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Data Is Everybody's Business: The Fundamentals of Data Monetization (Management on the Cutting Edge)
by Cynthia M. Beath Barbara H. Wixom Leslie OwensA clear, engaging, evidence-based guide to monetizing data, for everyone from employee to board member.Most organizations view data monetization—converting data into money—too narrowly: as merely selling data sets. But data monetization is a core business activity for both commercial and noncommercial organizations, and, within organizations, it&’s critical to have wide-ranging support for this pursuit. In Data Is Everybody&’s Business, the authors offer a clear and engaging way for people across the entire organization to understand data monetization and make it happen. The authors identify three viable ways to convert data into money—improving work with data, wrapping products with data, and selling information offerings—and explain when to pursue each and how to succeed. Key features of the book:• Grounded in twenty-eight years of academic research, including nine years of research at the MIT Sloan Center for Information Systems Research (MIT CISR)• Definitions of key terms, self-reflection questions, appealing graphics, and easy-to-use frameworks• Rich with detailed case studies• Supplemented by free MIT CISR website resources (cisr.mit.edu)Ideal for organizations engaged in data literacy training, data-driven transformation, or digital transformation, Data Is Everybody&’s Business is the essential guide for helping everybody in the organization—not just the data specialists—understand, get excited about, and participate in data monetization.
Data Lake Development with Big Data
by Pradeep Pasupuleti Beulah Salome PurraExplore architectural approaches to building Data Lakes that ingest, index, manage, and analyze massive amounts of data using Big Data technologies About This Book * Comprehend the intricacies of architecting a Data Lake and build a data strategy around your current data architecture * Efficiently manage vast amounts of data and deliver it to multiple applications and systems with a high degree of performance and scalability * Packed with industry best practices and use-case scenarios to get you up-and-running Who This Book Is For This book is for architects and senior managers who are responsible for building a strategy around their current data architecture, helping them identify the need for a Data Lake implementation in an enterprise context. The reader will need a good knowledge of master data management, information lifecycle management, data governance, data product design, data engineering, and systems architecture. Also required is experience of Big Data technologies such as Hadoop, Spark, Splunk, and Storm. What You Will Learn * Identify the need for a Data Lake in your enterprise context and learn to architect a Data Lake * Learn to build various tiers of a Data Lake, such as data intake, management, consumption, and governance, with a focus on practical implementation scenarios * Find out the key considerations to be taken into account while building each tier of the Data Lake * Understand Hadoop-oriented data transfer mechanism to ingest data in batch, micro-batch, and real-time modes * Explore various data integration needs and learn how to perform data enrichment and data transformations using Big Data technologies * Enable data discovery on the Data Lake to allow users to discover the data * Discover how data is packaged and provisioned for consumption * Comprehend the importance of including data governance disciplines while building a Data Lake In Detail A Data Lake is a highly scalable platform for storing huge volumes of multistructured data from disparate sources with centralized data management services. It eliminates the need for up-front modeling and rigid data structures by allowing schema-less writes. Data Lakes make it possible to ask complex far-reaching questions to find out hidden data patterns and relationships. This book explores the potential of Data Lakes and explores architectural approaches to building data lakes that ingest, index, manage, and analyze massive amounts of data using batch and real-time processing frameworks. It guides you on how to go about building a Data Lake that is managed by Hadoop and accessed as required by other Big Data applications such as Spark, Storm, Hive, and so on, to create an environment in which data from different sources can be meaningfully brought together and analyzed. Data Lakes can be viewed as having three capabilities--intake, management, and consumption. This book will take readers through each of these processes of developing a Data Lake and guide them (using best practices) in developing these capabilities. It will also explore often ignored, yet crucial considerations while building Data Lakes, with the focus on how to architect data governance, security, data quality, data lineage tracking, metadata management, and semantic data tagging. By the end of this book, you will have a good understanding of building a Data Lake for Big Data. You will be able to utilize Data Lakes for efficient and easy data processing and analytics. Style and approach Data Lake Development with Big Data provides architectural approaches to building a Data Lake. It follows a use case-based approach where practical implementation scenarios of each key component are explained. It also helps you understand how these use cases are implemented in a Data Lake. The chapters are organized in a way that mimics the sequential data flow evidenced in a Data Lake.
