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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1 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

2 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 p. 118 Use Cases p. 119 Overview of the Method p. 720 Determining the Number of Clusters p. 123 Diagnostics p. 128 Reasons to Choose and Cautions p. 130

3 Additional Algorithms p. 134 Summary p. 135 Exercises p. 135 Bibliography p. 136 Advanced Analytical Theory and Methods: Association Rules p. 137 Overview p. 138 Apriori Algorithm p. 140 Evaluation of Candidate Rules p. 141 Applications of Association Rules p. 143 An Example: Transactions in a Grocery Store p. 143 The Groceries Dataset p. 144 Frequent Itemset Generation p. 146 Rule Generation and Visualization p. 152 Validation and Testing p. 157 Diagnostics p. 158 Summary p. 158 Exercises p. 159 Bibliography p. 160 Advanced Analytical Theory and Method: Regression p. 161 Linear Regression p. 162 Use Cases p. 162 Model Description p. 163 Diagnostics p. 173 Logistic Regression p. 178 Use Cases p. 179 Model Description p. 179 Diagnostics p. 181 Reasons to Choose and Cautions p. 188 Additional Regression Models p. 189 Summary p. 190 Exercises p. 190 Advanced Analytical Theory and Methods: Classification p. 191 Decision Trees p. 192 Overview of a Decision Tree p. 193 The General Algorithm p. 197 Decision Tree Algorithms p. 203 Evaluating a Decision Tree p. 204 Decision Trees in R p. 206 Naive Bayes p. 211 Bayes' Theorem p. 272 Naive Bayes Classifier p. 214

4 Smoothing p. 217 Diagnostics p. 217 Naive Bayes in R p. 218 Diagnostics of Classifiers p. 224 Additional Classification Methods p. 228 Summary p. 229 Exercises p. 230 Bibliography p. 231 Advanced Analytical Theory and Methods: Time Series Analysis p. 233 Overview of Time Series Analysis p. 234 Box-Jenkins Methodology p. 235 ARIMA Model p. 236 Autocorrelation Function (ACF) p. 236 Autoregressive Models p. 238 Moving Average Models p. 239 ARMA and ARIMA Models p. 241 Building and Evaluating an ARIMA Model p. 244 Reasons to Choose and Cautions p. 252 Additional Methods p. 253 Summary p. 254 Exercises p. 254 Advanced Analytical Theory and Methods: Text Analysis p. 255 Text Analysis Steps p. 257 A Text Analysis Example p. 259 Collecting Raw Text p. 260 Representing Text p. 264 Term Frequency-Inverse Document Frequency (TFIDF) p. 269 Categorizing Documents by Topics p. 274 Determining Sentiments p. 277 Gaining Insights p. 283 Summary p. 290 Exercises p. 290 Bibliography p. 291 Advanced Analytics-Technology and Tools: MapReduce and Hadoop p. 295 Analytics for Unstructured Data p. 296 Use Cases p. 296 MapReduce p. 298 Apache Hadoop p. 300 The Hadoop Ecosystem p. 306 Pig p. 306 Hive p. 308

5 HBase p. 311 Mahout p. 319 NoSQL p. 322 Summary p. 323 Exercises p. 324 Bibliography p. 324 Advanced Analytics-Technology and Tools: In-Database Analytics p. 327 SQL Essentials p. 328 Joins p. 330 Set Operations p. 332 Grouping Extensions p. 334 In-Database Text Analysis p. 338 Advanced SQL p. 343 Window Functions p. 343 User-Defined Functions and Aggregates p. 347 Ordered Aggregates p. 351 MADlib p. 352 Summary p. 356 Exercises p. 356 Bibliography p. 357 The Endgame, or Putting It All Together p. 359 Communicating and Operationalizing an Analytics Project p. 360 Creating the Final Deliverables p. 362 Developing Core Material for Multiple Audiences p. 364 Project Goals p. 365 Main Findings p. 367 Approach p. 369 Model Description p. 371 Key Points Supported with Data p. 372 Model Details p. 372 Recommendations p. 374 Additional Tips on Final Presentation p. 375 Providing Technical Specifications and Code p. 376 Data Visualization Basics p. 377 Key Points Supported with Data p. 378 Evolution of a Graph p. 380 Common Representation Methods p. 386 How to Clean Up a Graphic p. 387 Additional Considerations p. 392 Summary p. 393 Exercises p. 394

6 References and Further Reading p. 394 Bibliography p. 394 Index p. 397 Table of Contents provided by Blackwell's Book Services and R.R. Bowker. Used with permission.

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