Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate 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 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!

Table of 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 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