Data Science Applied to Sustainability Analysis

Data Science Applied to Sustainability Analysis
Author: Jennifer Dunn,Prasanna Balaprakash
Publsiher: Elsevier
Total Pages: 310
Release: 2021-05-11
ISBN 10: 0128179775
ISBN 13: 9780128179772
Language: EN, FR, DE, ES & NL

Data Science Applied to Sustainability Analysis Book Review:

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

Computational Intelligent Data Analysis for Sustainable Development

Computational Intelligent Data Analysis for Sustainable Development
Author: Ting Yu,Nitesh Chawla,Simeon Simoff
Publsiher: CRC Press
Total Pages: 440
Release: 2016-04-19
ISBN 10: 1439895953
ISBN 13: 9781439895955
Language: EN, FR, DE, ES & NL

Computational Intelligent Data Analysis for Sustainable Development Book Review:

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

Environmental Data Analysis with MatLab

Environmental Data Analysis with MatLab
Author: William Menke,Joshua Ephraim Menke
Publsiher: Elsevier
Total Pages: 263
Release: 2012
ISBN 10: 0123918863
ISBN 13: 9780123918864
Language: EN, FR, DE, ES & NL

Environmental Data Analysis with MatLab Book Review:

Environmental Data Analysis with MatLab is for students and researchers working to analyze real data sets in the environmental sciences. One only has to consider the global warming debate to realize how critically important it is to be able to derive clear conclusions from often-noisy data drawn from a broad range of sources. This book teaches the basics of the underlying theory of data analysis, and then reinforces that knowledge with carefully chosen, realistic scenarios. MatLab, a commercial data processing environment, is used in these scenarios; significant content is devoted to teaching how it can be effectively used in an environmental data analysis setting. The book, though written in a self-contained way, is supplemented with data sets and MatLab scripts that can be used as a data analysis tutorial. Author's website: http://www.ldeo.columbia.edu/users/menke/edawm/index.htm Well written and outlines a clear learning path for researchers and students Uses real world environmental examples and case studies MatLab software for application in a readily-available software environment Homework problems help user follow up upon case studies with homework that expands them

CIGOS 2021 Emerging Technologies and Applications for Green Infrastructure

CIGOS 2021  Emerging Technologies and Applications for Green Infrastructure
Author: Cuong Ha-Minh,Anh Minh Tang,Tinh Quoc Bui,Xuan Hong Vu,Dat Vu Khoa Huynh
Publsiher: Springer Nature
Total Pages: 1958
Release: 2021-10-28
ISBN 10: 9811671605
ISBN 13: 9789811671609
Language: EN, FR, DE, ES & NL

CIGOS 2021 Emerging Technologies and Applications for Green Infrastructure Book Review:

This book highlights the key role of green infrastructure (GI) in providing natural and ecosystem solutions, helping alleviate many of the environmental, social, and economic problems caused by rapid urbanization. The book gathers the emerging technologies and applications in various disciplines involving geotechnics, civil engineering, and structures, which are presented in numerous high-quality papers by worldwide researchers, practitioners, policymakers, and entrepreneurs at the 6th CIGOS event, 2021. Moreover, by sharing knowledge and experiences around emerging GI technologies and policy issues, the book aims at encouraging adoption of GI technologies as well as building capacity for implementing GI practices at all scales. This book is useful for researchers and professionals in designing, building, and managing sustainable buildings and infrastructure.

Novel AI and Data Science Advancements for Sustainability in the Era of COVID 19

Novel AI and Data Science Advancements for Sustainability in the Era of COVID 19
Author: Victor Chang,Mohamed Abdel-Basset,Muthu Ramachandran,Nicolas Green,Gary Wills
Publsiher: Academic Press
Total Pages: 298
Release: 2022-04-05
ISBN 10: 0323903789
ISBN 13: 9780323903783
Language: EN, FR, DE, ES & NL

Novel AI and Data Science Advancements for Sustainability in the Era of COVID 19 Book Review:

