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

  • ID: 2741514
  • Book
  • 432 Pages
  • John Wiley and Sons Ltd
1 of 4

Data Science and Big Data Analytics

Discovering, Analyzing, Visualizing and Presenting Data

Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities, methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are relevant to any industry and technology environment, and the learning is supported and explained with illustrative examples using open–source software.

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 analyze big data
  • Learn how to tell a compelling story with data to drive business action
  • Prepare for EMC ProvenTM Professional Data Scientist certification

EMC Proven Professional is a leading education and certification program in the IT industry, providing comprehensive coverage of information storage technologies, virtualization, cloud computing, data science/big data analytics, and more...

Being Proven means investing in yourself and formally validating your expertise!

This book prepares you for the Data Science Associate exam E20–007 leading to EMC Proven Professional Data Science Associate (EMCDSA) certification.

Visit [external URL] for details.

Note: Product cover images may vary from those shown
2 of 4
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

Bibliography115

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 Regression178

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

Note: Product cover images may vary from those shown
3 of 4

Loading
LOADING...

4 of 4
EMC Education Services
Note: Product cover images may vary from those shown
5 of 4
Note: Product cover images may vary from those shown
Adroll
adroll