Описание: Now in a thoroughly revised 5th Edition, An Introduction to the Policy Process provides students at all levels with an accessible, readable, and affordable introduction to the field of public policy. In keeping with prior editions, author Tom Birkland conveys the best current thinking on the policy process in a clear, conversational style. Designed to address new developments in both policy theory and policy making, the 5th Edition includes examinations of: the Brexit referendum result and its effects on the UK, EU and world politics, as well as the 2016 election of Donald Trump as President of the United States, and the ways in which these events have caused voters and policy makers to rethink their assumptions; changes to the media environment, including the decline of newspapers and television news, the growth of social media, and the emergence of ‘fake news'; new policy theory developments like the emergence of the Narrative Policy Framework (NPF) and continued and newer applications of existing theories of policy process like Advocacy Coalitions, Multiple Streams, Punctuated Equilibrium, and Institutional Analysis and Development; all-new 'What Does the Research Say?' boxes to illustrate concepts outlined in the book, demonstrate the ways in which the material is applicable to a range of policy problems, and encourage students to further study the policy process and substantive policy matters in which they are interested; and all-new and updated chapter ‘at a glance’ outlines, definitions of key terms, provocative review questions, recommended reading, visual aids and case studies, theoretical literature, and PowerPoint slides and Test Banks to make teaching from the book easier than ever. Firmly grounded in both social science and political science, An Introduction to the Policy Process provides the most up-to-date and thorough overview of the theory and practice of the policy process, ideal for upper-level undergraduate and introductory graduate courses in Public Policy, Public Administration, and Political Science programs.
Автор: P Suresh et al. Название: Big data analytics in smart manufacturing ISBN: 1032065516 ISBN-13(EAN): 9781032065519 Издательство: Taylor&Francis Рейтинг: Цена: 14545.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The ultra-secure and immutable ledgers, strong, consensus mechanisms, decentralization, and self-sovereign identity of AI and IoT technologies have tremendous potential to rebalance and improve machine learning algorithms. This book discusses the possibility of using AI, IoT and machine learning for the enhancement of healthcare systems.
Data analytics is core to business and decision making. The rapid increase in data volume, velocity and variety offers both opportunities and challenges. While open source solutions to store big data, like Hadoop, offer platforms for exploring value and insight from big data, they were not originally developed with data security and governance in mind. Big Data Management discusses numerous policies, strategies and recipes for managing big data. It addresses data security, privacy, controls and life cycle management offering modern principles and open source architectures for successful governance of big data.
The author has collected best practices from the world’s leading organizations that have successfully implemented big data platforms. The topics discussed cover the entire data management life cycle, data quality, data stewardship, regulatory considerations, data council, architectural and operational models are presented for successful management of big data. The book is a must-read for data scientists, data engineers and corporate leaders who are implementing big data platforms in their organizations.
Автор: Burk, Scott , Miner, Gary Название: It`s All Analytics! ISBN: 0367493799 ISBN-13(EAN): 9780367493790 Издательство: Taylor&Francis Рейтинг: Цена: 9033.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book, the first in a series of three, provides a look at the foundations of artificial intelligence and analytics and why readers need an unbiased understanding of the subject.
Автор: Liebowitz, Jay Название: Pivoting Government through Digital Transformation ISBN: 1032439904 ISBN-13(EAN): 9781032439907 Издательство: Taylor&Francis Рейтинг: Цена: 20671.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Liebowitz, Jay Название: Pivoting Government through Digital Transformation ISBN: 1032439890 ISBN-13(EAN): 9781032439891 Издательство: Taylor&Francis Рейтинг: Цена: 6889.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Kellstedt Paul M Название: The Fundamentals of Political Science Research ISBN: 1316642674 ISBN-13(EAN): 9781316642672 Издательство: Cambridge Academ Рейтинг: Цена: 6334.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The third edition of the textbook is aimed at upper-level undergraduates and graduate students in political science. It provides the basic tools necessary for students to understand and craft scientific research on politics. The new edition contains a new pedagogical feature, `Your Turn` boxes, meant to engage students.
Автор: Kudyba, Stephan Название: Big Data, Mining, and Analytics ISBN: 0367378817 ISBN-13(EAN): 9780367378813 Издательство: Taylor&Francis Рейтинг: Цена: 9033.00 р. Наличие на складе: Нет в наличии.
Описание:
There is an ongoing data explosion transpiring that will make previous creations, collections, and storage of data look trivial. Big Data, Mining, and Analytics: Components of Strategic Decision Making ties together big data, data mining, and analytics to explain how readers can leverage them to extract valuable insights from their data. Facilitating a clear understanding of big data, it supplies authoritative insights from expert contributors into leveraging data resources, including big data, to improve decision making.
Illustrating basic approaches of business intelligence to the more complex methods of data and text mining, the book guides readers through the process of extracting valuable knowledge from the varieties of data currently being generated in the brick and mortar and internet environments. It considers the broad spectrum of analytics approaches for decision making, including dashboards, OLAP cubes, data mining, and text mining.
Includes a foreword by Thomas H. Davenport, Distinguished Professor, Babson College; Fellow, MIT Center for Digital Business; and Co-Founder, International Institute for Analytics
Introduces text mining and the transforming of unstructured data into useful information
Examines real time wireless medical data acquisition for today's healthcare and data mining challenges
Presents the contributions of big data experts from academia and industry, including SAS
Highlights the most exciting emerging technologies for big data
Filled with examples that illustrate the value of analytics throughout, the book outlines a conceptual framework for data modeling that can help you immediately improve your own analytics and decision-making processes. It also provides in-depth coverage of analyzing unstructured data with text mining methods.
