………………… •.•…… xvii

Chapter 1 • Introduction to Big Data Analytics ………………. . . . ………………….. 1

1.1 Big Data Overview ………………… ……. …..•… • …… . . . …….. • .. … . . … ……. ……. 2
1.1.1 Data Structures .. . .. . . . .. ……………. … … . .. . …… . .. .. …. . ……………….. ….. . .. . . . .. 5
1.1.2 Analyst Perspective on Data Repositories . ……………………….. . ………. …….•. … … .. .. 9

1.2 State of the Practice in Analytics ……………………………………………………….. . 11
1.2.1 Bl Versus Data Science ………….. …. ……. . .. . ……….. . . . …. . ………………….. .. …. 12
1.2.2 Current Analytical Architecture … . …. .• . . ……………. …. ………….. …. …. …… •.. . ….. 13
1.2.3 Drivers of Big Data ……………………………………………. . . . .. …………….. .. … . . 15
1.2.4 Emerging Big Data Ecosystem and a New Approach to Analytics .. ……. …… . ………… .. ……. 16

1.3 Key Roles for the New Big Data Ecosystem ……. ….. ……… . ……. . ….. .. ……………….. 19
1.4 Examples of Big Data Analytics … …. ………. …. . … ……. … …. . …… . ……………….. 22
Summary ………….. ………… … … ……… …. • … •……. …….. .. • ..•… . ……………. 23
Exercises ………………… …. ….. .. …… . ……•……… .. .. . … …. . ..•……………….. 23
Bibliography ……………………… …. .. … … … •………………. .. • …… ….. ….. ……. 24

Chapter 2 • Data Ana lytics Lifecycle …………………………………………….. . 25
2.1 Data Analytics Lifecycle Overview … ….. . …………. • …… •.. ….. …… • … •…………. . . . 26

2.1.1 Key Roles for a Successful Anolytics Project …. . .. . …. …. . …….. . .. .. . ..•……… •. •……. . .. . . 26
2.1.2 Background and Overview of Data Analytics Lifecyc/e …………………….. . …….•… . ….. … 28

2.2 Phase 1: Discovery ….. .. .. .. . ……………………….. . ..•………………… •……….. . 30
2.2.1 Learning the Business Domain .. . ……. … ..•.•. •…. . .. ….. . . .. . ……………….•……….. .30
2.2.2 Resources . . … . ………………. . …… . ……………………. ….. …………. •…….•…. 31
2.2.3 Framing the Problem …………•…. . ……………………………..•……… •.•…. . . …… 32
2.2.41dentifying Key Stakeholders … .. ………………….. … . … ……… …. . ……. •. . ………. . . 33
2.2.51nterviewing the Analytics Sponsor …… …….. …… .. ………. …. … .. … ….. .. ……….. … 33
2.2.6 Developing Initial Hypotheses …………….. .. . . . .. . . . .. . . . . … …. .. ……….. . . •………… . . 35
2.2.71dentifying Po tential Data Sources . … …•. •.. …. . . .. . ……•. •………. . ……. . ….. . … . .. .. . . 35

2.3 Phase 2: Data Preparation …………………………………………………..•…•..•….. 36
2.3.1 Preparing the Analytic Sandbox . ………….. . …………………. … •. •…….•………. .. …. 37
2.3.2 Performing ETLT …………………………………………………………•.•…….•… .. . 38
2.3.3 Learning About the Data .. ….. . ………….. .. ……………………•.•…….•.•…….. ….. . 39
2.3.4 Data Conditioning ……. .. ….•………. . ………………….. .. . .. . . . ……•. •…………. .. .40
2.3.5 Survey and Visualize . . . … .. …. .. .. …… . . ….. .. . ……………… . . •. …… . .•.. .. .. .. . . . ….. 41
2.3.6 Common Tools for the Data Preparation Phase . . . …. .. ….. ……. . •……… •.• .•.. .. ….. .. .. . . .42

2.4 Phase 3: Model Planning ……………………….•…………….. . … . .. •….. …..•…….. 42
2.4.1 Data Exploration and Variable Selection . . … . . .. . ……… •… . … . . …….. . ………….. .. .. . . . .44
2.4.2 Model Selection . … ……………. . .. . . . ……………. •…….•…•…………………….. . .45
2.4.3 Common Tools for the Model Planning Phase . . ……….•……. . . •. ……………………… . . . .45

