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xvii, 302 p. : ill.
  • Foreword Thomas H. Davenport xiii Preface xv What is the occupational hazard of predictive analytics? Introduction: The Prediction Effect 1 Chapter 1: Liftoff! Prediction Takes Action (deployment) 17 Chapter 2: With Power Comes Responsibility: Hewlett-Packard, Target, and the Police Deduce Your Secrets (ethics) 37 Chapter 3: The Data Effect: A Glut at the End of the Rainbow(data) 67 Chapter 4: The Machine That Learns: A Look Inside Chase sPrediction of Mortgage Risk (modeling) 103 Chapter 5: The Ensemble Effect: Netflix, Crowdsourcing, andSupercharging Prediction (ensembles) 133 Chapter 6: Watson and the Jeopardy! Challenge (questionanswering) 151 Chapter 7: Persuasion by the Numbers: How Telenor, U.S. Bank, and the Obama Campaign Engineered Influence (uplift) 187 Afterword 218 Ten Predictions for the First Hour of 2020 Appendices A. Five Effects of Prediction 221 B. Twenty-One Applications of Predictive Analytics 222 C. Prediction People Cast of "Characters" 225 Notes 228 Acknowledgments 290 About the Author 292 Index 293.
  • (source: Nielsen Book Data)9781118356852 20160612
"The Freakonomics of big data." Stein Kretsinger, founding executive of; former lead analyst at Capital One This book is easily understood by all readers. Rather than a "how to" for hands-on techies, the book entices lay-readers and experts alike by covering new case studies and the latest state-of-the-art techniques. You have been predicted by companies, governments, law enforcement, hospitals, and universities. Their computers say, "I knew you were going to do that!" These institutions are seizing upon the power to predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime fighting, and boosts sales. How? Prediction is powered by the world's most potent, booming unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future lifting a bit of the fog off our hazy view of tomorrow means pay dirt. In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: * What type of mortgage risk Chase Bank predicted before the recession. * Predicting which people will drop out of school, cancel a subscription, or get divorced before they are even aware of it themselves. * Why early retirement decreases life expectancy and vegetarians miss fewer flights. * Five reasons why organizations predict death, including one health insurance company. * How U.S. Bank, European wireless carrier Telenor, and Obama's 2012 campaign calculated the way to most strongly influence each individual. * How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! * How companies ascertain untold, private truths how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. * How judges and parole boards rely on crime-predicting computers to decide who stays in prison and who goes free. * What's predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS,, MTV, Netflix, Pandora, PayPal, Pfizer, and Wikipedia. A truly omnipresent science, predictive analytics affects everyone, every day. Although largely unseen, it drives millions of decisions, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. Predictive analytics transcends human perception. This book's final chapter answers the riddle: What often happens to you that cannot be witnessed, and that you can't even be sure has happened afterward but that can be predicted in advance? Whether you are a consumer of it or consumed by it get a handle on the power of Predictive Analytics.
(source: Nielsen Book Data)9781118356852 20160612
Stanford University Libraries
1 online resource (1 v.) : ill.
Hive makes life much easier for developers who work with stored and managed data in Hadoop clusters, such as data warehouses. With this example-driven guide, you'll learn how to use the Hive infrastructure to provide data summarization, query, and analysis - particularly with HiveQL, the query language dialect of SQL. You'll learn how to set up Hive in your environment and optimize its use, and how it interoperates with other tools, such as HBase. You'll also learn how to extend Hive with custom code written in Java or scripting languages. Ideal for developers with prior SQL experience, this book shows you how Hive simplifies many tasks that would be much harder to implement in the lower-level MapReduce API provided by Hadoop.
(source: Nielsen Book Data)9781449319335 20160609
Stanford University Libraries
1 online resource (xxiv, 404 p.) : ill.
