• Document: Introduction to Big Data Analytics p. 1 Big Data Overview p. 2 Data Structures p. 5 Analyst Perspective on Data Repositories p.
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Introduction p. xvii Introduction to Big Data Analytics p. 1 Big Data Overview p. 2 Data Structures p. 5 Analyst Perspective on Data Repositories p. 9 State of the Practice in Analytics p. 11 BI Versus Data Science p. 12 Current Analytical Architecture p. 73 Drivers of Big Data p. 15 Emerging Big Data Ecosystem and a New Approach to Analytics p. 16 Key Roles for the New Big Data Ecosystem p. 19 Examples of Big Data Analytics p. 22 Summary p. 23 Exercises p. 23 Bibliography p. 24 Data Analytics Lifestyle p. 25 Data Analytics Lifecycle Overview p. 26 Key Roles for a Successful Analytics Project p. 26 Background and Overview of Data Analytics Lifecycle p. 28 Phase 1: Discovery p. 30 Learning the Business Domain p. 30 Resources p. 31 Framing the Problem p. 32 Identifying Key Stakeholders p. 33 Interviewing the Analytics Sponsor p. 33 Developing Initial Hypotheses p. 35 Identifying Potential Data Sources p. 35 Phase 2: Data Preparation p. 36 Preparing the Analytic Sandbox p. 37 Performing ETLT p. 38 Learning About the Data p. 39 Data Conditioning p. 40 Survey and Visualize p. 47 Common Tools for the Data Preparation Phase p. 42 Phase 3: Model Planning p. 42 Data Exploration and Variable Selection p. 44 Model Selection p. 45 Common Tools for the Model Planning Phase p. 45 Phase 4: Model Building p. 46 Common Tools for the Model Building Phase p. 48 Phase 5: Communicate Results p. 49 Phase 6: Operationalize p. 50 Case Study: Global Innovation Network and Analysis (GINA) p. 53 Phase 1: Discovery p. 54 Phase 2: Data Preparation p. 55 Phase 3: Model Planning p. 56 Phase 4: Model Building p. 56 Phase 5: Communicate Results p. 58 Phase 6: Operationalize p. 59 Summary p. 60 Exercises p. 61 Bibliography p. 61 Review of Bask Data Analytic Methods Using R p. 63 Introduction to R p. 64 R Graphical User interfaces p. 67 Data import and Export p. 69 Attribute and Data Types p. 77 Descriptive Statistics p. 79 Exploratory Data Analysis p. 80 Visualization Before Analysis p. 82 Dirty Data p. 85 Visualizing a Single Variable p. 88 Examining Multiple Variables p. 91 Data Exploration Versus Presentation p. 99 Statistical Methods for Evaluation p. 101 Hypothesis Testing p. 102 Difference of Means p. 704 Wilcoxon Rank-Sum Test p. 108 Type I and Type II Errors p. 709 Power and Sample Size p. 110 ANOVA p. 770 Summary p. 114 Exercises p. 114 Bibliography p. 115 Advanced Analytical Theory and Methods: Clustering p. 117 Overview of Clustering p. 118 K-means

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