Автор: Gerald William Evans, William E. Biles, Ki-Hwan G. Bae Название: Analytics, Operations, and Strategic Decision Making in the Public Sector ISBN: 152257591X ISBN-13(EAN): 9781522575917 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 31324.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Analytics for the public sector involves the application of operations research and statistical techniques to solve various problems existing outside of the private sector. The use of analytics for the public sector results in more efficient and effective services for the clients and users of these systems.Analytics, Operations, and Strategic Decision Making in the Public Sector is an essential reference source that discusses analytics applications in various public sector organizations, and addresses the difficulties associated with the design and operation of these systems including multiple conflicting objectives, uncertainties and resulting risk, ill-structured nature, combinatorial design aspects, and scale. Featuring research on topics such as analytical modeling techniques, data mining, and statistical analysis, this book is ideally designed for academicians, educators, researchers, students, and public sector professionals including those in local, state, and federal governments; criminal justice systems; healthcare; energy and natural resources; waste management; emergency response; and the military.
Автор: Simon Walkowiak Название: Big Data Analytics with R ISBN: 1786466457 ISBN-13(EAN): 9781786466457 Издательство: Неизвестно Рейтинг: Цена: 11217.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Utilize R to uncover hidden patterns in your Big Data About This Book Perform computational analyses on Big Data to generate meaningful results Get a practical knowledge of R programming language while working on Big Data platforms like Hadoop, Spark, H2O and SQL/NoSQL databases, Explore fast, streaming, and scalable data analysis with the most cutting-edge technologies in the market Who This Book Is For This book is intended for Data Analysts, Scientists, Data Engineers, Statisticians, Researchers, who want to integrate R with their current or future Big Data workflows. It is assumed that readers have some experience in data analysis and understanding of data management and algorithmic processing of large quantities of data, however they may lack specific skills related to R. What You Will Learn Learn about current state of Big Data processing using R programming language and its powerful statistical capabilities Deploy Big Data analytics platforms with selected Big Data tools supported by R in a cost-effective and time-saving manner Apply the R language to real-world Big Data problems on a multi-node Hadoop cluster, e.g. electricity consumption across various socio-demographic indicators and bike share scheme usage Explore the compatibility of R with Hadoop, Spark, SQL and NoSQL databases, and H2O platform In Detail Big Data analytics is the process of examining large and complex data sets that often exceed the computational capabilities. R is a leading programming language of data science, consisting of powerful functions to tackle all problems related to Big Data processing. The book will begin with a brief introduction to the Big Data world and its current industry standards. With introduction to the R language and presenting its development, structure, applications in real world, and its shortcomings. Book will progress towards revision of major R functions for data management and transformations. Readers will be introduce to Cloud based Big Data solutions (e.g. Amazon EC2 instances and Amazon RDS, Microsoft Azure and its HDInsight clusters) and also provide guidance on R connectivity with relational and non-relational databases such as MongoDB and HBase etc. It will further expand to include Big Data tools such as Apache Hadoop ecosystem, HDFS and MapReduce frameworks. Also other R compatible tools such as Apache Spark, its machine learning library Spark MLlib, as well as H2O. Style and approach This book will serve as a practical guide to tackling Big Data problems using R programming language and its statistical environment. Each section of the book will present you with concise and easy-to-follow steps on how to process, transform and analyse large data sets."
Автор: Koitzsch Название: Pro Hadoop Data Analytics ISBN: 1484219090 ISBN-13(EAN): 9781484219096 Издательство: Springer Рейтинг: Цена: 5589.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Learn advanced analytical techniques and leverage existing toolkits to make your analytic applications more powerful, precise, and efficient. This book provides the right combination of architecture, design, and implementation information to create analytical systems which go beyond the basics of classification, clustering, and recommendation.In Pro Hadoop Data Analytics best practices are emphasized to ensure coherent, efficient development. A complete example system will be developed using standard third-party components which will consist of the toolkits, libraries, visualization and reporting code, as well as support glue to provide a working and extensible end-to-end system.The book emphasizes four important topics:The importance of end-to-end, flexible, configurable, high-performance data pipeline systems with analytical components as well as appropriate visualization results. Best practices and structured design principles. This will include strategic topics as well as the how to example portions.The importance of mix-and-match or hybrid systems, using different analytical components in one application to accomplish application goals. The hybrid approach will be prominent in the examples.Use of existing third-party libraries is key to effective development. Deep dive examples of the functionality of some of these toolkits will be showcased as you develop the example system.
What You'll Learn The what, why, and how of building big data analytic systems with the Hadoop ecosystemLibraries, toolkits, and algorithms to make development easier and more effectiveBest practices to use when building analytic systems with Hadoop, and metrics to measure performance and efficiency of components and systemsHow to connect to standard relational databases, noSQL data sources, and moreUseful case studies and example components which assist you in creating your own systems
Who This Book Is For
Software engineers, architects, and data scientists with an interest in the design and implementation of big data analytical systems using Hadoop, the Hadoop ecosystem, and other associated technologies.
Автор: Pyne Название: Big Data Analytics ISBN: 8132236262 ISBN-13(EAN): 9788132236269 Издательство: Springer Рейтинг: Цена: 15372.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book has a collection of articles written by Big Data experts to describe some of the cutting-edge methods and applications from their respective areas of interest, and provides the reader with a detailed overview of the field of Big Data Analytics as it is practiced today. The chapters cover technical aspects of key areas that generate and use Big Data such as management and finance; medicine and healthcare; genome, cytome and microbiome; graphs and networks; Internet of Things; Big Data standards; bench-marking of systems; and others. In addition to different applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modelling of high-dimensional data are also covered. The varied collection of themes in this volume introduces the reader to the richness of the emerging field of Big Data Analytics.
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