Cover -- Title Page -- Copyright -- Contents -- Introduction -- Chapter 1 Introduction to Big Data Analytics -- 1.1 Big Data Overview -- 1.1.1 Data Structures -- 1.1.2 Analyst Perspective on Data Repositories -- 1.2 State of the Practice in Analytics -- 1.2.1 BI Versus Data Science -- 1.2.2 Current Analytical Architecture -- 1.2.3 Drivers of Big Data -- 1.2.4 Emerging Big Data Ecosystem and a New Approach to Analytics -- 1.3 Key Roles for the New Big Data Ecosystem -- 1.4 Examples of Big Data Analytics -- Summary -- Exercises -- Bibliography -- Chapter 2 Data Analytics Lifecycle -- 2.1 Data Analytics Lifecycle Overview -- 2.1.1 Key Roles for a Successful Analytics Project -- 2.1.2 Background and Overview of Data Analytics Lifecycle -- 2.2 Phase 1: Discovery -- 2.2.1 Learning the Business Domain -- 2.2.2 Resources -- 2.2.3 Framing the Problem -- 2.2.4 Identifying Key Stakeholders -- 2.2.5 Interviewing the Analytics Sponsor -- 2.2.6 Developing Initial Hypotheses -- 2.2.7 Identifying Potential Data Sources -- 2.3 Phase 2: Data Preparation -- 2.3.1 Preparing the Analytic Sandbox -- 2.3.2 Performing ETLT -- 2.3.3 Learning About the Data -- 2.3.4 Data Conditioning -- 2.3.5 Survey and Visualize -- 2.3.6 Common Tools for the Data Preparation Phase -- 2.4 Phase 3: Model Planning -- 2.4.1 Data Exploration and Variable Selection -- 2.4.2 Model Selection -- 2.4.3 Common Tools for the Model Planning Phase -- 2.5 Phase 4: Model Building -- 2.5.1 Common Tools for the Model Building Phase -- 2.6 Phase 5: Communicate Results -- 2.7 Phase 6: Operationalize -- 2.8 Case Study: Global Innovation Network and Analysis (GINA) -- 2.8.1 Phase 1: Discovery -- 2.8.2 Phase 2: Data Preparation -- 2.8.3 Phase 3: Model Planning -- 2.8.4 Phase 4: Model Building -- 2.8.5 Phase 5: Communicate Results -- 2.8.6 Phase 6: Operationalize -- Summary -- Exercises.

Bibliography -- Chapter 3 Review of Basic Data Analytic Methods Using R -- 3.1 Introduction to R -- 3.1.1 R Graphical User Interfaces -- 3.1.2 Data Import and Export -- 3.1.3 Attribute and Data Types -- 3.1.4 Descriptive Statistics -- 3.2 Exploratory Data Analysis -- 3.2.1 Visualization Before Analysis -- 3.2.2 Dirty Data -- 3.2.3 Visualizing a Single Variable -- 3.2.4 Examining Multiple Variables -- 3.2.5 Data Exploration Versus Presentation -- 3.3 Statistical Methods for Evaluation -- 3.3.1 Hypothesis Testing -- 3.3.2 Difference of Means -- 3.3.3 Wilcoxon Rank-Sum Test -- 3.3.4 Type I and Type II Errors -- 3.3.5 Power and Sample Size -- 3.3.6 ANOVA -- Summary -- Exercises -- Bibliography -- Chapter 4 Advanced Analytical Theory and Methods: Clustering -- 4.1 Overview of Clustering -- 4.2 K-means -- 4.2.1 Use Cases -- 4.2.2 Overview of the Method -- 4.2.3 Determining the Number of Clusters -- 4.2.4 Diagnostics -- 4.2.5 Reasons to Choose and Cautions -- 4.3 Additional Algorithms -- Summary -- Exercises -- Bibliography -- Chapter 5 Advanced Analytical Theory and Methods: Association Rules -- 5.1 Overview -- 5.2 Apriori Algorithm -- 5.3 Evaluation of Candidate Rules -- 5.4 Applications of Association Rules -- 5.5 An Example: Transactions in a Grocery Store -- 5.5.1 The Groceries Dataset -- 5.5.2 Frequent Itemset Generation -- 5.5.3 Rule Generation and Visualization -- 5.6 Validation and Testing -- 5.7 Diagnostics -- Summary -- Exercises -- Bibliography -- Chapter 6 Advanced Analytical Theory and Methods: Regression -- 6.1 Linear Regression -- 6.1.1 Use Cases -- 6.1.2 Model Description -- 6.1.3 Diagnostics -- 6.2 Logistic Regression -- 6.2.1 Use Cases -- 6.2.2 Model Description -- 6.2.3 Diagnostics -- 6.3 Reasons to Choose and Cautions -- 6.4 Additional Regression Models -- Summary -- Exercises.

