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

This book will help you:

• Become a contributor on a data science team
• Deploy a structured lifecycle approach to data analytics problems
• Apply appropriate analytic techniques and tools to analyzing big data
• Learn how to tell a compelling story with data to drive business action
• Prepare for EMC Proven Professional Data Science Certification

Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today!

EMC is a global leader in enabling businesses and service providers to transform their operations and deliver IT as a service. Fundamental to this transformation is cloud computing. Through innovative products and services, EMC accelerates the journey to cloud computing, helping IT departments to store, manage, protect and analyze their most valuable asset — information — in a more agile, trusted and cost-efficient way. Additional information about EMC can be found at www.EMC.com

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 BI 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 Analytics Lifecycle 25

2.1 Data Analytics Lifecycle Overview 26

2.1.1 Key Roles for a Successful Analytics Project 26

2.1.2 Background and Overview of Data Analytics Lifecycle 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

2.2.4 Identifying Key Stakeholders 33

2.2.5 Interviewing the Analytics Sponsor 33

2.2.6 Developing Initial Hypotheses 35

2.2.7 Identifying Potential 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

2.5 Phase 4: Model Building 46

2.5.1 Common Tools for the Model Building Phase 48

2.6 Phase 5: Communicate Results 49

2.7 Phase 6: Operationalize 50

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: Model Building 56

2.8.5 Phase 5: Communicate 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 Introduction to R 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 Variables 91

3.2.5 Data Exploration Versus Presentation 99

3.3 Statistical Methods for Evaluation 101

3.3.1 Hypothesis Testing 102

3.3.2 Difference of Means 104

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 115

Chapter 4 Advanced Analytical Theory and Methods: Clustering 117

4.1 Overview of Clustering 118

4.2 K-means 118

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

4.2.5 Reasons to Choose and Cautions 130

4.3 Additional Algorithms 134

Summary 135

Exercises 135

Bibliography 136

Chapter 5 Advanced Analytical Theory and Methods: Association Rules 137

5.1 Overview 138

5.2 Apriori Algorithm 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 Itemset Generation 146

5.5.3 Rule Generation and Visualization 152

5.6 Validation and Testing 157

5.7 Diagnostics 158

Summary 158

Exercises 159

Bibliography 160

Chapter 6 Advanced Analytical Theory and Methods: Regression 161

6.1 Linear Regression 162

6.1.1 Use Cases 162

6.1.2 Model Description 163

6.1.3 Diagnostics 173

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 Analytical 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 Naïve Bayes 211

7.2.1 Bayes’ Theorem 212

7.2.2 Naïve Bayes Classifier 214

7.2.3 Smoothing 217

7.2.4 Diagnostics 217

7.2.5 Naïve Bayes in R 218

7.3 Diagnostics of Classifiers 224

7.4 Additional 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 (ACF) 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 259

9.3 Collecting Raw Text 260

9.4 Representing Text 264

9.5 Term Frequency—Inverse Document Frequency (TFIDF) 269

9.6 Categorizing Documents by Topics 274

9.7 Determining Sentiments 277

9.8 Gaining Insights 283

Summary 290

Exercises 290

Bibliography 291

Chapter 10 Advanced Analytics—Technology and Tools: MapReduce and Hadoop 295

10.1 Analytics for Unstructured Data 296

10.1.1 Use Cases 296

10.1.2 MapReduce 298

10.1.3 Apache Hadoop 300

10.2 The Hadoop Ecosystem 306

10.2.1 Pig 306

10.2.2 Hive 308

10.2.3 HBase 311

10.2.4 Mahout 319

10.3 NoSQL 322

Summary 323

Exercises 324

Bibliography 324

Chapter 11 Advanced Analytics—Technology and Tools: In-Database Analytics 327

11.1 SQL Essentials 328

11.1.1 Joins 330

11.1.2 Set Operations 332

11.1.3 Grouping Extensions 334

11.2 In-Database Text Analysis 338

11.3 Advanced SQL 343

11.3.1 Window Functions 343

11.3.2 User-Defined Functions and Aggregates 347

11.3.3 Ordered Aggregates 351

11.3.4 MADlib 352

Summary 356

Exercises 356

Bibliography 357

Chapter 12 The Endgame, or Putting It All Together 359

12.1 Communicating and Operationalizing an Analytics Project 360

12.2 Creating the Final Deliverables 362

12.2.1 Developing Core Material for Multiple Audiences 364

12.2.2 Project Goals 365

12.2.3 Main Findings 367

12.2.4 Approach 369

12.2.5 Model Description 371

12.2.6 Key Points Supported with Data 372

12.2.7 Model Details 372

12.2.8 Recommendations 374

12.2.9 Additional Tips on Final Presentation 375

12.2.10 Providing Technical Specifications and Code 376

12.3 Data Visualization Basics 377

12.3.1 Key Points Supported with Data 378

12.3.2 Evolution of a Graph 380

12.3.3 Common Representation Methods 386

12.3.4 How to Clean Up a Graphic 387

12.3.5 Additional Considerations 392

Summary 393

Exercises 394

References and Further Reading 394

Bibliography 394

Index 397

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