chirag patel himanshu pandya and devarshi mehta a study of microarray data resources and its management

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Dhammika Amaratunga Exploration and Analysis of DNA Microarray and Other High-Dimensional Data

Praise for the First Edition “…extremely well written…a comprehensive and up-to-date overview of this important field.” – Journal of Environmental Quality Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition provides comprehensive coverage of recent advancements in microarray data analysis. A cutting-edge guide, the Second Edition demonstrates various methodologies for analyzing data in biomedical research and offers an overview of the modern techniques used in microarray technology to study patterns of gene activity. The new edition answers the need for an efficient outline of all phases of this revolutionary analytical technique, from preprocessing to the analysis stage. Utilizing research and experience from highly-qualified authors in fields of data analysis, Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition features: A new chapter on the interpretation of findings that includes a discussion of signatures and material on gene set analysis, including network analysis New topics of coverage including ABC clustering, biclustering, partial least squares, penalized methods, ensemble methods, and enriched ensemble methods Updated exercises to deepen knowledge of the presented material and provide readers with resources for further study The book is an ideal reference for scientists in biomedical and genomics research fields who analyze DNA microarrays and protein array data, as well as statisticians and bioinformatics practitioners. Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition is also a useful text for graduate-level courses on statistics, computational biology, and bioinformatics.

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Jay Etchings A. Strategies in Biomedical Data Science. Driving Force for Innovation

An essential guide to healthcare data problems, sources, and solutions Strategies in Biomedical Data Science provides medical professionals with much-needed guidance toward managing the increasing deluge of healthcare data. Beginning with a look at our current top-down methodologies, this book demonstrates the ways in which both technological development and more effective use of current resources can better serve both patient and payer. The discussion explores the aggregation of disparate data sources, current analytics and toolsets, the growing necessity of smart bioinformatics, and more as data science and biomedical science grow increasingly intertwined. You'll dig into the unknown challenges that come along with every advance, and explore the ways in which healthcare data management and technology will inform medicine, politics, and research in the not-so-distant future. Real-world use cases and clear examples are featured throughout, and coverage of data sources, problems, and potential mitigations provides necessary insight for forward-looking healthcare professionals. Big Data has been a topic of discussion for some time, with much attention focused on problems and management issues surrounding truly staggering amounts of data. This book offers a lifeline through the tsunami of healthcare data, to help the medical community turn their data management problem into a solution. Consider the data challenges personalized medicine entails Explore the available advanced analytic resources and tools Learn how bioinformatics as a service is quickly becoming reality Examine the future of IOT and the deluge of personal device data The sheer amount of healthcare data being generated will only increase as both biomedical research and clinical practice trend toward individualized, patient-specific care. Strategies in Biomedical Data Science provides expert insight into the kind of robust data management that is becoming increasingly critical as healthcare evolves.

4179.55 рублей

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Kenett Ron S. Operational Risk Management. A Practical Approach to Intelligent Data Analysis

Models and methods for operational risks assessment and mitigation are gaining importance in financial institutions, healthcare organizations, industry, businesses and organisations in general. This book introduces modern Operational Risk Management and describes how various data sources of different types, both numeric and semantic sources such as text can be integrated and analyzed. The book also demonstrates how Operational Risk Management is synergetic to other risk management activities such as Financial Risk Management and Safety Management. Operational Risk Management: a practical approach to intelligent data analysis provides practical and tested methodologies for combining structured and unstructured, semantic-based data, and numeric data, in Operational Risk Management (OpR) data analysis. Key Features: The book is presented in four parts: 1) Introduction to OpR Management, 2) Data for OpR Management, 3) OpR Analytics and 4) OpR Applications and its Integration with other Disciplines. Explores integration of semantic, unstructured textual data, in Operational Risk Management. Provides novel techniques for combining qualitative and quantitative information to assess risks and design mitigation strategies. Presents a comprehensive treatment of «near-misses» data and incidents in Operational Risk Management. Looks at case studies in the financial and industrial sector. Discusses application of ontology engineering to model knowledge used in Operational Risk Management. Many real life examples are presented, mostly based on the MUSING project co-funded by the EU FP6 Information Society Technology Programme. It provides a unique multidisciplinary perspective on the important and evolving topic of Operational Risk Management. The book will be useful to operational risk practitioners, risk managers in banks, hospitals and industry looking for modern approaches to risk management that combine an analysis of structured and unstructured data. The book will also benefit academics interested in research in this field, looking for techniques developed in response to real world problems.

