{"product_id":"big-data-data-mining-and-machine-learning-isbn-9781118618042","title":"Big Data, Data Mining, and Machine Learning","description":"\u003cb\u003eWith big data analytics comes big insights into profitability\u003c\/b\u003e  \u003cp\u003eBig data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. \u003ci\u003eBig Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners\u003c\/i\u003e is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency.\u003c\/p\u003e \u003cp\u003eWith continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eA complete overview of big data and its notable characteristics\u003c\/li\u003e \u003cli\u003eDetails on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databases\u003c\/li\u003e \u003cli\u003eComprehensive coverage of data mining, text analytics, and machine learning algorithms\u003c\/li\u003e \u003cli\u003eA discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eBig Data, Data Mining, and Machine Learning\u003c\/i\u003e provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole.\u003c\/p\u003e \u003cp\u003eForward xiii\u003c\/p\u003e \u003cp\u003ePreface xv\u003c\/p\u003e \u003cp\u003eAcknowledgments xix\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIntroduction 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBig Data Timeline 5\u003c\/p\u003e \u003cp\u003eWhy This Topic is Relevant Now 8\u003c\/p\u003e \u003cp\u003eIs Big Data a Fad? 9\u003c\/p\u003e \u003cp\u003eWhere Using Big Data Makes a Big Difference 12\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart One The Computing Environment 23\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Hardware 27\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStorage (Disk) 27\u003c\/p\u003e \u003cp\u003eCentral Processing Unit 29\u003c\/p\u003e \u003cp\u003eMemory 31\u003c\/p\u003e \u003cp\u003eNetwork 33\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Distributed Systems 35\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDatabase Computing 36\u003c\/p\u003e \u003cp\u003eFile System Computing 37\u003c\/p\u003e \u003cp\u003eConsiderations 39\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Analytical Tools 43\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWeka 43\u003c\/p\u003e \u003cp\u003eJava and JVM Languages 44\u003c\/p\u003e \u003cp\u003eR 47\u003c\/p\u003e \u003cp\u003ePython 49\u003c\/p\u003e \u003cp\u003eSAS 50\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Two Turning Data into Business Value 53\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 Predictive Modeling 55\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Methodology for Building Models 58\u003c\/p\u003e \u003cp\u003esEMMA 61\u003c\/p\u003e \u003cp\u003eBinary Classifi cation 64\u003c\/p\u003e \u003cp\u003eMultilevel Classifi cation 66\u003c\/p\u003e \u003cp\u003eInterval Prediction 66\u003c\/p\u003e \u003cp\u003eAssessment of Predictive Models 67\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Common Predictive Modeling Techniques 71\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eRFM 72\u003c\/p\u003e \u003cp\u003eRegression 75\u003c\/p\u003e \u003cp\u003eGeneralized Linear Models 84\u003c\/p\u003e \u003cp\u003eNeural Networks 90\u003c\/p\u003e \u003cp\u003eDecision and Regression Trees 101\u003c\/p\u003e \u003cp\u003eSupport Vector Machines 107\u003c\/p\u003e \u003cp\u003eBayesian Methods Network Classifi cation 113\u003c\/p\u003e \u003cp\u003eEnsemble Methods 124\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Segmentation 127\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCluster Analysis 132\u003c\/p\u003e \u003cp\u003eDistance Measures (Metrics) 133\u003c\/p\u003e \u003cp\u003eEvaluating Clustering 134\u003c\/p\u003e \u003cp\u003eNumber of Clusters 135\u003c\/p\u003e \u003cp\u003e\u003ci\u003eK\u003c\/i\u003e‐means Algorithm 137\u003c\/p\u003e \u003cp\u003eHierarchical Clustering 138\u003c\/p\u003e \u003cp\u003eProfi ling Clusters 138\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Incremental Response Modeling 141\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBuilding the Response Model 142\u003c\/p\u003e \u003cp\u003eMeasuring the Incremental Response 143\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Time Series Data Mining 149\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReducing Dimensionality 150\u003c\/p\u003e \u003cp\u003eDetecting Patterns 151\u003c\/p\u003e \u003cp\u003eTime Series Data Mining in Action: Nike+ FuelBand 154\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Recommendation Systems 163\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eWhat Are Recommendation Systems? 163\u003c\/p\u003e \u003cp\u003eWhere Are They Used? 164\u003c\/p\u003e \u003cp\u003eHow Do They Work? 165\u003c\/p\u003e \u003cp\u003eAssessing Recommendation Quality 170\u003c\/p\u003e \u003cp\u003eRecommendations in Action: SAS Library 171\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Text Analytics 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eInformation Retrieval 176\u003c\/p\u003e \u003cp\u003eContent Categorization 177\u003c\/p\u003e \u003cp\u003eText Mining 178\u003c\/p\u003e \u003cp\u003eText Analytics in Action: Let’s Play \u003ci\u003eJeopardy! \u003c\/i\u003e180\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart Three Success Stories of Putting It All Together 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Case Study of a Large U.S.