June 15, 2021

Download Ebook Free Data Science Applied To Sustainability Analysis

Data Science Applied to Sustainability Analysis

Data Science Applied to Sustainability Analysis
Author : Jennifer Dunn,Prasanna Balaprakash
Publisher : Elsevier
Release Date : 2021-05-28
Category : Science
Total pages :310
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Data Science Applied to Sustainability Analysis focuses on the methodological considerations associated with applying this tool in analysis techniques such as lifecycle assessment and materials flow analysis. As sustainability analysts need examples of applications of big data techniques that are defensible and practical in sustainability analyses and that yield actionable results that can inform policy development, corporate supply chain management strategy, or non-governmental organization positions, this book helps answer underlying questions. In addition, it addresses the need of data science experts looking for routes to apply their skills and knowledge to domain areas. Presents data sources that are available for application in sustainability analyses, such as market information, environmental monitoring data, social media data and satellite imagery Includes considerations sustainability analysts must evaluate when applying big data Features case studies illustrating the application of data science in sustainability analyses

Big Data Science and Analytics for Smart Sustainable Urbanism

Big Data Science and Analytics for Smart Sustainable Urbanism
Author : Simon Elias Bibri
Publisher : Springer
Release Date : 2019-05-30
Category : Political Science
Total pages :337
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We are living at the dawn of what has been termed ‘the fourth paradigm of science,’ a scientific revolution that is marked by both the emergence of big data science and analytics, and by the increasing adoption of the underlying technologies in scientific and scholarly research practices. Everything about science development or knowledge production is fundamentally changing thanks to the ever-increasing deluge of data. This is the primary fuel of the new age, which powerful computational processes or analytics algorithms are using to generate valuable knowledge for enhanced decision-making, and deep insights pertaining to a wide variety of practical uses and applications. This book addresses the complex interplay of the scientific, technological, and social dimensions of the city, and what it entails in terms of the systemic implications for smart sustainable urbanism. In concrete terms, it explores the interdisciplinary and transdisciplinary field of smart sustainable urbanism and the unprecedented paradigmatic shifts and practical advances it is undergoing in light of big data science and analytics. This new era of science and technology embodies an unprecedentedly transformative and constitutive power—manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing social practices, producing new discourses, catalyzing major shifts, and fostering societal transitions. Of particular relevance, it is instigating a massive change in the way both smart cities and sustainable cities are studied and understood, and in how they are planned, designed, operated, managed, and governed in the face of urbanization. This relates to what has been dubbed data-driven smart sustainable urbanism, an emerging approach based on a computational understanding of city systems and processes that reduces urban life to logical and algorithmic rules and procedures, while also harnessing urban big data to provide a more holistic and integrated view or synoptic intelligence of the city. This is increasingly being directed towards improving, advancing, and maintaining the contribution of both sustainable cities and smart cities to the goals of sustainable development. This timely and multifaceted book is aimed at a broad readership. As such, it will appeal to urban scientists, data scientists, urbanists, planners, engineers, designers, policymakers, philosophers of science, and futurists, as well as all readers interested in an overview of the pivotal role of big data science and analytics in advancing every academic discipline and social practice concerned with data–intensive science and its application, particularly in relation to sustainability.

Research Handbook on Big Data Law

Research Handbook on Big Data Law
Author : Roland Vogl
Publisher : Edward Elgar Publishing
Release Date : 2021-05-28
Category : Law
Total pages :544
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This state-of-the-art Research Handbook provides an overview of research into, and the scope of current thinking in, the field of big data analytics and the law. It contains a wealth of information to survey the issues surrounding big data analytics in legal settings, as well as legal issues concerning the application of big data techniques in different domains.

Computational Intelligent Data Analysis for Sustainable Development

Computational Intelligent Data Analysis for Sustainable Development
Author : Ting Yu,Nitesh Chawla,Simeon Simoff
Publisher : CRC Press
Release Date : 2016-04-19
Category : Business & Economics
Total pages :440
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Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development present

Knowledge for Sustainable Development

Knowledge for Sustainable Development
Author : Unesco
Publisher : Oxford : EOLSS Publishers/UNESCO
Release Date : 2002
Category : Business & Economics
Total pages :1226
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This three volume set presents a multidisciplinary examination of the global life support systems on which we depend by providing a selection of articles on sustainable development issues written by international experts. Volume 1 focuses on the earth and atmospheric sciences, mathematical, biological and medical sciences, social sciences and humanities, physical sciences, engineering and technology resources. Volume 2 covers chemical sciences, energy science and water engineering, as well as the main issues related to environmental sciences and ecological resources. Volume 3 offers a comprehensive view of food and agricultural engineering resources, the management of human and natural resources, economic and institutional resources, information technology and systems management, as well as a regional overview of sustainable development issues. Each article includes a bibliography, a glossary and a guide to further information available as part of the on-line Encyclopedia version (http://www.eolss.net).