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Vijay Kumar Fundamentals of Pervasive Information Management Systems

A comprehensive new edition on mobile computing—covering both mobile and sensor data The new paradigm of pervasive computing was born from the needs of highly mobile workers to access and transfer data while on the go. Significant advances in the technology have lent and will continue to lend prevalence to its use—especially in m-commerce. Covering both mobile data and sensor data, this comprehensive text offers updated research on sensor technology, data stream processing, mobile database security, and contextual processing. Packed with cases studies, exercises, and examples, Fundamentals of Pervasive Information Management Systems covers essential aspects of wireless communication and provides a thorough discussion about managing information on mobile database systems (MDS). It addresses the integration of web and workflow with mobile computing and looks at the current state of research. Fundamentals of Pervasive Information Management Systems presents chapters on: Mobile Database System Mobile and Wireless Communication Location and Handoff Management Fundamentals of Database Processing Introduction to Concurrency Control Mechanisms Effect of Mobility on Data Processing Transaction Management in Mobile Database Systems Mobile Database Recovery Wireless Information Dissemination Introduction to Sensor Technology Sensor Technology and Data Streams Management Sensor Network Deployment: Case Studies Fundamentals of Pervasive Information Management Systems is an ideal book for researchers, teachers, and graduate students of mobile computing. The book may also be used as a reference text for researchers or managers.

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Joan Pynes E. Human Resources Management for Health Care Organizations. A Strategic Approach

This book is a comprehensive guide to the essential areas of health care human resources management, and is an immediately useful practical handbook for practitioners as well as a textbook for use health care management programs. Written by the authors of Handbook for the New Health Care Manager and Human Resources Management for Public and Nonprofit Organizations, the book covers the context of human resources management in the unique health care business arena from a strategic perspective includes SHRM and human resources planning, organizational culture and assessment, and the legal environment of human resources management. Managing volunteers and job analysis perfor­mance appraisal instruments, training and development programs, and recruitment, targeted selection and hiring techniques are covered. Compensation poli­cies and practices, employer-provided benefits management, implementation of training and organizational development programs, as well as labor-management relations for health care organizations and healthcare human resource information technology are covered, with practical examples and proven strategies amply provided in each chapter.

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Todd Lammle CCNA Data Center: Introducing Cisco Data Center Technologies Study Guide. Exam 640-916

Complete theory and practice for the CCNA Data Center Technologies exam CCNA Data Center, Introducing Cisco Data Center Technologies Study Guide is your comprehensive study guide for exam 640-916. Authors Todd Lammle and Todd Montgomery, authorities on Cisco networking, guide you through 100% of all exam objectives with expanded coverage of key exam topics, and hands-on labs that help you become confident in dealing with everyday challenges. You'll get access to the free Nexus switch simulator that allows you to try your hand at what you've learned without expensive software, plus bonus study aids, such as electronic flashcards, a practice exam, and a searchable PDF glossary of terms. Coverage includes Data Center networking and virtualization, storage networking, unified fabric, Cisco UCS configuration, Data Center services, and much more, for complete exam preparation. This is your guide to study for the entire second (and final) exam required for certification Review networking principles, products, and technologies Understand Nexus 1000V and Data Center virtualization Learn the principles and major configurations of Cisco UCS Practice hands-on solutions you'll employ on the job Prepare for using Cisco's Unified Data Center, which unifies computing, storage, networking, and management resources

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Brunt James W. Ecological Data. Design, Management and Processing

Ecologists are increasingly tackling difficult issues like global change, loss of biodiversity and sustainability of ecosystem services. These and related topics are enormously challenging, requiring unprecedented multidisciplinary collaboration and rapid synthesis of large amounts of diverse data into information and ultimately knowledge. New sensors, computers, data collection and storage devices and analytical and statistical methods provide a powerful tool kit to support analyses, graphics and visualizations that were unthinkable even a few years ago. New and increased emphasis on accessibility, management, processing and sharing of high-quality, well-maintained and understandable data represents a significant change in how scientists view and treat data. These issues are complex and despite their importance, are typically not addressed in database, ecological and statistical textbooks. This book addresses these issues, providing a much needed resource for those involved in designing and implementing ecological research, as well as students who are entering the environmental sciences. Chapters focus on the design of ecological studies, data management principles, scientific databases, data quality assurance, data documentation, archiving ecological data and information and processing data into information and knowledge. The book stops short of a detailed treatment of data analysis, but does provide pointers to the relevant literature in graphics, statistics and knowledge discovery. The central thesis of the book is that high quality data management systems are critical for addressing future environmental challenges. This requires a new approach to how we conduct ecological research, that views data as a resource and promotes stewardship, recycling and sharing of data. Ecological Data will be particularly useful to those ecologists and information specialists that actively design, manage and analyze environmental databases. However, it will also benefit a wider audience of scientists and students in the ecological and environmental sciences.