Data Leadership for Everyone: How You Can Harness the True Power of Data at Work
by Anthony AlgminA revolutionary approach to bringing data and business togetherData is lazy. It sits in files or databases, minding its own business but not accomplishing very much. Data is like someone in their mid-twenties, living with their parents, who won't get off the couch and make something of their life. Data is also the closest thing we have to truth in our organizations—but most business leaders today struggle using data to make an impact on what really matters: the success of their businesses. Data Leadership for Everyone is a game-changing book for anyone who believes in the power of data and is ready to create revolutionary change in their organization. Whether you're a C-suite executive, a manager, or an individual contributor, this book will propel your career by unlocking the mystery of how raw data transforms into real outcomes. In this book, data leadership advocate and transformation coach Anthony J. Algmin reveals his five-step Data Leadership Framework, breaking down the complexity of data systems and empowering you to:Access and prepare data for useRefine data to maximize its potentialUse data to find new insightsImpact business success with data valueGovern and scale data-driven outcomes Data is the key to the future success of all businesses, and anyone not making the most of data will lose, while those who can use data to drive business value will win. It's not enough to learn about data—business success requires a special leadership approach to connect data to the people, processes, and technologies unique to your organization. With over 150 specific takeaways, Data Leadership for Everyone is a must-have business leadership book to help you become a better data leader for the twenty-first century and beyond.
Data Management Technologies and Applications: Third International Conference, DATA 2014, Vienna, Austria, August 29-31, 2014, Revised Selected papers (Communications in Computer and Information Science #178)
by Andreas Holzinger Markus Helfert Orlando Belo Chiara FrancalanciThis book constitutes the thoroughly refereed proceedings of the Fourth International Conference on Data Technologies and Applications, DATA 2015, held in Colmar, France, in July 2015. The 9 revised full papers were carefully reviewed and selected from 70 submissions. The papers deal with the following topics: databases, data warehousing, data mining, data management, data security, knowledge and information systems and technologies; advanced application of data.
Data Management and Analysis: Case Studies in Education, Healthcare and Beyond (Studies in Big Data #65)
by Reda Alhajj Mohammad Moshirpour Behrouz FarData management and analysis is one of the fastest growing and most challenging areas of research and development in both academia and industry. Numerous types of applications and services have been studied and re-examined in this field resulting in this edited volume which includes chapters on effective approaches for dealing with the inherent complexity within data management and analysis. This edited volume contains practical case studies, and will appeal to students, researchers and professionals working in data management and analysis in the business, education, healthcare, and bioinformatics areas.
Data Management: Der Weg zum datengetriebenen Unternehmen
by Klaus-Dieter GronwaldDieses Lehrbuch betrachtet Data Management als interdisziplinäres Konzept mit Fokus auf den Zielen datengetriebener Unternehmen. Im Zentrum steht die interaktive Entwicklung eines Unternehmensdatenmodells für ein virtuelles Unternehmen mit Unterstützung eines online Learning Games unter Einbeziehung der Aufgaben, Ziele und Grundsätze des Data Managements, typischer Data-Management-Komponenten und Frameworks wie Datenmodellierung und Design, Metadaten Management, Data Architecture, und Data Governance, und verknüpft diese mit datengetriebenen Anwendungen wie Business Warehousing, Big Data, In-Memory Data Management, und Machine Learning im Data Management Kontext.Das Buch dient als Lehrbuch für Studierende der Informatik, der Wirtschaft und der Wirtschaftsinformatik an Universitäten, Hochschulen und Fachschulen und zur industriellen Aus- und Weiterbildung.