  • Foreword xvii Preface to the second edition xix Preface to the first edition xxi Acknowledgments xxiii Part I PRELIMINARIES Chapter 1 Introduction 3 1.1 What Is Data Mining? 3 1.2 Where Is Data Mining Used? 4 1.3 Origins of Data Mining 4 1.4 Rapid Growth of Data Mining 5 1.5 Why Are There So Many Different Methods? 6 1.6 Terminology and Notation 7 1.7 Road Maps to This Book 9 Chapter 2 Overview of the Data Mining Process 12 2.1 Introduction 12 2.2 Core Ideas in Data Mining 13 2.3 Supervised and Unsupervised Learning 15 2.4 Steps in Data Mining 15 2.5 Preliminary Steps 17 2.6 Building a Model: Example with Linear Regression 27 2.7 Using Excel for Data Mining 34 Part II DATA EXPLORATION AND DIMENSION REDUCTION Chapter 3 Data Visualization 43 3.1 Uses of Data Visualization 43 3.2 Data Examples 45 3.3 Basic Charts: Bar Charts, Line Graphs, and Scatterplots 45 3.4 Multidimensional Visualization 52 3.5 Specialized Visualizations 63 3.6 Summary ofMajor Visualizations and Operations, According to Data Mining Goal 67 Chapter 4 Dimension Reduction 71 4.1 Introduction 71 4.2 Practical Considerations 72 4.3 Data Summaries 73 4.4 Correlation Analysis . 76 4.5 Reducing the Number of Categories in Categorical Variables 76 4.6 Converting a Categorical Variable to a Numerical Variable 78 4.7 Principal Components Analysis 78 4.8 Dimension Reduction Using Regression Models 87 4.9 Dimension Reduction Using Classification and Regression Trees 88 Part III PERFORMANCE EVALUATION Chapter 5 Evaluating Classification and Predictive Performance 93 5.1 Introduction 93 5.2 Judging Classification Performance 94 5.3 Evaluating Predictive Performance 115 Part IV PREDICTION AND CLASSIFICATION METHODS Chapter 6 Multiple Linear Regression 121 6.1 Introduction 121 6.2 Explanatory versus Predictive Modeling 122 6.3 Estimating the Regression Equation and Prediction 123 6.4 Variable Selection in Linear Regression 127 Chapter 7 k-Nearest Neighbors (k-NN) 137 7.1 k-NN Classifier (Categorical Outcome) 137 7.2 k-NN for a Numerical Response 142 7.3 Advantages and Shortcomings of k-NN Algorithms 144 Chapter 8 Naive Bayes 148 8.1 Introduction 148 8.2 Applying the Full (Exact) Bayesian Classifier 150 8.3 Advantages and Shortcomings of the Naive Bayes Classifier 159 Chapter 9 Classification and Regression Trees 164 9.1 Introduction 164 9.2 Classification Trees 166 9.3 Measures of Impurity 169 9.4 Evaluating the Performance of a Classification Tree 173 9.5 Avoiding Overfitting 179 9.6 Classification Rules from Trees 183 9.7 Classification Trees for More Than Two Classes 185 9.8 RegressionTrees 185 9.9 Advantages, Weaknesses, and Extensions 187 Chapter 10 Logistic Regression 192 10.1 Introduction 192 10.2 Logistic Regression Model 194 10.3 Evaluating Classification Performance 202 10.4 Example of Complete Analysis: Predicting Delayed Flights 206 10.5 Appendix: Logistic Regression for Profiling 211 Chapter 11 Neural Nets 222 11.1 Introduction 222 11.2 Concept and Structure of a Neural Network 223 11.3 Fitting a Network to Data 223 11.4 Required User Input 237 11.5 Exploring the Relationship Between Predictors andResponse 239 11.6 Advantages and Weaknesses of Neural Networks 239 Chapter 12 Discriminant Analysis 243 12.1 Introduction 243 12.2 Distance of an Observation from a Class 246 12.3 Fisher's Linear Classification Functions 247 12.4 Classification Performance of Discriminant Analysis 251 12.5 Prior Probabilities 252 12.6 Unequal Misclassification Costs 252 12.7 Classifying More Than Two Classes 253 12.8 Advantages and Weaknesses 254 Part V MINING RELATIONSHIPS AMONG RECORDS Chapter 13 Association Rules 263 13.1 Introduction 263 13.2 Discovering Association Rules in Transaction Databases 263 13.3 Generating Candidate Rules 265 13.4 Selecting Strong Rules 267 13.5 Summary 275 Chapter 14 Cluster Analysis 279 14.1 Introduction 279 14.2 Measuring Distance Between Two Records 283 14.3 Measuring Distance Between Two Clusters 287 14.4 Hierarchical (Agglomerative) Clustering 290 14.5 Nonhierarchical Clustering: The k-Means Algorithm 295 Part VI FORECASTING TIME SERIES Chapter 15 Handling Time Series 305 15.1 Introduction 305 15.2 Explanatory versus Predictive Modeling 306 15.3 Popular Forecasting Methods in Business 307 15.4 Time Series Components 308 15.5 Data Partitioning 312 Chapter 16 Regression-Based Forecasting 317 16.1 Model with Trend 317 16.2 Model with Seasonality 322 16.3 Model with Trend and Seasonality 324 16.4 Autocorrelation and ARIMA Models 324 Chapter 17 Smoothing Methods 344 17.1 Introduction 344 17.2 MovingAverage 345 17.3 Simple Exponential Smoothing 350 17.4 Advanced Exponential Smoothing 353 Part VII CASES Chapter 18 Cases 367 18.1 Charles Book Club 367 18.2 German Credit 375 18.3 Tayko Software Cataloger 379 18.4 Segmenting Consumers of Bath Soap 383 18.5 Direct-MailFundraising 387 18.6 Catalog Cross Selling 389 18.7 Predicting Bankruptcy 390 18.8 Time Series Case: Forecasting Public Transportation Demand 393 References 397 Index 399.