CONTENTS

2.5 Phase 4: Model Building …… ……………… …… •. … ….. …. • … •. . •. .. •………•…•…. 46
2.5.1 Common Tools for th e Mode/Building Phase …… .. .. . ….. .. ….. . ……. . .. . . .. . . .. . …. . . .. . …. 48

2.6 Phase 5: Communicate Re sults ……… …. …… . … •…….. …….. … . •….. …..•. ….. •…. 49
2.7 Phase 6: Operationalize … … ……. … . .. …….. ……. … ……….. •. . •. . … ……. ………. SO
2.8 Case Study: Global Innovation Network and Analysis (GINA) …………….. •…………………. 53

2.8.1 Phase 1: Discovery ……………………………………………………………………… 54
2.8.2 Phase 2: Data Preparation …. •…….. . ……………………………………………… . …. 55
2.8.3 Phase 3: Model Planning . . . …•.•. . . .. . . ….. .. . . .. . ….. .. .. … …… . . . ………………. . . . .. . . 56
2.8.4 Phase 4: Mode/Building ….. . ….•.. .. .. ………. . ………….. . . .. . … . . ……. .. . …. … . .. . . . 56
2.8.5 Phase 5: Commun icate Results .. . . ….. . …… .. …… … .. . .. . . ………………… …… …….. 58
2.8.6 Phase 6: Operationalize . . … ……•….. ..• .. . . . .. . . …………..•………………………….. 59

Summary …………………………… • …………….. •..•.. •…….•…..••…….. . ….•…. 60
Exercises ……………………………•…. .. …………..•. . •………………….. . . . . . •…. 61
Bibliography ….• . .••……………………………..•…. . . • ….. .. ……………………….. 61

Chapter 3 • Review of Basic Data Analytic Methods Using R . . . . . . .. . … . .. .. . … . . . . . .. … . 63

3.1 Introd uction toR ………………………. … ……………………………… ….. ……… 64
3.1.1 R Graphical User Interfaces . ………… . …………………………. …… . .. … . . . … ……. … 67
3.1.2 Data Import and Export. . ……… . .. …………. ……….. ……….. ……………….. ……. 69
3.1.3 Attribute and Data Types . ………. .. …… . ………………………………………………. 71
3.1.4 Descriptive Statistics ………………….. . . . …………………………………………….. 79

3.2 Exploratory Data Analysis ………….. • … . .• •………….•……….. . ……………….. …. 80
3.2.1 Visualization Before Analysis …….. . …………………………………………..•……….. 82
3.2.2 Dirty Data ………… .. ………………………………………… . ……….. …•…… …. . 85
3.2.3 Visualizing a Single Variable …….. •.. . ……………. .. .. . . ……….. . …. ……. •.. . . . …. .. . . 88
3.2.4 Examining Multiple Varia bles . …. …. ….• . .. . … ………. ………….. …… . .. .. ………….. 91
3.2.5 Data Exploration Versus Presentation …… . …….. •. . . . .. . . ….. …… ………………. …… .. 99

3.3 Statistical Methods for Evaluation ……………….. . .. .• ……… … . .. ……………….. . .. 101
3.3.1 Hypoth esis Testing …….. …….. ………. …. ………………………. . .. . …… .. …… . … 102
3.3.2 Difference of Means …… . …. .. . …. ….. . …………………………………………….. 704
3.3.3 Wilcoxon Rank-Sum Test …………….•…………………… … .. . … . ……………… •… 108
3.3.4 Type I and Type II Errors … . …… . .. . ……………… . …….. . .. …. .. ……………………. 109
3.3.5 Power and Sample Size …..•.. . . .. . … …… . …….. ……. ………….. ……. .. …. ………. 110
3.3.6 ANOVA . ……………. . .. ……… . . …. .. . . … …. …….. . . .. ….. . … .. .. …. . •. •…….•… . 110

Summary …… …………. • ……. …… ….• .. •… • …………………………. •……•…… 114
Exercises …… ……… ……………………. . …………… …… . … … ……. •…………. 114
Bibliography …………………………….. . . . …………….. ……………… •…. . . .. . …. 11 5