Novel AI and Data Science Advancements for Sustainability in the Era of COVID-19 discusses how the role of recent technologies applied to health settings can help fight virus outbreaks. Moreover, it provides guidelines on how governments and institutions should prepare and quickly respond to drastic situations using technology to support their communities in order to maintain life and functional as efficiently as possible. The book discusses topics such as AI-driven histopathology analysis for COVID-19 diagnosis, bioinformatics for subtype rational drug design, deep learning-based treatment evaluation and outcome prediction, sensor informatics for monitoring infected patients, and machine learning for tracking and prediction models. In addition, the book presents AI solutions for hospital management during an epidemic or pandemic, along with real-world solutions and case studies of successful measures to support different types of communities. This is a valuable source for medical informaticians, bioinformaticians, clinicians and other healthcare workers and researchers who are interested in learning more on how recently developed technologies can help us fight and minimize the effects of global pandemics. Discusses AI advancements in predictive and decision modeling and how to design mobile apps to track contagion spread Presents the smart contract concept in blockchain and cryptography technology to guarantee security and privacy of people’s data once their information has been used to fight the pandemic Encompasses guidelines for emergency preparedness, planning, recovery and continuity management of communities to support people in emergencies like a virus outbreak

Research Handbook on Big Data Law

Research Handbook on Big Data Law
Author: Roland Vogl
Publsiher: Edward Elgar Publishing
Total Pages: 544
Release: 2021-05-28
ISBN 10: 1788972821
ISBN 13: 9781788972826
Language: EN, FR, DE, ES & NL

Research Handbook on Big Data Law Book Review:

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.

Statistical Data Analysis Explained

Statistical Data Analysis Explained
Author: Clemens Reimann,Peter Filzmoser,Robert Garrett,Rudolf Dutter
Publsiher: John Wiley & Sons
Total Pages: 362
Release: 2011-08-31
ISBN 10: 1119965284
ISBN 13: 9781119965282
Language: EN, FR, DE, ES & NL

Statistical Data Analysis Explained Book Review:

Few books on statistical data analysis in the natural sciences are written at a level that a non-statistician will easily understand. This is a book written in colloquial language, avoiding mathematical formulae as much as possible, trying to explain statistical methods using examples and graphics instead. To use the book efficiently, readers should have some computer experience. The book starts with the simplest of statistical concepts and carries readers forward to a deeper and more extensive understanding of the use of statistics in environmental sciences. The book concerns the application of statistical and other computer methods to the management, analysis and display of spatial data. These data are characterised by including locations (geographic coordinates), which leads to the necessity of using maps to display the data and the results of the statistical methods. Although the book uses examples from applied geochemistry, and a large geochemical survey in particular, the principles and ideas equally well apply to other natural sciences, e.g., environmental sciences, pedology, hydrology, geography, forestry, ecology, and health sciences/epidemiology. The book is unique because it supplies direct access to software solutions (based on R, the Open Source version of the S-language for statistics) for applied environmental statistics. For all graphics and tables presented in the book, the R-scripts are provided in the form of executable R-scripts. In addition, a graphical user interface for R, called DAS+R, was developed for convenient, fast and interactive data analysis. Statistical Data Analysis Explained: Applied Environmental Statistics with R provides, on an accompanying website, the software to undertake all the procedures discussed, and the data employed for their description in the book.

Methods in Sustainability Science

Methods in Sustainability Science
Author: Jingzheng Ren
Publsiher: Elsevier
Total Pages: 444
Release: 2021-08-05
ISBN 10: 012824240X
ISBN 13: 9780128242407
Language: EN, FR, DE, ES & NL

Methods in Sustainability Science Book Review:

Methods in Sustainability Science: Assessment, Prioritization, Improvement, Design and Optimization presents cutting edge, detailed methodologies needed to create sustainable growth in any field or industry, including life cycle assessments, building design, and energy systems. The book utilized a systematic structured approach to each of the methodologies described in an interdisciplinary way to ensure the methodologies are applicable in the real world, including case studies to demonstrate the methods. The chapters are written by a global team of authors in a variety of sustainability related fields. Methods in Sustainability Science: Assessment, Prioritization, Improvement, Design and Optimization will provide academics, researchers and practitioners in sustainability, especially environmental science and environmental engineering, with the most recent methodologies needed to maintain a sustainable future. It is also a necessary read for postgraduates in sustainability, as well as academics and researchers in energy and chemical engineering who need to ensure their industrial methodologies are sustainable. Provides a comprehensive overview of the most recent methodologies in sustainability assessment, prioritization, improvement, design and optimization Sections are organized in a systematic and logical way to clearly present the most recent methodologies for sustainability and the chapters utilize an interdisciplinary approach that covers all considerations of sustainability Includes detailed case studies demonstrating the efficacies of the described methods