Data Migration Management for SAP S/4HANA: A Practical Guide
by Aleksei ArziaevEnhance your data transfer and storage skills with this comprehensive step-by-step guide to managing data migration for new on-premises SAP S/4HANA implementations. This book is tailored towards small to large projects, with a focus on the managerial aspects of the data migration process rather than the technical details. You’ll follow a project-led approach, enriched with a practical case study, and a comprehensive methodology for data migration planning and documentation. Athen traverse through a detailed plan on managing and documenting data migration throughout the project lifecycle. This book utilizes the general SAP Activate methodology for on-premises solutions as its foundational framework, enhancing it with specific strategies for data migration. Structured in alignment with the project phases of the SAP Activate methodology, Data Migration Management for SAP S/4HANA methodically covers planning, organizing, and controlling the data migration process. It serves as an essential guide for professionals tasked with implementing SAP S/4HANA in their business, ensuring a thorough understanding of each data migration phase on the project. What You'll Learn Significantly decrease the time needed for both the preparation and execution of data migration activities. Foster clear transparency in data migration processes for all stakeholders, including the customer and the project team. Facilitate a seamless and timely data migration process. Establish a benchmark for data migration management in future projects. Address and remedy any deficiencies in the SAP Activate methodology pertaining to data migration. Who This Book Is For SAP projects and data migration workstreams leads, already well-versed in SAP Activate methodology and possessing moderate experience in project and workstream management, who are seeking to enhance their skills in professionally managing data migration in implementation projects.
Data Mining Methods and Applications
by Kenneth D. Lawrence Ronald K. Klimberg Stephan KudybaWith today's information explosion, many organizations are now able to access a wealth of valuable data. Unfortunately, most of these organizations find they are ill-equipped to organize this information, let alone put it to work for them. Gain a Competitive Advantage Employ data mining in research and forecasting Build models with data management
Data Mining Mobile Devices
by Jesus MenaWith today's consumers spending more time on their mobiles than on their PCs, new methods of empirical stochastic modeling have emerged that can provide marketers with detailed information about the products, content, and services their customers desire.Data Mining Mobile Devices defines the collection of machine-sensed environmental data pertainin
Data Mining and Analytics in Healthcare Management: Applications and Tools (International Series in Operations Research & Management Science #341)
by David L. Olson Özgür M. ArazThis book presents data mining methods in the field of healthcare management in a practical way. Healthcare quality and disease prevention are essential in today’s world. Healthcare management faces a number of challenges, e.g. reducing patient growth through disease prevention, stopping or slowing disease progression, and reducing healthcare costs while improving quality of care. The book provides an overview of current healthcare management problems and highlights how analytics and knowledge management have been used to better cope with them. It then demonstrates how to use descriptive and predictive analytics tools to help address these challenges. In closing, it presents applications of software solutions in the context of healthcare management. Given its scope, the book will appeal to a broad readership, from researchers and students in the operations research and management field to practitioners such as data analysts and decision-makers who work in the healthcare sector.
Data Mining and Business Analytics with R
by Johannes LedolterCollecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification.Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents:* A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools* Illustrations of how to use the outlined concepts in real-world situations* Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials* Numerous exercises to help readers with computing skills and deepen their understanding of the materialData Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.