Data Science Landscape

Data Science Landscape
Author : Usha Mujoo Munshi,Neeta Verma
Publisher : Springer
Release Date : 2018-03-01
Category : Computers
Total pages :339
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The edited volume deals with different contours of data science with special reference to data management for the research innovation landscape. The data is becoming pervasive in all spheres of human, economic and development activity. In this context, it is important to take stock of what is being done in the data management area and begin to prioritize, consider and formulate adoption of a formal data management system including citation protocols for use by research communities in different disciplines and also address various technical research issues. The volume, thus, focuses on some of these issues drawing typical examples from various domains. The idea of this work germinated from the two day workshop on “Big and Open Data – Evolving Data Science Standards and Citation Attribution Practices”, an international workshop, led by the ICSU-CODATA and attended by over 300 domain experts. The Workshop focused on two priority areas (i) Big and Open Data: Prioritizing, Addressing and Establishing Standards and Good Practices and (ii) Big and Open Data: Data Attribution and Citation Practices. This important international event was part of a worldwide initiative led by ICSU, and the CODATA-Data Citation Task Group. In all, there are 21 chapters (with 21st Chapter addressing four different core aspects) written by eminent researchers in the field which deal with key issues of S&T, institutional, financial, sustainability, legal, IPR, data protocols, community norms and others, that need attention related to data management practices and protocols, coordinate area activities, and promote common practices and standards of the research community globally. In addition to the aspects touched above, the national / international perspectives of data and its various contours have also been portrayed through case studies in this volume.

Smart Sustainable Cities of the Future

Smart Sustainable Cities of the Future
Author : Simon Elias Bibri
Publisher : Springer
Release Date : 2018-02-24
Category : Political Science
Total pages :660
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This book is intended to help explore the field of smart sustainable cities in its complexity, heterogeneity, and breadth, the many faces of a topical subject of major importance for the future that encompasses so much of modern urban life in an increasingly computerized and urbanized world. Indeed, sustainable urban development is currently at the center of debate in light of several ICT visions becoming achievable and deployable computing paradigms, and shaping the way cities will evolve in the future and thus tackle complex challenges. This book integrates computer science, data science, complexity science, sustainability science, system thinking, and urban planning and design. As such, it contains innovative computer–based and data–analytic research on smart sustainable cities as complex and dynamic systems. It provides applied theoretical contributions fostering a better understanding of such systems and the synergistic relationships between the underlying physical and informational landscapes. It offers contributions pertaining to the ongoing development of computer–based and data science technologies for the processing, analysis, management, modeling, and simulation of big and context data and the associated applicability to urban systems that will advance different aspects of sustainability. This book seeks to explicitly bring together the smart city and sustainable city endeavors, and to focus on big data analytics and context-aware computing specifically. In doing so, it amalgamates the design concepts and planning principles of sustainable urban forms with the novel applications of ICT of ubiquitous computing to primarily advance sustainability. Its strength lies in combining big data and context–aware technologies and their novel applications for the sheer purpose of harnessing and leveraging the disruptive and synergetic effects of ICT on forms of city planning that are required for future forms of sustainable development. This is because the effects of such technologies reinforce one another as to their efforts for transforming urban life in a sustainable way by integrating data–centric and context–aware solutions for enhancing urban systems and facilitating coordination among urban domains. This timely and comprehensive book is aimed at a wide audience across science, academia industry, and policymaking. It provides the necessary material to inform relevant research communities of the state–of–the–art research and the latest development in the area of smart sustainable urban development, as well as a valuable reference for planners, designers, strategists, and ICT experts who are working towards the development and implementation of smart sustainable cities based on big data analytics and context–aware computing.

Environmental Science

Environmental Science
Author : Daniel B. Botkin,Edward A. Keller,Dorothy B. Rosenthal
Publisher : John Wiley & Sons Incorporated
Release Date : 2003
Category : Science
Total pages :668
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A guide to environmental science that provides information on various environmental issues, ecosystem management, biological diversity, the atmosphere and climate, air pollution, ozone depletion,waste management, and other related topics.

Which Degree Guide

Which Degree Guide
Author : Anonim
Publisher : Unknown
Release Date : 2001
Category : Degrees, Academic
Total pages :129
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Open Data and Energy Analytics

Open Data and Energy Analytics
Author : Benedetto Nastasi,Massimiliano Manfren,Michel Noussan
Publisher : MDPI
Release Date : 2020-06-25
Category : Science
Total pages :218
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Open data and policy implications coming from data-aware planning entail collection and pre- and postprocessing as operations of primary interest. Before these steps, making data available to people and their decision-makers is a crucial point. Referring to the relationship between data and energy, public administrations, governments, and research bodies are promoting the construction of reliable and robust datasets to pursue policies coherent with the Sustainable Development Goals, as well as to allow citizens to make informed choices. Energy engineers and planners must provide the simplest and most robust tools to collect, process, and analyze data in order to offer solid data-based evidence for future projections in building, district, and regional systems planning. This Special Issue aims at providing the state-of-the-art on open-energy data analytics; its availability in the different contexts, i.e., country peculiarities; and its availability at different scales, i.e., building, district, and regional for data-aware planning and policy-making. For all the aforementioned reasons, we encourage researchers to share their original works on the field of open data and energy analytics. Topics of primary interest include but are not limited to the following: 1. Open data and energy sustainability; 2. Open data science and energy planning; 3. Open science and open governance for sustainable development goals; 4. Key performance indicators of data-aware energy modelling, planning, and policy; 5. Energy, water, and sustainability database for building, district, and regional systems; 6. Best practices and case studies.