Sustainability Metrics and Indicators of Environmental Impact

Sustainability Metrics and Indicators of Environmental Impact
Author: Eduardo Jacob-Lopes,Leila Queiroz Zepka,Mariany Costa Depra
Publsiher: Elsevier
Total Pages: 212
Release: 2021-07-16
ISBN 10: 0128236043
ISBN 13: 9780128236048
Language: EN, FR, DE, ES & NL

Sustainability Metrics and Indicators of Environmental Impact Book Review:

Sustainability Metrics and Indicators of Environmental Impact: Industrial and Agricultural Life Cycle Assessment covers trending topics on the environmental impact of systems of production, putting emphasis on lifecycle assessment (LCA). This methodology is one of the most important tools of analysis, as mathematical models are applied that will quantify the systematic inputs and outputs of the processes in order to evaluate the sustainability of industrial processes and products. In this sense, LCA is mainly a tool to support environmental decision-making that analyzes the environmental impacts of products and technologies from a lifecycle perspective. The emergence of ever-larger global issues, such as the energy dilemma, the changing climate and the scarcity of natural resources, such as water, has boosted the search for tools capable of ensuring the reliability of the results published by the industries, and has become an important tool in order to achieve sustainability and environmental preservation. Thus, lifecycle assessment (LCA), including carbon footprint valuation is necessary to ensure better internal management. Provides guidance on environmental impacts and the carbon footprint of industrial processes Features guidelines in lifecycle assessment to support a sustainable approach, along with quantifiable data to support proposed solutions Includes a companion website with slides and graphics to quantity environmental impact and other metrics of lifecycle assessment

Earth System Analysis for Sustainability

Earth System Analysis for Sustainability
Author: Dahlem Konferenzen
Publsiher: MIT Press
Total Pages: 454
Release: 2004
ISBN 10: 9780262195133
ISBN 13: 0262195135
Language: EN, FR, DE, ES & NL

Earth System Analysis for Sustainability Book Review:

This book presents the complete story of the inseparably intertwined evolution of life and matter on earth, focussing on four major topics. It analyzes the driving forces behind global change and uses this knowledge to propose principles for global stewardship.

Encyclopedia of Data Science and Machine Learning

Encyclopedia of Data Science and Machine Learning
Author: John Wang
Publsiher: Engineering Science Reference
Total Pages: 135
Release: 2022
ISBN 10: 9781799892205
ISBN 13: 1799892204
Language: EN, FR, DE, ES & NL

Encyclopedia of Data Science and Machine Learning Book Review:

"This book examines current, state-of-the-art research in the areas of data science, machine learning, data mining, optimization, artificial intelligence, statistics, and the interactions, linkages, and applications of knowledge-based business with information systems"--

Data Science Without Makeup

Data Science Without Makeup
Author: Mikhail Zhilkin
Publsiher: CRC Press
Total Pages: 194
Release: 2021-11-02
ISBN 10: 1000464806
ISBN 13: 9781000464801
Language: EN, FR, DE, ES & NL

Data Science Without Makeup Book Review:

Mikhail Zhilkin, a data scientist who has worked on projects ranging from Candy Crush games to Premier League football players’ physical performance, shares his strong views on some of the best and, more importantly, worst practices in data analytics and business intelligence. Why data science is hard, what pitfalls analysts and decision-makers fall into, and what everyone involved can do to give themselves a fighting chance—the book examines these and other questions with the skepticism of someone who has seen the sausage being made. Honest and direct, full of examples from real life, Data Science Without Makeup: A Guidebook for End-Users, Analysts and Managers will be of great interest to people who aspire to work with data, people who already work with data, and people who work with people who work with data—from students to professional researchers and from early-career to seasoned professionals. Mikhail Zhilkin is a data scientist at Arsenal FC. He has previously worked on the popular Candy Crush mobile games and in sports betting.

Sustainability Analysis

Sustainability Analysis
Author: Stanislav Shmelev,Irina Shmeleva
Publsiher: Palgrave Macmillan
Total Pages: 335
Release: 2012-01-27
ISBN 10: 0230355242
ISBN 13: 9780230355248
Language: EN, FR, DE, ES & NL

Sustainability Analysis Book Review:

Since the United Nations Conference on Environment and Development in Rio de Janeiro in 1992, the issues of sustainability at the international, national and regional level have become a top priority for national governments, business leaders and NGOs. Sustainability Analysis: An Interdisciplinary Approach is the result of collective reflection by an international group of academics from Canada, France, Norway, Russia, Sweden, Switzerland, and the UK. It was inspired by the interdisciplinary discussions started in St Petersburg, Russia at the conference Globalisation, New Economy, and the Environment: Business and Society Challenges for Sustainable Development, organized by the editors of this volume under the auspices of the International Society for Ecological Economics in 2005. This book explores the major actors, paradigms and ideologies in sustainable development, employing novel approaches such as linguistic and discourse analysis as well as simulation games and the psychology of ecological consciousness to provide an important contribution to the environmental policy field.

