Автор: Hoffer Gittell, Jody , Naim Ali, Hebatallah Название: Relational Analytics ISBN: 0367436256 ISBN-13(EAN): 9780367436254 Издательство: Taylor&Francis Рейтинг: Цена: 34700.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This guidebook goes beyond people analytics to provide a research-based, practice-tested methodology for doing relational analytics, based on the science of relational coordination.
A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.
Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.
After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.
Автор: Ozcan, Yasar A. Название: Analytics and decision support in health care management ISBN: 1119219817 ISBN-13(EAN): 9781119219811 Издательство: Wiley Рейтинг: Цена: 91820.00 T Наличие на складе: Поставка под заказ. Описание: A compendium of health care quantitative techniques based in Excel Analytics and Decision Support in Health Care Operations is a comprehensive introductory guide to quantitative techniques, with practical Excel-based solutions for strategic health care management.
Автор: Croll Alistair, Yoskovitz Benjamin Название: Lean Analytics: Measuring Your Way to Product-Market Fit ISBN: 1449335675 ISBN-13(EAN): 9781449335670 Издательство: Wiley Рейтинг: Цена: 33780.00 T Наличие на складе: Поставка под заказ. Описание: Whether you`re a startup founder trying to disrupt an industry or an intrapreneur trying to provoke change from within, your biggest challenge is creating a product people actually want. This book shows you how to validate your initial idea, find the right customers, decide what to build, how to monetize your business, and how to spread the word.
Автор: Hilpisch Y Название: Derivatives Analytics with Python ISBN: 1119037999 ISBN-13(EAN): 9781119037996 Издательство: Wiley Рейтинг: Цена: 66530.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Supercharge options analytics and hedging using the power of Python Derivatives Analytics with Python shows you how to implement market-consistent valuation and hedging approaches using advanced financial models, efficient numerical techniques, and the powerful capabilities of the Python programming language.
Автор: Bennett, M., & Hugen, D. Название: Financial Analytics with R ISBN: 1107150752 ISBN-13(EAN): 9781107150751 Издательство: Cambridge Academ Рейтинг: Цена: 61240.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book provides the intuition and basic vocabulary as steps towards the financial, statistical, and algorithmic knowledge needed to resolve current industry problems, while also presenting a systematic way of developing analytical programs for finance in the statistical language R. This book is a key training resource for students and professionals alike.
Автор: Hui Yang,Eva K. Lee Название: Healthcare Analytics: From Data to Knowledge to Healthcare Improvement ISBN: 1118919394 ISBN-13(EAN): 9781118919392 Издательство: Wiley Рейтинг: Цена: 113990.00 T Наличие на складе: Поставка под заказ. Описание: Features statistical and operational research methods and tools being used to improve the healthcare industry With a focus on cutting–edge approaches to the quickly growing field of healthcare, Healthcare Analytics: From Data to Knowledge to Healthcare Improvement provides an integrated and comprehensive treatment on recent research advancements in data–driven healthcare analytics in an effort to provide more personalized and efficient healthcare services. Emphasizing data and healthcare analytics from an operational management and statistical perspective, the book details how analytical methods and tools can be utilized to enhance care quality and operational efficiency. Organized into two main sections, Part One features biomedical and health informatics and specifically addresses the analytics of genomic and proteomic data; physiological signals from patient monitoring systems; data uncertainty in clinical laboratory tests; predictive modeling; disease modeling for sepsis; and the design of cyber infrastructures for early prediction of epidemic events. Part Two focuses on healthcare delivery systems, including system advances for transforming clinic workflow and patient care; macro analysis of patient flow distribution; intensive care units; primary care; demand and resource allocation; mathematical models for predicting patient readmission and postoperative outcome; physician–patient interactions; insurance claims; and the role of social media in healthcare. Healthcare Analytics: From Data to Knowledge to Healthcare Improvement also features: Contributions from well–known international experts who shed light on new approaches in this growing area Discussions on contemporary methods and techniques to address the handling of rich and large–scale healthcare data as well as the overall optimization of healthcare system operations Numerous real–world examples and case studies that emphasize the vast potential of statistical and operational research tools and techniques to address the big data environment within the healthcare industry Plentiful applications that showcase the various analytical methods and tools that can be applied to successful predictive modeling The book is an ideal reference for academics and practitioners in operations research, management science, applied mathematics, statistics, business, industrial and systems engineering, healthcare systems, and economics. Healthcare Analytics: From Data to Knowledge to Healthcare Improvement is also appropriate for graduate–level courses typically offered within operations research, industrial engineering, business, and public health departments. Hui Yang, PhD, is Associate Professor in the Harold and Inge Marcus Department of Industrial and Manufacturing Engineering at The Pennsylvania State University. His research interests include sensor–based modeling and analysis of complex systems for process monitoring/control; system diagnostics/prognostics; quality improvement; and performance optimization with special focus on nonlinear stochastic dynamics and the resulting chaotic, recurrence, self–organizing behaviors. Eva K. Lee, PhD, is Professor in the H. Milton Stewart School of Industrial and Systems Engineering at the Georgia Institute of Technology, Director of the Center for Operations Research in Medicine and HealthCare, and Distinguished Scholar in Health System, Health Systems Institute at both Emory University School of Medicine and Georgia Institute of Technology. Her research interests include health risk prediction; early disease prediction and diagnosis; optimal treatment strategies and drug delivery; healthcare outcome analysis and treatment prediction; public health and medical preparedness; large–scale healthcare/medical decision analysis and quality improvement; clinical translational science; and business intelligence and organization transformation.