Chapter 4 • Advanced Analytical Theory and Method s: Clu stering .. . . .. . .. . … . .. . . . … . .. 117

4.1 Overview of Clustering …….. …… ……… .. …………………………………………. 11 8
4.2 K-means …………… ……. … ………………….. .. …….. . … . ………. . …. . …. …. 11 8

4.2.1 Use Cases ….. .. …………. . •…..• … … .. ….. …….. ………. . . .. …….. …… … .. . …… 119
4.2.2 Overview of the Method . ………… ……. … . .. …….. ………………. … … .. . .•. ….. . .. . 120
4.2.3 Determining the Number of Clusters . . . .. .. •. •…………………. . ………. ….. .. … …… . … 123
4.2.4 Diagnostics .. ……………………. …•…. ……….. ….. ………………….. .. .. ……. . 128

CONTENTS

4.2.5 Reasons to Choose and Cautions .. . .. . . . . . . .. . . . . . .. … . ….. … .. .. . . • . •. • . . …•. • .• . … . ….. … 730
4.3 Add itional Algorithms ………….. … . . . . .. . …… . … . …….. .• .. .. . .. ……………. .. …. 134
Summary ……… …. …………………… .. . ………………….. . . . ..•.. . ……………… 135
Exercises ……….. ………………… . . ….. . …………………………. . ………. .. ….. . 135
Bibliography ……………………….. ……. ………………………….. . ……………… 136

Chapter 5 • Advanced Analytica l Theory and Methods: Association Ru les ……………… 137

5.1 Overview …. . . … …………………………………. .. . .. . ….. . .. ……………… .. …. 138
5.2 A priori Algorit hm ……….. . …………… . . . …… … . . …. . . ….. ………. .. ……… … … 140
5.3 Evaluation of Candidate Rules ………………….. . … .. . .. ….. • ……. . ……………. ….. 141
5.4 Applications of Association Rules ………… … ….. . ….. . . . … ….. . . .. . . . …… ………….. 143
5.5 An Example: Transactions in a Grocery Store … . ……………….. …. . . … ………. ……….. 143

5.5.1 The Groceries Dataset ………………. . . .. ………….. •……….. •… . …….•…………… 144
5.5.2 Frequent ltemset Generation . . ……………………… .. ……… . . • . •……… •…………… 146
5.5.3 Rule Generation and Visualization …… . … . ……………………. . .•. •…. . •. •……….. . .. . 752

5.6 Validation and Testing ……….. . … …. . . ……………………………………… . ……. 157
5.7 Diagnostics .. …. ………………… . .. . . ….. . ………… . … . . … . …… . ……… .. …. . . . 158
Summa ry ……. .. ……………. . ….. … . . .. . . …… …. …. . …….. . . …. ….. ………….. . . 158
Exercises ………………………….. … . . . …….. . …………….. . …. ……. ……… . …. 159
Bibliog raphy ………………………….. . .. …. ….. ………… ….. . … ……….. … . …… . 160

Chapter 6 • Advanced Analytical Theory and Methods: Regression ……………… . ….. 161

6.1 Li near Regression ………. . ………. . .. . .. .. …… . ………… …. . . . ……. ……….. …… 162
6.1.1 UseCases . . . … . . . .. . …… ….. ……………………. .. . ……. …. …. .. …… . ………. . .. . /62
6.1.2 Model Description .. … .. . .. . ….. . ……….. . .. . .. …. . . •. ….. . •. •.• . …… . .•…………. . .. . 163
6.1.3 Diagnostics ………………….. . …. .. . . . . . . ……. •.•.• …..•. •.•…… .• . • .•.. . .. . …. . . . . . . . 773

6.2 Logistic Regression ………… …….. . ….. ………………………….. . ……… .. .. . .. .. 178
6.2.1 Use Cases …… . ………………………………… …. ……………. …. ………………. 179
6.2.2 Model Description …….. .. …. … •….. . …. …….. .. .. • . ….. … . .•. •…• .•………………. 179
6.2.3 Diagnostics …………….. ….. …… . . .. …………•. •. ……..•. ….. .• .•………………. 181