Statistical Foundations of Data Science

Statistical Foundations of Data Science
Author: Jianqing Fan,Runze Li,Cun-Hui Zhang,Hui Zou
Publsiher: CRC Press
Total Pages: 752
Release: 2020-09-21
ISBN 10: 1466510854
ISBN 13: 9781466510852
Language: EN, FR, DE, ES & NL

Statistical Foundations of Data Science Book Review:

Statistical Foundations of Data Science gives a thorough introduction to commonly used statistical models, contemporary statistical machine learning techniques and algorithms, along with their mathematical insights and statistical theories. It aims to serve as a graduate-level textbook and a research monograph on high-dimensional statistics, sparsity and covariance learning, machine learning, and statistical inference. It includes ample exercises that involve both theoretical studies as well as empirical applications. The book begins with an introduction to the stylized features of big data and their impacts on statistical analysis. It then introduces multiple linear regression and expands the techniques of model building via nonparametric regression and kernel tricks. It provides a comprehensive account on sparsity explorations and model selections for multiple regression, generalized linear models, quantile regression, robust regression, hazards regression, among others. High-dimensional inference is also thoroughly addressed and so is feature screening. The book also provides a comprehensive account on high-dimensional covariance estimation, learning latent factors and hidden structures, as well as their applications to statistical estimation, inference, prediction and machine learning problems. It also introduces thoroughly statistical machine learning theory and methods for classification, clustering, and prediction. These include CART, random forests, boosting, support vector machines, clustering algorithms, sparse PCA, and deep learning.

Applied Machine Learning for Smart Data Analysis

Applied Machine Learning for Smart Data Analysis
Author: Nilanjan Dey,Sanjeev Wagh,Parikshit N. Mahalle,Mohd. Shafi Pathan
Publsiher: CRC Press
Total Pages: 225
Release: 2019-05-20
ISBN 10: 0429804571
ISBN 13: 9780429804571
Language: EN, FR, DE, ES & NL

Applied Machine Learning for Smart Data Analysis Book Review:

The book focuses on how machine learning and the Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Ontologies that are used in heterogeneous IoT environments have been discussed including interpretation, context awareness, analyzing various data sources, machine learning algorithms and intelligent services and applications. Further, it includes unsupervised and semi-supervised machine learning techniques with study of semantic analysis and thorough analysis of reviews. Divided into sections such as machine learning, security, IoT and data mining, the concepts are explained with practical implementation including results. Key Features Follows an algorithmic approach for data analysis in machine learning Introduces machine learning methods in applications Address the emerging issues in computing such as deep learning, machine learning, Internet of Things and data analytics Focuses on machine learning techniques namely unsupervised and semi-supervised for unseen and seen data sets Case studies are covered relating to human health, transportation and Internet applications

Sustainability in the Design Synthesis and Analysis of Chemical Engineering Processes

Sustainability in the Design  Synthesis and Analysis of Chemical Engineering Processes
Author: Gerardo Ruiz Mercado,Heriberto Cabezas
Publsiher: Butterworth-Heinemann
Total Pages: 426
Release: 2016-07-29
ISBN 10: 0128020644
ISBN 13: 9780128020647
Language: EN, FR, DE, ES & NL

Sustainability in the Design Synthesis and Analysis of Chemical Engineering Processes Book Review:

Sustainability in the Design, Synthesis and Analysis of Chemical Engineering Processes is an edited collection of contributions from leaders in their field. It takes a holistic view of sustainability in chemical and process engineering design, and incorporates economic analysis and human dimensions. Ruiz-Mercado and Cabezas have brought to this book their experience of researching sustainable process design and life cycle sustainability evaluation to assist with development in government, industry and academia. This book takes a practical, step-by-step approach to designing sustainable plants and processes by starting from chemical engineering fundamentals. This method enables readers to achieve new process design approaches with high influence and less complexity. It will also help to incorporate sustainability at the early stages of project life, and build up multiple systems level perspectives. Ruiz-Mercado and Cabezas’ book is the only book on the market that looks at process sustainability from a chemical engineering fundamentals perspective. Improve plants, processes and products with sustainability in mind; from conceptual design to life cycle assessment Avoid retro fitting costs by planning for sustainability concerns at the start of the design process Link sustainability to the chemical engineering fundamentals

