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Catalogue complet

Data-Directed Mathematical Modeling

Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program, Third Edition, is for any student, executive, manager, or data professional who needs to understand and implement a data governance program, to ensure consistent, accurate and reliable data across their organization. The problem of managing data continues to grow with issues surrounding the cost of storage, exponential growth, as well as administrative, management, and security concerns - the solution to being able to scale all of these issues up is data governance which provides better services to users and saves money. This book offers an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. John Ladley guides readers though the detailed process of developing a Data Governance program, including an introduction to the concepts and principles of Data Governance, Data literacy concepts, business case, strategy, systems architecture and design, the importance of team engagement, implementation, and change management. The Third Edition includes all-new coverage embracing new data capabilities such as ethics and AI integration, as well as key regulatory changes. Data governance is now inevitable, and no longer an option. John Ladley examines the impact of society on data and the relevance for organizations to simply "do better." The Third Edition updates coverage on data governance tools to achieve delivery of necessary capabilities. This valuable resource provides foundational understanding to advanced students in Data Management and related courses, as well as comprehensive guidance for beginning professionals, managers or analysts looking to improve their processes. With the provided framework and case studies, all students and professionals in the data governance field will gain key insights into launching successful and money-saving data governance programs.

How to Design, Deploy, and Sustain an Effective Data Governance Program

Data Governance

Inside Rugby's Tactical and Data Revolution

Attacking the Space

De Gruyter Series in Applied and Numerical Mathematics

Advanced Computational Approaches for Rarefied Flows

Beyond Navier-Stokes

As data is the most important asset for any organization, it is essential to apply analytics to fulfill the sustainability needs of any organization. This book aims to provide a context for data analytics for the sustainability of organizations demonstrating innovation across a few specific subject areas with detailed coverage. It highlights how data analytics enables organizations to create intelligence by exploring opportunities and overcoming challenges, both present and future.Data Science: Semantic Technology, Intelligent Data Analytics, and Sustainability Practice is packed with up-to-date examples and data, ensuring it's both meaningful and practical for a diverse audience. It begins with an introduction to data analytics in the context of sustainable development. Each chapter explores the different aspects of data analytics for sustainability, utilizing a state-of-the-art context that is easy to grasp. Key concepts are presented through clear explanations, making them accessible to all readers. At the end of each chapter, is a summary of the main points, allowing for quick reviews of the essential ideas. It provides practical insights into applying data analytics concepts towards sustainable development, empowering readers to enhance their decision-making abilities, and discusses a strategic approach aligned with the vision for 2030 and beyond while addressing the anticipated challenges.This resource provides support to researchers, scholars, technology practitioners, business analysts, data scientists, and industry professionals worldwide, promoting globalization and enabling real-life application of findings in their organizations.

Semantic Technology, Intelligent Data Analytics, and Sustainability Practices

Data Science

Applications in R and Python

Essentials of Big Data Analytics

Understand the transformative impact of data and artificial intelligence on the global travel and tourism industry and how to leverage these innovations within travel businesses

Leveraging AI, Data and Analytics in Travel

Technology-Led Tourism

Graph Mining

Math Survival Guide: Tips for Science Student, 3E

A Guide to Navigating Defaults and Managing Sovereign Fixed Income Portfolios

The Sovereign Debt Investor

Many of the deepest and most important areas of mathematics have arisen from questions about extremes - shortest lines, smallest areas, densest packings, fewest colours. Mathematicians have been grappling with such issues for centuries, and some go back thousands of years. The isoperimetric problem, for example - which asks for the shortest path enclosing a given area - dates back to the mythological founding of the city of Carthage. By contrast, it was only in 2017 that the densest ways to pack identical spheres into a space of 24 dimensions was finally proved. Many of these problems are more than mere thought experiments. The origins of the Travelling Salesperson Problem - find the shortest route that visits a given set of cities - are self-explanatory. The Plateau problem, about the geometry of soap bubbles, now has applications as diverse as cosmology and biological development. Reaching for the Extreme tells the stories of these and other similar problems: their historical roots, the struggles to solve them, and the uses that can be made of the results, when such uses exist.

How the Quest for the Biggest, Fewest and Weirdest Makes Maths

Reaching for the Extreme

Chapman & Hall/CRC Computational Science

This book is a well-recognized and leading guidebook on High Performance Computing for a broad audience of readers from industry and academia. The new edition is completely reorganized and updated to include the latest research in HPC. A new introductory chapter and a new chapter on accelerated computing have been added to the book to keep pace with recent advances.

Introduction to High Performance Computing for Scientists and Engineers, Second Edition

Wiley Series in Probability and Statistics

New edition of a PROSE award finalist title on core concepts for machine learning, updated with the latest developments in the field, now with Python and R source code side-by-side Machine Learning is a comprehensive text on the core concepts, approaches, and applications of machine learning. It presents fundamental ideas, terminology, and techniques for solving applied problems in classification, regression, clustering, density estimation, and dimension reduction. New content for this edition includes chapter expansions which provide further computational and algorithmic insights to improve reader understanding. This edition also revises several chapters to account for developments since the prior edition. In this book, the design principles behind the techniques are emphasized, including the bias-variance trade-off and its influence on the design of ensemble methods, enabling readers to solve applied problems more efficiently and effectively. This book also includes methods for optimization, risk estimation, model selection, and dealing with biased data samples and software limitations - essential elements of most applied projects. Written by an expert in the field, this important resource: <ul> <li>Illustrates many classification methods with a single, running example, highlighting similarities and differences between methods</li> <li>Presents side-by-side Python and R source code which shows how to apply and interpret many of the techniques covered</li> <li>Includes many thoughtful exercises as an integral part of the text, with an appendix of selected solutions</li> <li>Contains useful information for effectively communicating with clients on both technical and ethical topics</li> <li>Details classification techniques including likelihood methods, prototype methods, neural networks, classification trees, and support vector machines</li> </ul> A volume in the popular Wiley Series in Probability and Statistics, Machine Learning offers the practical information needed for an understanding of the methods and application of machine learning for advanced undergraduate and beginner graduate students, data science and machine learning practitioners, and other technical professionals in adjacent fields.

A Concise Introduction

Machine Learning

Data-Directed Mathematical Modeling

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