6.3 Reasons to Choose and Cautions ……. . . …. .. …. ………… ……….. ……… ……. ….. . 188
6.4 Additional Regression Models ………… … .. …… . … . …………. . … …….. ……….. … 189
Summary ……….. …. . . ……….. . ……. . ………•… . …… . …… … . .. . . … .. ……….. . . 190
Exercises ………… .. ………. .. . .. ……………. .. .. .. ………… . . .. ………. . . . .. .. …. . . 190

Chapter 7 • Advanced Ana lytical Theory and Methods: Classification …… . ………. . …. 191

7.1 Decision Trees … .. …………… …… ………… …………. ………. ………….. … …. 192
7.1.1 Overview of a Decision Tree …… . ……………….. .. . …………………… .. …. ….. . …… 193
7.1.2 The General Algorithm . ………….. ………….. … ..•. … ………….. .• .. .. …….. …. . .. . . 197
7.1.3 Decision Tree Algorithms …………. .. . …. .. ……•. . .•.. … • . •… …. . …. … . ………….. .. 203
7.1.4 Evaluating a Decision Tree …………. . . •… . … . …•… …. . ……. . ……………….. . … . . . . 204
7.1.5 Decision Trees in R . . . .. ……………. …… .. .. ….. ….. …. ……………… . ….. …….. .. 206

7.2 Na’lve Bayes . …. … ……………. . ….. . …… . ………. . .. . … . ….. .. ….. ……… . …… 211
7.2.1 Bayes’ Theorem . . .. . …………………… . …………………………………………….. 212
7.2.2 Nai’ve Bayes Classifier ………………. •… . … ….. …….•……………………………. .. . 214

CONTENTS

7.2.3 Smoothing . …………… ……………….. . .. . …….. . .. . …… .. • . .. ………. .. ………. . 277
7.2.4 Diagnostics .. . ……….. . ………………… .. …. . .•……… •.•…..•…•…….. . . . ……… 217
7.2.5 Nai’ve Bayes in R …………… . . .. . …..•… .. . …•.•………•.•.. .. . .. •. •.•…. …….. . .. …. . 278

7.3 Diagnostics of Classifiers ………… •…… ……….. •………. …•…• .. •… •. …. ……….. 224
7.4 Additiona l Classification Methods …. • … • …… • …………. • ……………..•… …. ……… 228
Summary …………….. ….. ………… • ……•………….. .. ……………………..•….. 229
Exercises ……………… … ……… …. …………………….•…. . . . ……………..•….. 230
Bibliography …… . ……….•……… …. ……….. . … . ………….. … …•………………. 231

Chapter 8 • Advanced Analytical Theory and Methods: Time Series Analysis . . .. … . … . .. . 233

8.1 Overview of Time Series Analysis ……. ……. ……………. ……………………. …. ….. 234
8.1.1 Box-Jenkins Methodology ………………. . .. …. …… . ……………….. . .. ….. ………… 235

8.2 ARIMA Model. ……………. . .. . ……. •..•….. .. …… . … •…………….. • … . ..•…….. 236
8.2.1 Autocorrelation Function (A CF) .. ……… …………………. … …….. . ……… . .. ….. ….. 236
8.2.2 Autoregressive Models . …… … ………… . . . .. •. … ….. … . .. … … . ……… . ……. .. . . …. 238
8.2.3 Moving Average Models . .. .. . ……………………………… ……………….. •….. . …. . 239
8.2.4 ARMA and ARIMA Models …………. . ……………………………•………..•…..•……. 241
8.2.5 Building and Evaluating an ARIMA Model ……………………….. . .•………•. •. . … •…… 244
8.2.6 Reasons to Choose and Cautions .. ……………. . .. . …….. .. . . .. . ……. . …. .•.•. •.. . •. . …. . 252

8.3 Additional Methods …….. … . … ……. … .. …… …… .. ……. ……. .. … . …. . … . …… . 253
Summary …………………… … … …… .. ………… • ……… ……… ..• .. …….• … ….. 254
Exercises ………….. . ………. … ……… . •. .. ………………………..• .. . . .. • . .• … ….. 254

Chapter 9 • Advanced Analytical Theory and Methods: Text Analysis …… . … . .. .. .. . . … 255
9.1 Text Analysis Steps ………. . …. ……… …… … ……………….. . …… . …… . . .•……. 257
9.2 A Text Analysis Example ….. •…. …. ………………………. .. ………… …

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