Автор: B. Baesens, D. Roesch, H. Scheule Название: Credit Risk Analytics - Measurement Techniques, Applications, and Examples in SAS ISBN: 1119143985 ISBN-13(EAN): 9781119143987 Издательство: Wiley Рейтинг: Цена: 71810.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The long-awaited, comprehensive guide to practical credit risk modeling Credit Risk Analytics provides a targeted training guide for risk managers looking to efficiently build or validate in-house models for credit risk management.
Автор: Mishra, Srikanta Название: Applied Statistical Modeling and Data Analytics ISBN: 0128032790 ISBN-13(EAN): 9780128032794 Издательство: Elsevier Science Рейтинг: Цена: 114530.00 T Наличие на складе: Поставка под заказ. Описание:
Applied Statistical Modeling and Data Analytics: A Practical Guide for the Petroleum Geosciences provides a practical guide to many of the classical and modern statistical techniques that have become established for oil and gas professionals in recent years. It serves as a "how to" reference volume for the practicing petroleum engineer or geoscientist interested in applying statistical methods in formation evaluation, reservoir characterization, reservoir modeling and management, and uncertainty quantification.
Beginning with a foundational discussion of exploratory data analysis, probability distributions and linear regression modeling, the book focuses on fundamentals and practical examples of such key topics as multivariate analysis, uncertainty quantification, data-driven modeling, and experimental design and response surface analysis. Data sets from the petroleum geosciences are extensively used to demonstrate the applicability of these techniques. The book will also be useful for professionals dealing with subsurface flow problems in hydrogeology, geologic carbon sequestration, and nuclear waste disposal.
Authored by an internationally renowned scientist and a leader in the field of spatial statistics
Presents an easy to follow narrative which progresses from simple concepts to more challenging ones
Includes practical examples for the most relevant and popular statistical methods, using data sets from the petroleum geosciences
Addresses the theory and practice of statistical modeling and analysis from the perspective of geoscience applications
Автор: Fred Dallmayr Название: Democracy to Come: Politics as Relational Praxis ISBN: 0190670975 ISBN-13(EAN): 9780190670979 Издательство: Oxford Academ Рейтинг: Цена: 35370.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: In this book Fred Dallmayr lays the groundwork for a new understanding of democracy. He argues that democracy is not a stable system anchored in a manifest authority (like monarchy), but is sustained by the recessed and purely potential rule of the "people". Hence, democracy has to constantly reinvent itself, resembling theologically a creatio continua. Like one of Calder's mobiles, democracy for him involves three basic elements that must be balanced constantly: the people, political leaders, and policy goals. Where this balance is disrupted, democracy derails into populism, Bonapartism, or messianism. Given this need for balance, democratic politics is basically a "relational praxis." In our globalizing age, democracy cannot be confined domestically. Dallmayr rejects the idea that it can be autocratically imposed abroad through forced regime change, or that the dominant Western model can simply be transferred elsewhere. In this respect, he challenges the equation of democracy with the pursuit of individual or collective self-interest, insisting that other, more ethical conceptions are possible and that different societies should nurture democracy with their own cultural resources. Providing examples, he discusses efforts to build democracy in the Middle East, China, and India (respectively with Islamic, Confucian and Hindu resources). In the end, Dallmayr's hope is for a "democracy to come", that is, a cosmopolitan community governed not by hegemonic force but by the spirit of equality and mutual respect.
Автор: Hsu, Hui-Huang Название: Big Data Analytics for Sensor-Network Collected Intelligence ISBN: 0128093935 ISBN-13(EAN): 9780128093931 Издательство: Elsevier Science Рейтинг: Цена: 101060.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Big Data Analytics for Sensor-Network Collected Intelligence explores state-of-the-art methods for using advanced ICT technologies to perform intelligent analysis on sensor collected data. The book shows how to develop systems that automatically detect natural and human-made events, how to examine people's behaviors, and how to unobtrusively provide better services.
It begins by exploring big data architecture and platforms, covering the cloud computing infrastructure and how data is stored and visualized. The book then explores how big data is processed and managed, the key security and privacy issues involved, and the approaches used to ensure data quality.
In addition, readers will find a thorough examination of big data analytics, analyzing statistical methods for data analytics and data mining, along with a detailed look at big data intelligence, ubiquitous and mobile computing, and designing intelligence system based on context and situation.
Indexing: The books of this series are submitted to EI-Compendex and SCOPUS
Contains contributions from noted scholars in computer science and electrical engineering from around the globe
Provides a broad overview of recent developments in sensor collected intelligence
Edited by a team comprised of leading thinkers in big data analytics
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