11493.12 рублей

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Hugh McKenna The Delphi Technique in Nursing and Health Research

The Delphi Technique in Nursing and Health Research is a practical guide to using the Delphi methodology for students and researchers in nursing and health. It adopts a logical step-by-step approach, introducing the researcher to the Delphi, outlining its development, analysing key characteristics and parameters for its successful use and exploring its applications in nursing and health. The book addresses issues of methodology, design, framing the research question, sampling, instrumentation, methodological rigour, reliability and validity, and methods of data analysis. The Delphi Technique in Nursing and Health Research enables the reader to be aware of the limitations of the technique and possible solutions, to design a Delphi questionnaire for each of the different rounds of a study, to consider different approaches to the technique in relation to a study, to analyse the data from each round of a Delphi study, and to understand the importance of feedback between rounds. Key Features A practical guide to facilitate use of the Delphi technique Provides the reader with the necessary information to participate in and conduct Delphi studies Examines different types of Delphi, including the e-Delphi, and modifications made to the technique Includes examples of real empirical investigations, brief case scenarios and key learning points for each chapter Explores the role of the Delphi researcher Explores ethical issues and issues of anonymity, use of experts and controlled feedback

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Giudici Paolo Applied Data Mining for Business and Industry

The increasing availability of data in our current, information overloaded society has led to the need for valid tools for its modelling and analysis. Data mining and applied statistical methods are the appropriate tools to extract knowledge from such data. This book provides an accessible introduction to data mining methods in a consistent and application oriented statistical framework, using case studies drawn from real industry projects and highlighting the use of data mining methods in a variety of business applications. Introduces data mining methods and applications. Covers classical and Bayesian multivariate statistical methodology as well as machine learning and computational data mining methods. Includes many recent developments such as association and sequence rules, graphical Markov models, lifetime value modelling, credit risk, operational risk and web mining. Features detailed case studies based on applied projects within industry. Incorporates discussion of data mining software, with case studies analysed using R. Is accessible to anyone with a basic knowledge of statistics or data analysis. Includes an extensive bibliography and pointers to further reading within the text. Applied Data Mining for Business and Industry, 2nd edition is aimed at advanced undergraduate and graduate students of data mining, applied statistics, database management, computer science and economics. The case studies will provide guidance to professionals working in industry on projects involving large volumes of data, such as customer relationship management, web design, risk management, marketing, economics and finance.

14274.78 рублей

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Joseph Morabito Designing Knowledge Organizations. A Pathway to Innovation Leadership

A pedagogical approach to the principles and architecture of knowledge management in organizations This textbook is based on a graduate course taught at Stevens Institute of Technology. It focuses on the design and management of today’s complex K organizations. A K organization is any company that generates and applies knowledge. The text takes existing ideas from organizational design and knowledge management to enhance and elevate each through harmonization with concepts from other disciplines. The authors—noted experts in the field—concentrate on both micro- and macro design and their interrelationships at individual, group, work, and organizational levels. A key feature of the textbook is an incisive discussion of the cultural, practice, and social aspects of knowledge management. The text explores the processes, tools, and infrastructures by which an organization can continuously improve, maintain, and exploit all elements of its knowledge base that are most relevant to achieve its strategic goals. The book seamlessly intertwines the disciplines of organizational design and knowledge management and offers extensive discussions, illustrative examples, student exercises, and visualizations. The following major topics are addressed: Knowledge management, intellectual capital, and knowledge systems Organizational design, behavior, and architecture Organizational strategy, change, and development Leadership and innovation Organizational culture and learning Social networking, communications, and collaboration Strategic human resources; e.g., hiring K workers and performance reviews Knowledge science, thinking, and creativity Philosophy of knowledge and information Information, knowledge, social, strategy, and contract continuums Information management and intelligent systems; e.g., business intelligence, big data, and cognitive systems Designing Knowledge Organizations takes an interdisciplinary and original approach to assess and synthesize the disciplines of knowledge management and organizational design, drawing upon conceptual underpinnings and practical experiences in these and related areas.

