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Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization’s day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. Demystifying Big Data and Machine Learning for Healthcare investigates how healthcare organizations can leverage this tapestry of big data to discover new business value, use cases, and knowledge as well as how big data can be woven into pre-existing business intelligence and analytics efforts. This book focuses on teaching you how to: Develop skills needed to identify and demolish big-data myths Become an expert in separating hype from reality Understand the V’s that matter in healthcare and why Harmonize the 4 C’s across little and big data Choose data fi delity over data quality Learn how to apply the NRF Framework Master applied machine learning for healthcare Conduct a guided tour of learning algorithms Recognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs) The variety of data in healthcare spans multiple business workflows, formats (structured, un-, and semi-structured), integration at point of care/need, and integration with existing knowledge. In order to deal with these realities, the authors propose new approaches to creating a knowledge-driven learning organization-based on new and existing strategies, methods and technologies. This book will address the long-standing challenges in healthcare informatics and provide pragmatic recommendations on how to deal with them.
The full text downloaded to your computer With eBooks you can: search for key concepts, words and phrases make highlights and notes as you study share your notes with friends eBooks are downloaded to your computer and accessible either offline through the Bookshelf (available as a free download), available online and also via the iPad and Android apps. Upon purchase, you'll gain instant access to this eBook. Time limit The eBooks products do not have an expiry date. You will continue to access your digital ebook products whilst you have your Bookshelf installed. 'Big Data' refers to a new class of data, to which 'big' doesn't quite do it justice. Much like an ocean is more than simply a deeper swimming pool, big data is fundamentally different to traditional data and needs a whole new approach. Packed with examples and case studies, this clear, comprehensive book will show you how to accumulate and utilise 'big data' in order to develop your business strategy. Big Data Demystified is your practical guide to help you draw deeper insights from the vast information at your fingertips; you will be able to understand customer motivations, speed up production lines, and even offer personalised experiences to each and every customer. With 20 years of industry experience, David Stephenson shows how big data can give you the best competitive edge, and why it is integral to the future of your business.
Artificial intelligence (AI) in its various forms –– machine learning, chatbots, robots, agents, etc. –– is increasingly being seen as a core component of enterprise business workflow and information management systems. The current promise and hype around AI are being driven by software vendors, academic research projects, and startups. However, we posit that the greatest promise and potential for AI lies in the enterprise with its applications touching all organizational facets. With increasing business process and workflow maturity, coupled with recent trends in cloud computing, datafication, IoT, cybersecurity, and advanced analytics, there is an understanding that the challenges of tomorrow cannot be solely addressed by today’s people, processes, and products. There is still considerable mystery, hype, and fear about AI in today’s world. A considerable amount of current discourse focuses on a dystopian future that could adversely affect humanity. Such opinions, with understandable fear of the unknown, don’t consider the history of human innovation, the current state of business and technology, or the primarily augmentative nature of tomorrow’s AI. This book demystifies AI for the enterprise. It takes readers from the basics (definitions, state-of-the-art, etc.) to a multi-industry journey, and concludes with expert advice on everything an organization must do to succeed. Along the way, we debunk myths, provide practical pointers, and include best practices with applicable vignettes. AI brings to enterprise the capabilities that promise new ways by which professionals can address both mundane and interesting challenges more efficiently, effectively, and collaboratively (with humans). The opportunity for tomorrow’s enterprise is to augment existing teams and resources with the power of AI in order to gain competitive advantage, discover new business models, establish or optimize new revenues, and achieve better customer and user satisfaction.
Measure, Use, Improve! Data Use in Out-of-School Time shares the experience and wisdom from a broad cross-section of out-of-school time professionals, ranging from internal evaluators, to funders, to researchers, to policy advocates. Key themes of the volume include building support for learning and evaluation within out-of-school time programs, creating and sustaining continuous quality improvement efforts, authentically engaging young people and caregivers in evaluation, and securing funder support for learning and evaluation. This volume will be particularly useful to leadership-level staff in out-of-school time organizations that are thinking about deepening their own learning and evaluation systems, yet aren’t sure where to start. Authors share conceptual frameworks that have helped inform their thinking, walk through practical examples of how they use data in out-of-school time, and offer advice to colleagues.