Data Mining and Exploration: From Traditional Statistics to Modern Data Science
by Chong Ho Alex YuThis book introduces both conceptual and procedural aspects of cutting-edge data science methods, such as dynamic data visualization, artificial neural networks, ensemble methods, and text mining. There are at least two unique elements that can set the book apart from its rivals. First, most students in social sciences, engineering, and business took at least one class in introductory statistics before learning data science. However, usually these courses do not discuss the similarities and differences between traditional statistics and modern data science; as a result learners are disoriented by this seemingly drastic paradigm shift. In reaction, some traditionalists reject data science altogether while some beginning data analysts employ data mining tools as a “black box”, without a comprehensive view of the foundational differences between traditional and modern methods (e.g., dichotomous thinking vs. pattern recognition, confirmation vs. exploration, single method vs. triangulation, single sample vs. cross-validation etc.). This book delineates the transition between classical methods and data science (e.g. from p value to Log Worth, from resampling to ensemble methods, from content analysis to text mining etc.). Second, this book aims to widen the learner's horizon by covering a plethora of software tools. When a technician has a hammer, every problem seems to be a nail. By the same token, many textbooks focus on a single software package only, and consequently the learner tends to fit the problem with the tool, but not the other way around. To rectify the situation, a competent analyst should be equipped with a tool set, rather than a single tool. For example, when the analyst works with crucial data in a highly regulated industry, such as pharmaceutical and banking, commercial software modules (e.g., SAS) are indispensable. For a mid-size and small company, open-source packages such as Python would come in handy. If the research goal is to create an executive summary quickly, the logical choice is rapid model comparison. If the analyst would like to explore the data by asking what-if questions, then dynamic graphing in JMP Pro is a better option. This book uses concrete examples to explain the pros and cons of various software applications.
Data Mining and Market Intelligence for Optimal Marketing Returns
by Domingo Tavella Susan ChiuThe authors present a practical and highly informative perspective on the elements that are crucial to the success of a marketing campaign. Unlike books that are either too theoretical to be of practical use to practitioners, or too soft to serve as solid and measurable implementation guidelines, this book focuses on the integration of established quantitative techniques into real life case studies that are immediately relevant to marketing practitioners.
Data Mining for Business Analytics: Concepts, Techniques, And Applications In Jmp
by Galit Shmueli Peter C. Bruce Nitin R. Patel Mia L. StephensData Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® presents an applied and interactive approach to data mining. Featuring hands-on applications with JMP Pro®, a statistical package from the SAS Institute, the bookuses engaging, real-world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® also includes: Detailed summaries that supply an outline of key topics at the beginning of each chapter End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material Data-rich case studies to illustrate various applications of data mining techniques A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics. The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field. Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University's Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks, and book chapters, including Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition, also published by Wiley. Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective and co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner ®, Third Edition, both published by Wiley. Mia Stephens is Academic Ambassador at JMP®, a division of SAS Institute. Prior to joining SAS, she was an adjunct professor of statistics at the University of New Hampshire and a founding member of the North Haven Group LLC, a statistical training and consulting company. She is the co-author of three other books, including Visual Six Sigma: Making Data Analysis Lean, Second Edition, also published by Wiley. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years. He is co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition, also published by Wiley.
Data Mining for Business Analytics: Concepts, Techniques, And Applications In R
by Galit Shmueli Peter C. Bruce Nitin R. Patel Inbal Yahav Kenneth C. Lichtendahl Jr.Data Mining for Business Analytics: Concepts, Techniques, and Applications in R presents an applied approach to data mining concepts and methods, using R software for illustration Readers will learn how to implement a variety of popular data mining algorithms in R (a free and open-source software) to tackle business problems and opportunities. This is the fifth version of this successful text, and the first using R. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: • Two new co-authors, Inbal Yahav and Casey Lichtendahl, who bring both expertise teaching business analytics courses using R, and data mining consulting experience in business and government • Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students • More than a dozen case studies demonstrating applications for the data mining techniques described • End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented • A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in R is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “ This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 publications including books. Peter C. Bruce is President and Founder of the Institute for Statistics Education at Statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective (Wiley) and co-author of Practical Statistics for Data Scientists: 50 Essential Concepts (O’Reilly). Inbal Yahav, PhD, is Professor at the Graduate School of Business Administration at Bar-Ilan University, Israel. She teaches courses in social network analysis, advanced research methods, and software quality assurance. Dr. Yahav received her PhD in Operations Research and Data Mining from the University of Maryland, College Park. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American St
Data Mining for Business Intelligence
by Galit Shmueli Peter C. Bruce Nitin R. PatelPraise 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."--computingreviews.comIncorporating 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 ensemblesA revised chapter on data visualization that now features interactive visualization principles and added assignments that demonstrate interactive visualization in practiceSeparate chapters that each treat k-nearest neighbors and Naïve Bayes methodsSummaries at the start of each chapter that supply an outline of key topicsThe 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.