Handbook of Sustainability Science and Research

Handbook of Sustainability Science and Research
Author: Walter Leal Filho
Publsiher: Springer
Total Pages: 991
Release: 2017-10-03
ISBN 10: 3319630075
ISBN 13: 9783319630076
Language: EN, FR, DE, ES & NL

Handbook of Sustainability Science and Research Book Review:

This multidisciplinary handbook explores concrete case studies which illustrate how sustainability science and research can contribute to the realization of the goals of the 2030 Agenda for Sustainable Development. It contains contributions from sustainability researchers from across the world.

Advanced Data Science and Analytics with Python

Advanced Data Science and Analytics with Python
Author: Jesus Rogel-Salazar
Publsiher: CRC Press
Total Pages: 384
Release: 2020-05-07
ISBN 10: 0429822316
ISBN 13: 9780429822315
Language: EN, FR, DE, ES & NL

Advanced Data Science and Analytics with Python Book Review:

Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications. Features: Targets readers with a background in programming, who are interested in the tools used in data analytics and data science Uses Python throughout Presents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needs Focuses on the practical use of the tools rather than on lengthy explanations Provides the reader with the opportunity to use the book whenever needed rather than following a sequential path The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book. Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences – in this case, literally to the users’ fingertips in the form of an iPhone app. About the Author Dr. Jesús Rogel-Salazar is a lead data scientist in the field, working for companies such as Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.

Big Data and Social Science

Big Data and Social Science
Author: Ian Foster,Rayid Ghani,Ron S. Jarmin,Frauke Kreuter,Julia Lane
Publsiher: CRC Press
Total Pages: 391
Release: 2020-11-18
ISBN 10: 100020863X
ISBN 13: 9781000208634
Language: EN, FR, DE, ES & NL

Big Data and Social Science Book Review:

Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the field of data science provide a unique perspective on how to apply modern social science research principles and current analytical and computational tools. The text teaches you how to identify and collect appropriate data, apply data science methods and tools to the data, and recognize and respond to data errors, biases, and limitations. Features Takes an accessible, hands-on approach to handling new types of data in the social sciences Presents the key data science tools in a non-intimidating way to both social and data scientists while keeping the focus on research questions and purposes Illustrates social science and data science principles through real-world problems Links computer science concepts to practical social science research Promotes good scientific practice Provides freely available data and code as well as practical programming exercises through Binder and GitHub New to the Second Edition Increased use of examples from different areas of social sciences New chapter on dealing with Bias and Fairness in Machine Learning models Expanded chapters focusing on Machine Learning and Text Analysis Revamped hands-on Jupyter notebooks to reinforce concepts covered in each chapter This classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners. It empowers you to use the massive and rapidly growing amounts of available data to interpret economic and social activities in a scientific and rigorous manner.

Object Oriented Data Analysis

Object Oriented Data Analysis
Author: J. S. Marron,Ian L. Dryden
Publsiher: CRC Press
Total Pages: 436
Release: 2021-11-18
ISBN 10: 1351189662
ISBN 13: 9781351189668
Language: EN, FR, DE, ES & NL

Object Oriented Data Analysis Book Review:

Object Oriented Data Analysis is a framework that facilitates inter-disciplinary research through new terminology for discussing the often many possible approaches to the analysis of complex data. Such data are naturally arising in a wide variety of areas. This book aims to provide ways of thinking that enable the making of sensible choices. The main points are illustrated with many real data examples, based on the authors' personal experiences, which have motivated the invention of a wide array of analytic methods. While the mathematics go far beyond the usual in statistics (including differential geometry and even topology), the book is aimed at accessibility by graduate students. There is deliberate focus on ideas over mathematical formulas. J. S. Marron is the Amos Hawley Distinguished Professor of Statistics, Professor of Biostatistics, Adjunct Professor of Computer Science, Faculty Member of the Bioinformatics and Computational Biology Curriculum and Research Member of the Lineberger Cancer Center and the Computational Medicine Program, at the University of North Carolina, Chapel Hill. Ian L. Dryden is a Professor in the Department of Mathematics and Statistics at Florida International University in Miami, has served as Head of School of Mathematical Sciences at the University of Nottingham, and is joint author of the acclaimed book Statistical Shape Analysis.