{"product_id":"analytics-in-a-big-data-world-isbn-9781118892701","title":"Analytics in a Big Data World","description":"\u003cb\u003eThe guide to targeting and leveraging business opportunities using big data \u0026amp; analytics\u003c\/b\u003e  \u003cp\u003eBy leveraging big data \u0026amp; analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. \u003ci\u003eAnalytics in a Big Data World\u003c\/i\u003e reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. Designed to be an accessible resource, this essential book does not include exhaustive coverage of all analytical techniques, instead focusing on analytics techniques that really provide added value in business environments.\u003c\/p\u003e \u003cp\u003eThe book draws on author Bart Baesens' expertise on the topics of big data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use data analytics to their advantage, but need a good starting point. Baesens has conducted extensive research on big data, analytics, customer relationship management, web analytics, fraud detection, and credit risk management, and uses this experience to bring clarity to a complex topic.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eIncludes numerous case studies on risk management, fraud detection, customer relationship management, and web analytics\u003c\/li\u003e \u003cli\u003eOffers the results of research and the author's personal experience in banking, retail, and government\u003c\/li\u003e \u003cli\u003eContains an overview of the visionary ideas and current developments on the strategic use of analytics for business\u003c\/li\u003e \u003cli\u003eCovers the topic of data analytics in easy-to-understand terms without an undo emphasis on mathematics and the minutiae of statistical analysis\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eFor organizations looking to enhance their capabilities via data analytics, this resource is the go-to reference for leveraging data to enhance business capabilities.\u003c\/p\u003e \u003cp\u003ePreface xiii\u003c\/p\u003e \u003cp\u003eAcknowledgments xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Big Data and Analytics 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExample Applications 2\u003c\/p\u003e \u003cp\u003eBasic Nomenclature 4\u003c\/p\u003e \u003cp\u003eAnalytics Process Model 4\u003c\/p\u003e \u003cp\u003eJob Profiles Involved 6\u003c\/p\u003e \u003cp\u003eAnalytics 7\u003c\/p\u003e \u003cp\u003eAnalytical Model Requirements 9\u003c\/p\u003e \u003cp\u003eNotes 10\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Data Collection, Sampling, and Preprocessing 13\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTypes of Data Sources 13\u003c\/p\u003e \u003cp\u003eSampling 15\u003c\/p\u003e \u003cp\u003eTypes of Data Elements 17\u003c\/p\u003e \u003cp\u003eVisual Data Exploration and Exploratory Statistical Analysis 17\u003c\/p\u003e \u003cp\u003eMissing Values 19\u003c\/p\u003e \u003cp\u003eOutlier Detection and Treatment 20\u003c\/p\u003e \u003cp\u003eStandardizing Data 24\u003c\/p\u003e \u003cp\u003eCategorization 24\u003c\/p\u003e \u003cp\u003eWeights of Evidence Coding 28\u003c\/p\u003e \u003cp\u003eVariable Selection 29\u003c\/p\u003e \u003cp\u003eSegmentation 32\u003c\/p\u003e \u003cp\u003eNotes 33\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Predictive Analytics 35\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTarget Definition 35\u003c\/p\u003e \u003cp\u003eLinear Regression 38\u003c\/p\u003e \u003cp\u003eLogistic Regression 39\u003c\/p\u003e \u003cp\u003eDecision Trees 42\u003c\/p\u003e \u003cp\u003eNeural Networks 48\u003c\/p\u003e \u003cp\u003eSupport Vector Machines 58\u003c\/p\u003e \u003cp\u003eEnsemble Methods 64\u003c\/p\u003e \u003cp\u003eMulticlass Classification Techniques 67\u003c\/p\u003e \u003cp\u003eEvaluating Predictive Models 71\u003c\/p\u003e \u003cp\u003eNotes 84\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Descriptive Analytics 87\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAssociation Rules 87\u003c\/p\u003e \u003cp\u003eSequence Rules 94\u003c\/p\u003e \u003cp\u003eSegmentation 95\u003c\/p\u003e \u003cp\u003eNotes 104\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Survival Analysis 105\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSurvival Analysis Measurements 106\u003c\/p\u003e \u003cp\u003eKaplan Meier Analysis 109\u003c\/p\u003e \u003cp\u003eParametric Survival Analysis 111\u003c\/p\u003e \u003cp\u003eProportional Hazards Regression 114\u003c\/p\u003e \u003cp\u003eExtensions of Survival Analysis Models 116\u003c\/p\u003e \u003cp\u003eEvaluating Survival Analysis Models 117\u003c\/p\u003e \u003cp\u003eNotes 117\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Social Network Analytics 119\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSocial Network Definitions 119\u003c\/p\u003e \u003cp\u003eSocial Network Metrics 121\u003c\/p\u003e \u003cp\u003eSocial Network Learning 123\u003c\/p\u003e \u003cp\u003eRelational Neighbor Classifier 124\u003c\/p\u003e \u003cp\u003eProbabilistic Relational Neighbor Classifier 125\u003c\/p\u003e \u003cp\u003eRelational Logistic Regression 126\u003c\/p\u003e \u003cp\u003eCollective Inferencing 128\u003c\/p\u003e \u003cp\u003eEgonets 129\u003c\/p\u003e \u003cp\u003eBigraphs 130\u003c\/p\u003e \u003cp\u003eNotes 132\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Analytics: Putting It All to Work 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBacktesting Analytical Models 134\u003c\/p\u003e \u003cp\u003eBenchmarking 146\u003c\/p\u003e \u003cp\u003eData Quality 149\u003c\/p\u003e \u003cp\u003eSoftware 153\u003c\/p\u003e \u003cp\u003ePrivacy 155\u003c\/p\u003e \u003cp\u003eModel Design and Documentation 158\u003c\/p\u003e \u003cp\u003eCorporate Governance 159\u003c\/p\u003e \u003cp\u003eNotes 159\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Example Applications 161\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCredit Risk Modeling 161\u003c\/p\u003e \u003cp\u003eFraud Detection 165\u003c\/p\u003e \u003cp\u003eNet Lift Response Modeling 168\u003c\/p\u003e \u003cp\u003eChurn Prediction 172\u003c\/p\u003e \u003cp\u003eRecommender Systems 176\u003c\/p\u003e \u003cp\u003eWeb Analytics 185\u003c\/p\u003e \u003cp\u003eSocial Media Analytics 195\u003c\/p\u003e \u003cp\u003eBusiness Process Analytics 204\u003c\/p\u003e \u003cp\u003eNotes 220\u003c\/p\u003e \u003cp\u003eAbout the Author 223\u003c\/p\u003e \u003cp\u003eIndex 225\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eBART BAESENS\u003c\/b\u003e is an associate professor at KU Leuven (Belgium) and a lecturer at the University of Southampton (United Kingdom), as well as an internationally known data analytics consultant. He is a foremost researcher in the areas of web analytics, customer relationship management, and fraud detection. His findings have been published in well-known international journals including \u003ci\u003eMachine Learning\u003c\/i\u003e and \u003ci\u003eManagement Science\u003c\/i\u003e. Baesens is also co-author of the book \u003ci\u003eCredit Risk Management: Basic Concepts\u003c\/i\u003e (Oxford University Press, 2008).   \u003c\/p\u003e\u003cp\u003eA few years ago, big data was little more than a buzzword. Today, it's a reality for every business, but only a few firms are taking advantage of the new world of information. The science of analytics is a way to get inside customers' minds and understand the complex behavioral dynamics that affect business. \u003ci\u003eAnalytics in a Big Data World\u003c\/i\u003e advances the discussion of big data by moving it out of the theoretical realm and into everyday business practice. \u003c\/p\u003e\u003cp\u003eIt has been said that data is the new oil—an abundant resource of great value. The difference between data and oil, as top analytics researcher Bart Baesens understands, is that \u003ci\u003eeveryone\u003c\/i\u003e has data. In areas like risk management, fraud detection, and customer relationship management, the potential gains afforded by big data analytics are well worth exploring. Reading \u003ci\u003eAnalytics in a Big Data\u003c\/i\u003e \u003ci\u003eWorld\u003c\/i\u003e is the first step in extracting the valuable information waiting in your databases. \u003c\/p\u003e\u003cp\u003eBy taking a practitioner's perspective, this book shows readers how to use the latest developments and new ideas in big data to build an analytics strategy with practical applications. The mathematics and theory have already been tested, so \u003ci\u003eAnalytics in a Big Data World\u003c\/i\u003e draws on case studies and action plans, rather than dwelling unnecessarily on technical details. This realistic focus makes the guide ideal for analytics professionals who want to learn the latest techniques for leveraging data to expand markets. \u003c\/p\u003e\u003cp\u003eThis latest addition to the Wiley and SAS Business Series is relevant to decisions that all businesses will need to make in the coming years. As the number of practical applications for data skyrockets, learning how to extract business value from big data becomes a competitive requirement. Bart Baesens has accomplished something significant with \u003ci\u003eAnalytics in a Big Data World\u003c\/i\u003e, which delivers an action-oriented guide to staying competitive using the latest analytical models.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988736229605,"sku":"NP9781118892701","price":52.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118892701.jpg?v=1761781387","url":"https:\/\/k12savings.com\/es\/products\/analytics-in-a-big-data-world-isbn-9781118892701","provider":"K12savings","version":"1.0","type":"link"}