Master the art and science of data storytelling—with frameworks and techniques to help you craft compelling stories with data. The ability to effectively communicate with data is no longer a luxury in today’s economy; it is a necessity. Transforming data into visual communication is only one part of the picture. It is equally important to engage your audience with a narrative—to tell a story with the numbers. Effective Data Storytelling will teach you the essential skills necessary to communicate your insights through persuasive and memorable data stories. Narratives are more powerful than raw statistics, more enduring than pretty charts. When done correctly, data stories can influence decisions and drive change. Most other books focus only on data visualization while neglecting the powerful narrative and psychological aspects of telling stories with data. Author Brent Dykes shows you how to take the three central elements of data storytelling—data, narrative, and visuals—and combine them for maximum effectiveness. Taking a comprehensive look at all the elements of data storytelling, this unique book will enable you to: Transform your insights and data visualizations into appealing, impactful data stories Learn the fundamental elements of a data story and key audience drivers Understand the differences between how the brain processes facts and narrative Structure your findings as a data narrative, using a four-step storyboarding process Incorporate the seven essential principles of better visual storytelling into your work Avoid common data storytelling mistakes by learning from historical and modern examples Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals is a must-have resource for anyone who communicates regularly with data, including business professionals, analysts, marketers, salespeople, financial managers, and educators.
This book aims to provide readers with a comprehensive guide to the fundamentals of big data analytics and its applications in various industries and smart societies. What sets this book apart is its in-depth coverage of different aspects of big data analytics, including machine learning algorithms, spatial data analytics, and IoT-based smart systems for precision agriculture. The book also delves into the use of big data analytics in healthcare, energy management, and agricultural development, among others. The authors have used clear and concise language, along with relevant examples and case studies, to help readers understand the complex concepts involved in big data analytics. Key Features: Comprehensive coverage of the fundamentals of big data analytics In-depth discussion of different aspects of big data analytics, including machine learning algorithms, spatial data analytics, and IoT-based smart systems. Practical examples and case studies to help readers understand complex concepts. Coverage of the use of big data analytics in various industries, including healthcare, energy management, and agriculture Discussion of challenges and legal frameworks involved in big data analytics. Clear and concise language that is easy to understand. This book is a valuable resource for business owners, data analysts, students, and anyone interested in the field of big data analytics. It provides readers with the tools they need to leverage the power of big data and make informed decisions that can help their organizations succeed. Whether you are new to the field or an experienced practitioner, "Demystifying Big Data Analytics for Industries and Smart Societies" is must-read.
Are you searching for a coding language that will work for you? Do you want to create your own website of desktop applications? If so, C# is the right choice for you. When it comes to programming and choosing a coding language there are so many on the market that the beginner is faced with a bewildering choice and it can appear that they all do much the same job. But if creating visually elegant and functional applications is what you want, then C# is the one for you. Now, with C#: 2 books in 1 - The Ultimate Beginner's & Intermediate Guide to Learn C# Programming Step by Step, even a complete beginner can start to understand and develop programs and increase his knowledge with it through chapters on: Book 1 • What C# is • An overview of the features • Program structure and basic syntax • Working with variables • The conditional statements • C# methods • 7 data types supported by C# • Accurate use of operators and conditional statements • Proper use of arrays, structures, and encapsulations • And lots more… Book 2 • How C# was conceived and where it came from • C# interfaces and how to use them • Advanced decision statements and flow control • The different functions that are available • An introduction to garbage collections • Asynchronous programming and what it does • And much more… Book 3 • An insight into advanced C# languages • Dealing with unary and binary operators overload • Simple ways to fix name clashes • How to create and apply custom attributes • The benefits of CIL and dynamic assemblies • Graphics rendering made easy • The purpose and uses for NET core With the information contained in this book you could be on your way to learning how this guide can develop and expand on your programming knowledge and lead you to exciting new discoveries in this fascinating subject. This book will help you take the next step up from the basics of C# quickly and seamlessly. Get a copy now and begin your journey to a better and simpler world of programming.