‐Based Financial Services Company 197\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eTraditional Marketing Campaign Process 198\u003c\/p\u003e \u003cp\u003eHigh‐Performance Marketing Solution 202\u003c\/p\u003e \u003cp\u003eValue Proposition for Change 203\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Case Study of a Major Health Care Provider 205\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eCAHPS 207\u003c\/p\u003e \u003cp\u003eHEDIS 207\u003c\/p\u003e \u003cp\u003eHOS 208\u003c\/p\u003e \u003cp\u003eIRE 208\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Case Study of a Technology Manufacturer 215\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFinding Defective Devices 215\u003c\/p\u003e \u003cp\u003eHow They Reduced Cost 216\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 Case Study of Online Brand Management 221\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 Case Study of Mobile Application Recommendations 225\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16 Case Study of a High‐Tech Product Manufacturer 229\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eHandling the Missing Data 230\u003c\/p\u003e \u003cp\u003eApplication beyond Manufacturing 231\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17 Looking to the Future 233\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReproducible Research 234\u003c\/p\u003e \u003cp\u003ePrivacy with Public Data Sets 234\u003c\/p\u003e \u003cp\u003eThe Internet of Things 236\u003c\/p\u003e \u003cp\u003eSoftware Development in the Future 237\u003c\/p\u003e \u003cp\u003eFuture Development of Algorithms 238\u003c\/p\u003e \u003cp\u003eIn Conclusion 241\u003c\/p\u003e \u003cp\u003eAbout the Author 243\u003c\/p\u003e \u003cp\u003eAppendix 245\u003c\/p\u003e \u003cp\u003eReferences 247\u003c\/p\u003e \u003cp\u003eIndex 253\u003c\/p\u003e  \u003cp\u003e\"...explains what it covers very well...\" (\u003cem\u003eZDNet,\u003c\/em\u003e September 2014)   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eJARED DEAN\u003c\/b\u003e is a Senior Director of Research and Development at SAS Institute. He is responsible for the development of SAS's worldwide data mining solutions. This includes customer engagements, new feature development, technical support, sales support, and product integration. Prior to joining SAS, Dean worked as a Mathematical Statistician for the US Census Bureau.   \u003c\/p\u003e\u003cp\u003eIn today’s business environment an endless stream of big data often shapes critical decision-making processes. To maintain and sustain a profitable business, it is imperative to harness the power of big data. However, simply accessing the data and having the ability to process it isn’t enough to yield meaningful results.\u003c\/p\u003e \u003cp\u003e\u003ci\u003eBig Data, Data Mining, and Machine Learning\u003c\/i\u003e offers marketing executives, business leaders, and technology experts a comprehensive resource for developing and implementing the strategies and methods that can consistently produce effective results and ultimately increase profitability. In this book, Jared Dean offers an accessible and thorough review of the current state of big data analytics and the growing trend toward high performance computing architectures. \u003ci\u003eBig Data, Data Mining, and Machine Learning\u003c\/i\u003e clearly shows how big data analytics can be leveraged to foster positive change and drive efficiency.\u003c\/p\u003e \u003cp\u003eStep by step, Jared Dean reveals what it takes to use technology to create an analytical environment for data mining, machine learning, and working with big data. The author also explores the trade-offs that result from certain technology choices. \u003ci\u003eBig Data, Data Mining, and Machine Learning\u003c\/i\u003e includes a range of algorithms and methods that can be implemented to glean information from mined data and provides explanations on how to apply these approaches most effectively. Filled with illustrative case studies, the book offers myriad examples of successful organizations that have used new technological advances and algorithms to their competitive advantage. The author also includes a discussion of explanatory and predictive modeling and how these tools can be applied to the decision-making process.\u003c\/p\u003e \u003cp\u003eFor any organization that wants to access the power of data analytics, this important book can serve as a linchpin for understanding the underlying technology and analysis of big data. Now you can take control of your organization’s big data analytics with confidence and create results that go directly to the bottom line.\u003c\/p\u003e  \u003cp\u003eIn today's business environment an endless stream of big data often shapes critical decision-making processes. To maintain and sustain a profitable business, it is imperative to harness the power of big data. However, simply accessing the data and having the ability to process it isn't enough to yield meaningful results.  \u003c\/p\u003e\u003cp\u003e\u003ci\u003eBig Data, Data Mining, and Machine Learning\u003c\/i\u003e offers marketing executives, business leaders, and technology experts a comprehensive resource for developing and implementing the strategies and methods that can consistently produce effective results and ultimately increase profitability. In this book, Jared Dean offers an accessible and thorough review of the current state of big data analytics and the growing trend toward high performance computing architectures. \u003ci\u003eBig Data, Data Mining, and Machine Learning\u003c\/i\u003e clearly shows how big data analytics can be leveraged to foster positive change and drive efficiency. \u003c\/p\u003e\u003cp\u003e \u003c\/p\u003e\u003cp\u003eStep by step, Jared Dean reveals what it takes to use technology to create an analytical environment for data mining, machine learning, and working with big data. The author also explores the trade-offs that result from certain technology choices. \u003ci\u003eBig Data, Data Mining, and Machine Learning\u003c\/i\u003e includes a range of algorithms and methods that can be implemented to glean information from mined data and provides explanations on how to apply these approaches most effectively. Filled with illustrative case studies, the book offers myriad examples of successful organizations that have used new technological advances and algorithms to their competitive advantage. The author also includes a discussion of explanatory and predictive modeling and how these tools can be applied to the decision-making process.  \u003c\/p\u003e\u003cp\u003eFor any organization that wants to access the power of data analytics, this important book can serve as a linchpin for understanding the underlying technology and analysis of big data. Now you can take control of your organization's big data analytics with confidence and create results that go directly to the bottom line.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988810121445,"sku":"NP9781118618042","price":63.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118618042.jpg?v=1761781678","url":"https:\/\/k12savings.com\/es\/products\/big-data-data-mining-and-machine-learning-isbn-9781118618042","provider":"K12savings","version":"1.0","type":"link"}