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Author: UNESCO
Publisher: UNESCO Publishing
ISBN: 9231006002
Category : Political Science
Languages : en
Pages : 64
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Book Description
Author: UNESCO
Publisher: UNESCO Publishing
ISBN: 9231006002
Category : Political Science
Languages : en
Pages : 64
Get Book Here
Book Description
Author: Davies, Tim
Publisher: African Minds
ISBN: 1928331955
Category : Social Science
Languages : en
Pages : 590
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Book Description
It’s been ten years since open data first broke onto the global stage. Over the past decade, thousands of programmes and projects around the world have worked to open data and use it to address a myriad of social and economic challenges. Meanwhile, issues related to data rights and privacy have moved to the centre of public and political discourse. As the open data movement enters a new phase in its evolution, shifting to target real-world problems and embed open data thinking into other existing or emerging communities of practice, big questions still remain. How will open data initiatives respond to new concerns about privacy, inclusion, and artificial intelligence? And what can we learn from the last decade in order to deliver impact where it is most needed? The State of Open Data brings together over 60 authors from around the world to address these questions and to take stock of the real progress made to date across sectors and around the world, uncovering the issues that will shape the future of open data in the years to come.
Author: Nathan Coyle
Publisher: CRC Press
ISBN: 1040037283
Category : Computers
Languages : en
Pages : 188
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Book Description
What if I told you something that could empower our third sector and activists to enhance their capacity? From gathering evidence for funding tenders to campaigning for crucial social issues and much more? It's called open data, yet many in social action remain unaware of it. Primarily shaped by corporate entities, open data seems tailored only for technologists, alienating the third sector. But in reality, it's a powerful tool for social change, bolstering civil society, and creating resilient communities. This book argues a simple point: if open data and the digital aspects that support it aren't accessible to all, then what is the point of it? In an age where technology should be seen as a fundamental human right, it's time to rethink outreach. Deeply rooted in grassroots social activism, this book explores a journey that led to collaborations with governments globally, based on real hands-on work, aiming to democratize open data. Through narrative storytelling, we share insights, best practices, procedures, and community-driven approaches. Regardless of your skill set or organization size, from grassroots workers to third-sector professionals and government officers, join us to reshape the perception of open data, fostering change in neighborhoods. Open Data for Everybody: Using Open Data for Social Good is a love letter to open data's transformative power. To create solutions, understanding the problem is crucial. This book seeks to return control to the real experts—those living and working within our communities.
Author: Bernard Marr
Publisher: John Wiley & Sons
ISBN: 1119795575
Category : Business & Economics
Languages : en
Pages : 359
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Book Description
WINNER OF THE BUSINESS BOOK OF THE YEAR AWARD 2022! Stay one step ahead of the competition with this expert review of the most impactful and disruptive business trends coming down the pike Far from slowing down, change and transformation in business seems to come only at a more and more furious rate. The last ten years alone have seen the introduction of groundbreaking new trends that pose new opportunities and challenges for leaders in all industries. In Business Trends in Practice: The 25+ Trends That Are Redefining Organizations, best-selling business author and strategist Bernard Marr breaks down the social and technological forces underlying these rapidly advancing changes and the impact of those changes on key industries. Critical consumer trends just emerging today—or poised to emerge tomorrow—are discussed, as are strategies for rethinking your organisation’s product and service delivery. The book also explores: Crucial business operations trends that are changing the way companies conduct themselves in the 21st century The practical insights and takeaways you can glean from technological and social innovation when you cut through the hype Disruptive new technologies, including AI, robotic and business process automation, remote work, as well as social and environmental sustainability trends Business Trends in Practice: The 25+ Trends That Are Redefining Organizations is a must-read resource for executives, business leaders and managers, and business development and innovation leads trying to get – and stay – on top of changes and disruptions that are right around the corner.
Author: Jay Liebowitz
Publisher: CRC Press
ISBN: 1000094677
Category : Computers
Languages : en
Pages : 187
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Book Description
Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that "artificial intelligence is included." We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools? Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data.