  • (source: Nielsen Book Data)9780470526828 20160605
Praise for the First Edition " full of vivid and thought-provoking anecdotes needs to be read by anyone with a serious interest in research and marketing." -- Research magazine "Shmueli et al. have done a wonderful job in presenting the field of data mining a welcome addition to the literature." -- Incorporating a new focus on data visualization and time series forecasting, Data Mining for Business Intelligence , Second Edition continues to supply insightful, detailed guidance on fundamental data mining techniques. This new edition guides readers through the use of the Microsoft Office Excel add-in XLMiner for developing predictive models and techniques for describing and finding patterns in data. From clustering customers into market segments and finding the characteristics of frequent flyers to learning what items are purchased with other items, the authors use interesting, real-world examples to build a theoretical and practical understanding of key data mining methods, including classification, prediction, and affinity analysis as well as data reduction, exploration, and visualization. The Second Edition now features: Three new chapters on time series forecasting, introducing popular business forecasting methods including moving average, exponential smoothing methods; regression-based models; and topics such as explanatory vs. predictive modeling, two-level models, and ensembles A revised chapter on data visualization that now features interactive visualization principles and added assignments that demonstrate interactive visualization in practice Separate chapters that each treat k-nearest neighbors and Naive Bayes methods Summaries at the start of each chapter that supply an outline of key topics The book includes access to XLMiner, allowing readers to work hands-on with the provided data. Throughout the book, applications of the discussed topics focus on the business problem as motivation and avoid unnecessary statistical theory. Each chapter concludes with exercises that allow readers to assess their comprehension of the presented material. The final chapter includes a set of cases that require use of the different data mining techniques, and a related Web site features data sets, exercise solutions, PowerPoint slides, and case solutions. Data Mining for Business Intelligence , Second Edition is an excellent book for courses on data mining, forecasting, and decision support systems at the upper-undergraduate and graduate levels. It is also a one-of-a-kind resource for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology.
(source: Nielsen Book Data)9780470526828 20160605
Stanford University Libraries
xv, 218 p. : ill. ; 24 cm.
  • Introduction.- Getting data into R.- Accessing variables and managing subsets of data.- Simple commands.- An introduction to basic plotting tools.- Loops and functions.- Graphing tools.- An introduction to lattice package.- Common R mistakes.
  • (source: Nielsen Book Data)9780387938363 20160528
Based on their extensive experience with teaching R and statistics to applied scientists, the authors provide a beginner's guide to R. To avoid the difficulty of teaching R and statistics at the same time, statistical methods are kept to a minimum. The text covers how to download and install R, import and manage data, elementary plotting, an introduction to functions, advanced plotting, and common beginner mistakes. This book contains everything you need to know to get started with R. 'Its biggest advantage is that it aims only to teach R...It organizes R commands very efficiently, with much teaching guidance included. I would describe this book as being handy - it's the kind of book that you want to keep in your jacket pocket or backpack all the time, ready for use, like a Swiss Army knife' - Loveday Conquest, University of Washington. 'Whilst several books focus on learning statistics in R..., the authors of this book fill a gap in the market by focusing on learning R whilst almost completely avoiding any statistical jargon...The fact that the authors have very extensive experience of teaching R to absolute beginners shines throughout' - Mark Mainwaring, Lancaster University. 'Exactly what is needed...This is great, nice work. I love the ecological/biological examples; they will be an enormous help' - Andrew J. Tyne, University of Nebraska-Lincoln.
(source: Nielsen Book Data)9780387938363 20160528 SpringerLink
Stanford University Libraries
1 v. (unpaged) : ill.
  • A little background
  • Creating and populating a database
  • Query primer
  • Filtering
  • Querying multiple tables
  • Working with sets
  • Data generation, conversion, manipulation
  • Grouping and aggregates
  • Subqueries
  • Joins revisted Conditional logic
  • Transactions
  • Indexes and constraints
  • Views
  • Metadata.
Updated for the latest database management systems - including MySQL 6.0, Oracle 11g, and Microsoft's SQL Server 2008 - this introductory guide will get you up and running with SQL quickly. Whether you need to write database applications, perform administrative tasks, or generate reports, "Learning SQL, Second Edition", will help you easily master all the SQL fundamentals. Each chapter presents a self-contained lesson on a key SQL concept or technique, with numerous illustrations and annotated examples. Exercises at the end of each chapter let you practice the skills you learn. With this book, you will: move quickly through SQL basics and learn several advanced features; use SQL data statements to generate, manipulate, and retrieve data; create database objects, such as tables, indexes, and constraints, using SQL schema statements; learn how data sets interact with queries, and understand the importance of subqueries; and, convert and manipulate data with SQL's built-in functions, and use conditional logic in data statements Knowledge of SQL is a must for interacting with data. With "Learning SQL", you'll quickly learn how to put the power and flexibility of this language to work.
(source: Nielsen Book Data)9780596520830 20160528
Stanford University Libraries