Chapter 7 Advanced Analytical Theory and Methods: Classification -- 7.1 Decision Trees -- 7.1.1 Overview of a Decision Tree -- 7.1.2 The General Algorithm -- 7.1.3 Decision Tree Algorithms -- 7.1.4 Evaluating a Decision Tree -- 7.1.5 Decision Trees in R -- 7.2 Naïve Bayes -- 7.2.1 Bayes' Theorem -- 7.2.2 Naïve Bayes Classifier -- 7.2.3 Smoothing -- 7.2.4 Diagnostics -- 7.2.5 Naïve Bayes in R -- 7.3 Diagnostics of Classifiers -- 7.4 Additional Classification Methods -- Summary -- Exercises -- Bibliography -- Chapter 8 Advanced Analytical Theory and Methods: Time Series Analysis -- 8.1 Overview of Time Series Analysis -- 8.1.1 Box-Jenkins Methodology -- 8.2 ARIMA Model -- 8.2.1 Autocorrelation Function (ACF) -- 8.2.2 Autoregressive Models -- 8.2.3 Moving Average Models -- 8.2.4 ARMA and ARIMA Models -- 8.2.5 Building and Evaluating an ARIMA Model -- 8.2.6 Reasons to Choose and Cautions -- 8.3 Additional Methods -- Summary -- Exercises -- Chapter 9 Advanced Analytical Theory and Methods: Text Analysis -- 9.1 Text Analysis Steps -- 9.2 A Text Analysis Example -- 9.3 Collecting Raw Text -- 9.4 Representing Text -- 9.5 Term Frequency-Inverse Document Frequency (TFIDF) -- 9.6 Categorizing Documents by Topics -- 9.7 Determining Sentiments -- 9.8 Gaining Insights -- Summary -- Exercises -- Bibliography -- Chapter 10 Advanced Analytics-Technology and Tools: MapReduce and Hadoop -- 10.1 Analytics for Unstructured Data -- 10.1.1 Use Cases -- 10.1.2 MapReduce -- 10.1.3 Apache Hadoop -- 10.2 The Hadoop Ecosystem -- 10.2.1 Pig -- 10.2.2 Hive -- 10.2.3 HBase -- 10.2.4 Mahout -- 10.3 NoSQL -- Summary -- Exercises -- Bibliography -- Chapter 11 Advanced Analytics-Technology and Tools: In-Database Analytics -- 11.1 SQL Essentials -- 11.1.1 Joins -- 11.1.2 Set Operations -- 11.1.3 Grouping Extensions -- 11.2 In-Database Text Analysis.

11.3 Advanced SQL -- 11.3.1 Window Functions -- 11.3.2 User-Defined Functions and Aggregates -- 11.3.3 Ordered Aggregates -- 11.3.4 MADlib -- Summary -- Exercises -- Bibliography -- Chapter 12 The Endgame, or Putting It All Together -- 12.1 Communicating and Operationalizing an Analytics Project -- 12.2 Creating the Final Deliverables -- 12.2.1 Developing Core Material for Multiple Audiences -- 12.2.2 Project Goals -- 12.2.3 Main Findings -- 12.2.4 Approach -- 12.2.5 Model Description -- 12.2.6 Key Points Supported with Data -- 12.2.7 Model Details -- 12.2.8 Recommendations -- 12.2.9 Additional Tips on Final Presentation -- 12.2.10 Providing Technical Specifications and Code -- 12.3 Data Visualization Basics -- 12.3.1 Key Points Supported with Data -- 12.3.2 Evolution of a Graph -- 12.3.3 Common Representation Methods -- 12.3.4 How to Clean Up a Graphic -- 12.3.5 Additional Considerations -- Summary -- Exercises -- References and Further Reading -- Bibliography -- Index -- EULA.