Author: Vijayalakshmi Kakulapati
Publisher: BoD – Books on Demand
ISBN: 183769592X
Category : Computers
Languages : en
Pages : 126
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Book Description
Data is often open to all users and sharers. Governments provide data on publicly available websites and this data may pertain to specific regions or be aggregate data on national or international issues. Data that is in the public domain but not in a machine-readable format is considered public data and may only be accessible via a right-of-access request. Maintaining accuracy and management is a major obstacle when it comes to data systems and solutions. Data governance describes the rules, procedures, and responsibilities that outline the data's acquisition, storage, retrieval and use. Data security and privacy refer to safeguards put in place to protect information from being seen, copied, distributed, altered, or destroyed without permission. Data integration and interoperability involve combining and exchanging data from many sources, systems, and formats, as well as facilitating data sharing and collaboration across various platforms, apps, and organizations. Defining data standards, implementing data quality checks, assigning data ownership and responsibility, and monitoring data performance and utilization are all important steps toward resolving the data quality problem. This book contains two sections. “Trends and Challenges of Open Data” and “Case Studies”. Each section contains three chapters.
Author: Mark Ryan
Publisher: Simon and Schuster
ISBN: 163835717X
Category : Computers
Languages : en
Pages : 262
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Book Description
Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Summary Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world businesses depend on. Filled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Here’s a dirty secret: Half of the time in most data science projects is spent cleaning and preparing data. But there’s a better way: Deep learning techniques optimized for tabular data and relational databases deliver insights and analysis without requiring intense feature engineering. Learn the skills to unlock deep learning performance with much less data filtering, validating, and scrubbing. About the book Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Get started using a dataset based on the Toronto transit system. As you work through the book, you’ll learn how easy it is to set up tabular data for deep learning, while solving crucial production concerns like deployment and performance monitoring. What's inside When and where to use deep learning The architecture of a Keras deep learning model Training, deploying, and maintaining models Measuring performance About the reader For readers with intermediate Python and machine learning skills. About the author Mark Ryan is a Data Science Manager at Intact Insurance. He holds a Master's degree in Computer Science from the University of Toronto. Table of Contents 1 Why deep learning with structured data? 2 Introduction to the example problem and Pandas dataframes 3 Preparing the data, part 1: Exploring and cleansing the data 4 Preparing the data, part 2: Transforming the data 5 Preparing and building the model 6 Training the model and running experiments 7 More experiments with the trained model 8 Deploying the model 9 Recommended next steps
Author: Tim Davies
Publisher: African Books Collective
ISBN: 1552506126
Category : Social Science
Languages : en
Pages : 592
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Book Description
Its been ten years since open data first broke onto the global stage. Over the past decade, thousands of programmes and projects around the world have worked to open data and use it to address a myriad of social and economic challenges. Meanwhile, issues related to data rights and privacy have moved to the centre of public and political discourse. As the open data movement enters a new phase in its evolution, shifting to target real-world problems and embed open data thinking into other existing or emerging communities of practice, big questions still remain. How will open data initiatives respond to new concerns about privacy, inclusion, and artificial intelligence? And what can we learn from the last decade in order to deliver impact where it is most needed? The State of Open Data brings together over 60 authors from around the world to address these questions and to take stock of the real progress made to date across sectors and around the world, uncovering the issues that will shape the future of open data in the years to come.
Author: Pamela Robinson
Publisher: University of Ottawa Press
ISBN: 077662976X
Category : Political Science
Languages : en
Pages : 209
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Book Description
The Future of Open Data flows from a multi-year Social Sciences and Humanities Research Council (SSHRC) Partnership Grant project that set out to explore open government geospatial data from an interdisciplinary perspective. Researchers on the grant adopted a critical social science perspective grounded in the imperative that the research should be relevant to government and civil society partners in the field. This book builds on the knowledge developed during the course of the grant and asks the question, “What is the future of open data?” The contributors’ insights into the future of open data combine observations from five years of research about the Canadian open data community with a critical perspective on what could and should happen as open data efforts evolve. Each of the chapters in this book addresses different issues and each is grounded in distinct disciplinary or interdisciplinary perspectives. The opening chapter reflects on the origins of open data in Canada and how it has progressed to the present date, taking into account how the Indigenous data sovereignty movement intersects with open data. A series of chapters address some of the pitfalls and opportunities of open data and consider how the changing data context may impact sources of open data, limits on open data, and even liability for open data. Another group of chapters considers new landscapes for open data, including open data in the global South, the data priorities of local governments, and the emerging context for rural open data.
Author: Ajay Agrawal
Publisher: University of Chicago Press
ISBN: 0226833127
Category : Business & Economics
Languages : en
Pages : 172
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Book Description
A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.