As thinking, writing, and gossip increasingly take place in cyberspace, the part of our life that can be monitored and searched has vastly expanded. E-mail, even after it is deleted, becomes a permanent record that can be resurrected by employers or prosecutors at any point in the future. On the Internet, every website we visit, every store we browse in, every magazine we skim--and the amount of time we skim it--create electronic footprints that can be traced back to us, revealing detailed patterns about our tastes, preferences, and intimate thoughts. In this pathbreaking book, Jeffrey Rosen explores the legal, technological, and cultural changes that have undermined our ability to control how much personal information about ourselves is communicated to others, and he proposes ways of reconstructing some of the zones of privacy that law and technology have been allowed to invade. In the eighteenth century, when the Bill of Rights was drafted, the spectacle of state agents breaking into a citizen's home and rummaging through his or her private diaries was considered the paradigm case of an unconstitutional search and seizure. But during the impeachment of President Bill Clinton, prosecutors were able to subpoena Monica Lewinsky's bookstore receipts and to retrieve unsent love letters from her home computer. And the sense of violation that Monica Lewinsky experienced is not unique. In a world in which everything that Americans read, write, and buy can be recorded and monitored in cyberspace, there is a growing danger that intimate personal information originally disclosed only to our friends and colleagues may be exposed to--and misinterpreted by--a less understanding audience of strangers. Privacy is important, Rosen argues, because it protects us from being judged out of context in a world of short attention spans, a world in which isolated bits of intimate information can be confused with genuine knowledge. Rosen also examines the expansion of sexual-harassment law that has given employers an incentive to monitor our e-mail, Internet browsing habits, and office romances. And he suggests that some forms of offensive speech in the workplace--including the indignities allegedly suffered by Paula Jones and Anita Hill--are better conceived of as invasions of privacy than as examples of sex discrimination. Combining discussions of current events--from Kenneth Starr's tapes to DoubleClick's on-line profiles--with inno-vative legal and cultural analysis, The Unwanted Gaze offers a powerful challenge to Americans to be proactive in the face of new threats to privacy in the twenty-first century.
The purpose of this book is to speed up the processing of learning and mastering the Web Ontology Language OWL. To that end, the focus is on the 30% of OWL that gets used 90% of the time. After a slow incubation period of nearly 15 years, a large and growing number of organizations now have one or more projects using the Semantic Web stack of technologies. The Web Ontology Language (OWL) is an essential ingredient in this stack, and the need for ontologists is increasing faster than the number and variety of available resources for learning OWL. This is especially true for the primary target audience for this book: modelers who want to build OWL ontologies for practical use in enterprise and government settings. Others who may benefit from this book include technically oriented managers, semantic technology developers, undergraduate and post-graduate students, and finally, instructors looking for new ways to explain OWL. The book unfolds in a spiral manner, starting with the core ideas. Each subsequent cycle reinforces and expands on what has been learned in prior cycles and introduces new related ideas. Part 1 is a cook's tour of ontology and OWL, giving an informal overview of what things need to be said to build an ontology, followed by a detailed look at how to say them in OWL. This is illustrated using a healthcare example. Part 1 concludes with an explanation of some foundational ideas about meaning and semantics to prepare the reader for subsequent chapters. Part 2 goes into depth on properties and classes, which are the core of OWL. There are detailed descriptions of the main constructs that you are likely to need in every day modeling, including what inferences are sanctioned. Each is illustrated with real-world examples. Part 3 explains and illustrates how to put OWL into practice, using examples in healthcare, collateral, and financial transactions. A small ontology is described for each, along with some key inferences. Key limitations of OWL are identified, along with possible workarounds. The final chapter gives a variety of practical tips and guidelines to send the reader on their way.