Journal of Borderlands Studies

Journal of Borderlands Studies
Author : Anonim
Publisher : Unknown
Release Date : 2003
Category : Borderlands
Total pages :129
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Managing Data Science

Managing Data Science
Author : Kirill Dubovikov
Publisher : Packt Publishing Ltd
Release Date : 2019-11-12
Category : Computers
Total pages :290
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Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key Features Learn the basics of data science and explore its possibilities and limitations Manage data science projects and assemble teams effectively even in the most challenging situations Understand management principles and approaches for data science projects to streamline the innovation process Book Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learn Understand the underlying problems of building a strong data science pipeline Explore the different tools for building and deploying data science solutions Hire, grow, and sustain a data science team Manage data science projects through all stages, from prototype to production Learn how to use ModelOps to improve your data science pipelines Get up to speed with the model testing techniques used in both development and production stages Who this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book.

Handbook of Sustainable Development Planning

Handbook of Sustainable Development Planning
Author : M. A. Quaddus,Muhammed Abu B. Siddique
Publisher : Edward Elgar Pub
Release Date : 2004
Category : Business & Economics
Total pages :347
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"Given the rise in prominence of sustainable development planning in recent years, this Handbook will be invaluable to a wide-ranging audience including NGOs, planners, consultants, policymakers, and academics."--Jacket.

Analytics

Analytics
Author : Daniel Covington
Publisher : Lulu.com
Release Date : 2019-08-13
Category : Business & Economics
Total pages :288
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SO MANY PEOPLE DREAM OF BECOMING THEIR OWN BOSS OR SUCCEEDING IN THEIR CHOSEN PROFESSION, AND WITH THE RESOURCES AVAILABLE TODAY, MORE ENTREPRENEURS AND PROFESSIONALS ARE ACHIEVING GREAT SUCCESS! HOWEVER, SUCCESS SHOULD BE DEFINED FOR THE LONG TERM, AND AS OPPORTUNITIES START TO GROW, SO DOES THE COMPETITION. Getting your business up and running or starting on your career path is one thing, but have a sustainable business or career is completely another. Many people make the mistake of making plans but having no follow-through. This is where analytics comes in. DonÕt you wish to have the power to know what your target consumers are thinking? WonÕt you want to have a preview of what future trends to expect in the market you are in? Well, this book is just the one you need. This book will teach you, in simple and easy-to-understand terms, how to take advantage of data from your daily operations and make such data a powerful tool that can influence how well your business does over time.

Advanced Intelligent Systems for Sustainable Development (AI2SD’2019)

Advanced Intelligent Systems for Sustainable Development (AI2SD’2019)
Author : Mostafa Ezziyyani
Publisher : Springer Nature
Release Date : 2020-02-05
Category : Technology & Engineering
Total pages :521
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This book gathers papers presented at the second installment of the International Conference on Advanced Intelligent Systems for Sustainable Development (AI2SD-2019), which was held on July 08–11, 2019 in Marrakech, Morocco. It offers comprehensive coverage of recent advances in big data, data analytics and related paradigms. The book consists of fifty-two chapters, each of which shares the latest research in the fields of big data and data science, and describes use cases and applications of big data technologies in various domains, such as social networks and health care. All parts of the book discuss open research problems and potential opportunities that have arisen from the rapid advances in big data technologies. In addition, the book surveys the state of the art in data science, and provides practical guidance on big data analytics and data science. Expert perspectives are provided by authoritative researchers and practitioners from around the world, who discuss research developments and emerging trends, present case studies on helpful frameworks and innovative methodologies, and suggest best practices for efficient and effective data analytics. Chiefly intended for researchers, IT professionals and graduate students, the book represents a timely contribution to the growing field of big data, which has been recognized as one of the leading emerging technologies that will have a major impact on various fields of science and various aspects of human society over the next several decades. Therefore, the content in this book is an essential tool to help readers understand current developments, and provides them with an extensive 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 health care, networks, the Internet of Things, big data standards, benchmarking of systems, and others. In addition to a diverse range of applications, key algorithmic approaches such as graph partitioning, clustering and finite mixture modeling of high-dimensional data are also covered. The varied collection of topics addressed introduces readers to the richness of the emerging field of big data analytics.