Data Mining for Design and Marketing (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
by Katsutoshi Yada Yukio OhsawaData Mining for Design and Marketing shows how to design and integrate data mining tools into human thinking processes in order to make better business decisions, especially in designing and marketing products and systems.The expert contributors discuss how data mining can identify valuable consumer patterns, which aid marketers and designers in de
Data Mining for Managers
by Richard BoireBig Data is a growing business trend, but there little advice available on how to use it practically. Written by a data mining expert with over 30 years of experience, this book uses case studies to help marketers, brand managers and IT professionals understand how to capture and measure data for marketing purposes.
Data Mining for Social Network Data (Annals of Information Systems #12)
by Hsinchun Chen Nasrullah Memon David L. Hicks Jennifer Jie XuDriven by counter-terrorism efforts, marketing analysis and an explosion in online social networking in recent years, data mining has moved to the forefront of information science. This proposed Special Issue on Data Mining for Social Network Data will present a broad range of recent studies in social networking analysis. It will focus on emerging trends and needs in discovery and analysis of communities, solitary and social activities, activities in open for a and commercial sites as well. It will also look at network modeling, infrastructure construction, dynamic growth and evolution pattern discovery using machine learning approaches and multi-agent based simulations. Editors are three rising stars in world of data mining, knowledge discovery, social network analysis, and information infrastructures, and are anchored by Springer author/editor Hsinchun Chen (Terrorism Informatics; Medical Informatics; Digital Government), who is one of the most prominent intelligence analysis and data mining experts in the world.
Data Mining for the Social Sciences
by David Monaghan Paul AttewellWe live in a world of big data: the amount of information collected on human behavior each day is staggering, and exponentially greater than at any time in the past. Additionally, powerful algorithms are capable of churning through seas of data to uncover patterns. Providing a simple and accessible introduction to data mining, Paul Attewell and David B. Monaghan discuss how data mining substantially differs from conventional statistical modeling familiar to most social scientists. The authors also empower social scientists to tap into these new resources and incorporate data mining methodologies in their analytical toolkits. Data Mining for the Social Sciences demystifies the process by describing the diverse set of techniques available, discussing the strengths and weaknesses of various approaches, and giving practical demonstrations of how to carry out analyses using tools in various statistical software packages.
Data Mining: A Tutorial-Based Primer, Second Edition (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
by Richard J. Roiger"Dr. Roiger does an excellent job of describing in step by step detail formulae involved in various data mining algorithms, along with illustrations. In addition, his tutorials in Weka software provide excellent grounding for students in comprehending the underpinnings of Machine Learning as applied to Data Mining. The inclusion of?RapidMiner software tutorials and examples in the book is also a definite plus since it is one of the most popular Data Mining software platforms in use today." --Robert Hughes, Golden Gate University, San Francisco, CA, USA Data Mining: A Tutorial-Based Primer, Second Edition provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results. The text guides students to understand how data mining can be employed to solve real problems and recognize whether a data mining solution is a feasible alternative for a specific problem. Fundamental data mining strategies, techniques, and evaluation methods are presented and implemented with the help of two well-known software tools. Several new topics have been added to the second edition including an introduction to Big Data and data analytics, ROC curves, Pareto lift charts, methods for handling large-sized, streaming and imbalanced data, support vector machines, and extended coverage of textual data mining. The second edition contains tutorials for attribute selection, dealing with imbalanced data, outlier analysis, time series analysis, mining textual data, and more. The text provides in-depth coverage of RapidMiner Studio and Weka’s Explorer interface. Both software tools are used for stepping students through the tutorials depicting the knowledge discovery process. This allows the reader maximum flexibility for their hands-on data mining experience.