8048.45 рублей

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Bart Baesens Analytics in a Big Data World. The Essential Guide to Data Science and its Applications

The guide to targeting and leveraging business opportunities using big data & analytics By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. Analytics in a Big Data World 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. The 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. Includes numerous case studies on risk management, fraud detection, customer relationship management, and web analytics Offers the results of research and the author's personal experience in banking, retail, and government Contains an overview of the visionary ideas and current developments on the strategic use of analytics for business Covers the topic of data analytics in easy-to-understand terms without an undo emphasis on mathematics and the minutiae of statistical analysis For organizations looking to enhance their capabilities via data analytics, this resource is the go-to reference for leveraging data to enhance business capabilities.

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Susan Zneimer Mahler Cytogenetic Laboratory Management. Chromosomal, FISH and Microarray-Based Best Practices and Procedures

Cytogenetic Laboratory Management: Chromosomal, FISH and Microarray-Based Best Practices and Procedures is a practical guide that describes how to develop and implement best practice processes and procedures in the genetic laboratory setting. The text first describes good laboratory practices, including quality management, design control of tests and FDA guidelines for laboratory developed tests, and pre-clinical validation study designs. The second focus of the book describes best practices for staffing and training, including cost of testing, staffing requirements, process improvement using Six Sigma techniques, training and competency guidelines and complete training programs for cytogenetic and molecular genetic technologists. The third part of the text provides step-wise standard operating procedures for chromosomal, FISH and microarray-based tests, including pre-analytic, analytic and post-analytic steps in testing, and divided into categories by specimen type, and test-type. All three sections of the book include example worksheets, procedures, and other illustrative examples that can be downloaded from the Wiley website to be used directly without having to develop prototypes in your laboratory. Providing both a wealth of information on laboratory management and molecular and cytogenetic testing, Cytogenetic Laboratory Management will be an essential tool for laboratorians world-wide in the field of laboratory testing and genetics testing in particular. This book gives the essentials of: Developing and implementing good quality management programs in laboratories Understanding design control of tests and pre-clinical validations studies and reports FDA guidelines for laboratory developed tests Use of reagents, instruments and equipment Cost of testing assessment and process improvement using Six Sigma methodology Staffing training and competency objectives Complete training programs for molecular and cytogenetic technologists Standard operating procedures for all components of chromosomal analysis, FISH and microarray testing of different specimen types This volume is a companion to Cytogenetic Abnormalities: Chromosomal, FISH and Microarray-Based Clinical Reporting. The combined volumes give an expansive approach to performing, reporting and interpreting cytogenetic laboratory testing and the necessary management practices, staff and testing requirements.

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Stephen Bach Managing Human Resources. Human Resource Management in Transition

This revised edition is a comprehensive, authoritative set of essays. It is more detailed and analytical than the mainstream treatments of HRM. As in previous editions, Managing Human Resources analyses HRM, the study of work and employment, using an integrated multi-disciplinary approach. The starting point is a recognition that HRM practice and firm performance are influenced by a variety of institutional arrangements that extend beyond the firm. The consequences of HRM need to incorporate analysis of employees and other stakeholders as well as the implications for organizational performance.

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Chunlei Tang The Data Industry. The Business and Economics of Information and Big Data

Provides an introduction of the data industry to the field of economics This book bridges the gap between economics and data science to help data scientists understand the economics of big data, and enable economists to analyze the data industry. It begins by explaining data resources and introduces the data asset. This book defines a data industry chain, enumerates data enterprises’ business models versus operating models, and proposes a mode of industrial development for the data industry. The author describes five types of enterprise agglomerations, and multiple industrial cluster effects. A discussion on the establishment and development of data industry related laws and regulations is provided. In addition, this book discusses several scenarios on how to convert data driving forces into productivity that can then serve society. This book is designed to serve as a reference and training guide for ata scientists, data-oriented managers and executives, entrepreneurs, scholars, and government employees. Defines and develops the concept of a “Data Industry,” and explains the economics of data to data scientists and statisticians Includes numerous case studies and examples from a variety of industries and disciplines Serves as a useful guide for practitioners and entrepreneurs in the business of data technology The Data Industry: The Business and Economics of Information and Big Data is a resource for practitioners in the data science industry, government, and students in economics, business, and statistics. CHUNLEI TANG, Ph.D., is a research fellow at Harvard University. She is the co-founder of Fudan’s Institute for Data Industry and proposed the concept of the “data industry”. She received a Ph.D. in Computer and Software Theory in 2012 and a Master of Software Engineering in 2006 from Fudan University, Shanghai, China.