Data Mining: Concepts, Methods and Applications in Management and Engineering Design (Decision Engineering)
by Jiafu Tang Yong Yin Ikou Kaku Jianming ZhuData Mining introduces in clear and simple ways how to use existing data mining methods to obtain effective solutions for a variety of management and engineering design problems. Data Mining is organised into two parts: the first provides a focused introduction to data mining and the second goes into greater depth on subjects such as customer analysis. It covers almost all managerial activities of a company, including: * supply chain design, * product development, * manufacturing system design, * product quality control, and * preservation of privacy. Incorporating recent developments of data mining that have made it possible to deal with management and engineering design problems with greater efficiency and efficacy, Data Mining presents a number of state-of-the-art topics. It will be an informative source of information for researchers, but will also be a useful reference work for industrial and managerial practitioners.
Data Mining: Technologies, Techniques, Tools, and Trends
by Bhavani ThuraisinghamFocusing on a data-centric perspective, this book provides a complete overview of data mining: its uses, methods, current technologies, commercial products, and future challenges.Three parts divide Data Mining:Part I describes technologies for data mining - database systems, warehousing, machine learning, visualization, decision sup
Data Mining: Theories, Algorithms, and Examples (Human Factors And Ergonomics Ser.)
by Nong YeNew technologies have enabled us to collect massive amounts of data in many fields. However, our pace of discovering useful information and knowledge from these data falls far behind our pace of collecting the data. Data Mining: Theories, Algorithms, and Examples introduces and explains a comprehensive set of data mining algorithms from various dat
Data Modeling of Financial Derivatives: A Conceptual Approach
by Robert MamayevWritten in plain English and based on successful client engagements, Data Modeling of Financial Derivatives: A Conceptual Approach introduces new and veteran data modelers, financial analysts, and IT professionals to the fascinating world of financial derivatives. Covering futures, forwards, options, swaps, and forward rate agreements, finance and modeling expert Robert Mamayev shows you step-by-step how to structure and describe financial data using advanced data modeling techniques. The book introduces IT professionals, in particular, to various financial and data modeling concepts that they may not have seen before, giving them greater proficiency in the financial language of derivatives--and greater ability to communicate with financial analysts without fear or hesitation. Such knowledge will be especially useful to those looking to pick up the necessary skills to become productive right away working in the financial sector. Financial analysts reading this book will come to grips with various data modeling concepts and therefore be in better position to explain the underlying business to their IT audience. Data Modeling of Financial Derivatives--which presumes no advanced knowledge of derivatives or data modeling--will help you: Learn the best entity-relationship modeling method out there--Barker's CASE methodology--and its application in the financial industry Understand how to identify and creatively reuse data modeling patterns Gain an understanding of financial derivatives and their various applications Learn how to model derivatives contracts and understand the reasoning behind certain design decisions Resolve derivatives data modeling complexities parsimoniously so that your clients can understand them intuitively Packed with numerous examples, diagrams, and techniques, this book will enable you to recognize the various design patterns that you are most likely to encounter in your professional career and apply them successfully in practice. Anyone working with financial models will find it an invaluable tool and career booster. What you'll learn You will learn how to: Recognize and identify financial derivatives Reuse data modeling patterns and apply them to create something new Data model simple and complex options Data model SWAPS Data model futures and forward contracts Who this book is for Data modelers, financial analysts, IT professionals, and anyone with an interest in data modeling and business analysis. Table of Contents Introduction Notation Financial Contracts Primer Modeling Forward Contracts Modeling Futures Contracts Modeling Options Advanced Options Modeling - Designing Trading Strategies Swaps and Forward Rate Agreements (FRAs) Finishing Thoughts