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Galit Shmueli Information Quality. The Potential of Data and Analytics to Generate Knowledge

Provides an important framework for data analysts in assessing the quality of data and its potential to provide meaningful insights through analysis Analytics and statistical analysis have become pervasive topics, mainly due to the growing availability of data and analytic tools. Technology, however, fails to deliver insights with added value if the quality of the information it generates is not assured. Information Quality (InfoQ) is a tool developed by the authors to assess the potential of a dataset to achieve a goal of interest, using data analysis. Whether the information quality of a dataset is sufficient is of practical importance at many stages of the data analytics journey, from the pre-data collection stage to the post-data collection and post-analysis stages. It is also critical to various stakeholders: data collection agencies, analysts, data scientists, and management. This book: Explains how to integrate the notions of goal, data, analysis and utility that are the main building blocks of data analysis within any domain. Presents a framework for integrating domain knowledge with data analysis. Provides a combination of both methodological and practical aspects of data analysis. Discusses issues surrounding the implementation and integration of InfoQ in both academic programmes and business / industrial projects. Showcases numerous case studies in a variety of application areas such as education, healthcare, official statistics, risk management and marketing surveys. Presents a review of software tools from the InfoQ perspective along with example datasets on an accompanying website. This book will be beneficial for researchers in academia and in industry, analysts, consultants, and agencies that collect and analyse data as well as undergraduate and postgraduate courses involving data analysis.

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Jeffrey Miles A. Management and Organization Theory. A Jossey-Bass Reader

Management and Organization Theory offers a summary and analysis of the 40 most popular, researched, and applied management and organization theories. This important resource includes key instruments used to measure variables in each theory and examines pertinent questions about the theory: strengths and weaknesses, practical applications, and the seminal articles published on each theory. «This is a remarkable book. Jeffrey Miles clearly explains and synthesizes 40 major theories of management and organization in an easily accessible and engaging style. Well researched, comprehensive in its coverage, thorough, balanced, and fair in its analyses of theories, the book is destined to be a major authoritative reference in the field. It is one of the most readable, informative, and useful books I have read. I strongly recommend it.» —Shaker A. Zahra, department chair, Robert E. Buuck Chair, and professor, Strategic Management and Organizations Department, University of Minnesota «This book provides a terrific advantage to any student or manager seeking to grasp the fundamental concepts that explain organizations and the behavior of people within them.»—Richard L. Daft, author, The Executive and the Elephant: A Leader's Guide to Building Inner Excellence; and the Brownlee O. Currey Jr. Professor of Management, Owen Graduate School of Management, Vanderbilt University «An easy-to-read summary of some of the most critical theories in the field of management—theories that have implications not just for scholars, but for practicing managers as well.» —Jay Barney, professor of management and human resources, and Chase Chair for Excellence in Corporate Strategy, Fisher College of Business, The Ohio State University

3935.21 рублей

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Galit Shmueli Data Mining for Business Analytics. Concepts, Techniques, and Applications with JMP Pro

Data 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 book uses 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.

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Thomas Wilson C. Value and Capital Management. A Handbook for the Finance and Risk Functions of Financial Institutions

A value management framework designed specifically for banking and insurance The Value Management Handbook is a comprehensive, practical reference written specifically for bank and insurance valuation and value management. Spelling out how the finance and risk functions add value in their respective spheres, this book presents a framework for measuring – and more importantly, influencing – the value of the firm from the position of the CFO and CRO. Case studies illustrating value-enhancing initiatives are designed to help Heads of Strategy offer CEOs concrete ideas toward creating more value, and discussion of «hard» and «soft» skills put CFOs and CROs in a position to better influence strategy and operations. The challenge of financial services valuation is addressed in terms of the roles of risk and capital, and business-specific «value trees» demonstrate the source of successful value enhancement initiatives. While most value management resources fail to adequately address the unique role of risk and capital in banks, insurance, and asset management, this book fills the gap by providing concrete, business-specific information that connects management actions and value creation, helping readers to: Measure value accurately for more productive value-based management initiatives and evaluation of growth opportunities Apply a quantitative, risk-adjusted value management framework reconciled with the way financial services shares are valued by the market Develop a value set specific to the industry to inspire initiatives that increase the firm's value Study the quantitative and qualitative management frameworks that move CFOs and CROs from measurement to management The roles of CFO and CRO in financial firms have changed dramatically over the past decade, requiring business savvy and the ability to challenge the CEO. The Value Management Handbook provides the expert guidance that leads CFOs and CROs toward better information, better insight, and better decisions.

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Data 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 book uses 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.

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