- Table View
- List View
Data Science for Public Policy (Springer Series in the Data Sciences)
by Jeffrey C. Chen Edward A. Rubin Gary J. CornwallThis textbook presents the essential tools and core concepts of data science to public officials, policy analysts, and economists among others in order to further their application in the public sector. An expansion of the quantitative economics frameworks presented in policy and business schools, this book emphasizes the process of asking relevant questions to inform public policy. Its techniques and approaches emphasize data-driven practices, beginning with the basic programming paradigms that occupy the majority of an analyst’s time and advancing to the practical applications of statistical learning and machine learning. The text considers two divergent, competing perspectives to support its applications, incorporating techniques from both causal inference and prediction. Additionally, the book includes open-sourced data as well as live code, written in R and presented in notebook form, which readers can use and modify to practice working with data.
Data Science for Sensory and Consumer Scientists (Chapman & Hall/CRC Data Science Series)
by Thierry Worch Julien Delarue Vanessa Rios De Souza John EnnisData Science for Sensory and Consumer Scientists is a comprehensive textbook that provides a practical guide to using data science in the field of sensory and consumer science through real-world applications. It covers key topics including data manipulation, preparation, visualization, and analysis, as well as automated reporting, machine learning, text analysis, and dashboard creation. Written by leading experts in the field, this book is an essential resource for anyone looking to master the tools and techniques of data science and apply them to the study of consumer behavior and sensory-led product development. Whether you are a seasoned professional or a student just starting out, this book is the ideal guide to using data science to drive insights and inform decision-making in the sensory and consumer sciences. Key Features: • Elucidation of data scientific workflow. • Introduction to reproducible research. • In-depth coverage of data-scientific topics germane to sensory and consumer science. • Examples based in industrial practice used throughout the book
Data Science for Social Good: Philanthropy and Social Impact in a Complex World (SpringerBriefs in Complexity)
by Massimo Lapucci Ciro CattutoThis book is a collection of reflections by thought leaders at first-mover organizations in the exploding field of "Data Science for Social Good", meant as the application of knowledge from computer science, complex systems and computational social science to challenges such as humanitarian response, public health, sustainable development. The book provides both an overview of scientific approaches to social impact – identifying a social need, targeting an intervention, measuring impact – and the complementary perspective of funders and philanthropies that are pushing forward this new sector. This book will appeal to students and researchers in the rapidly growing field of data science for social impact, to data scientists at companies whose data could be used to generate more public value, and to decision makers at nonprofits, foundations, and agencies that are designing their own agenda around data.
Data Science for Transport: A Self-study Guide With Computer Exercises (Springer Textbooks In Earth Sciences, Geography And Environment Ser.)
by Charles FoxThe quantity, diversity and availability of transport data is increasing rapidly, requiring new skills in the management and interrogation of data and databases. Recent years have seen a new wave of 'big data', 'Data Science', and 'smart cities' changing the world, with the Harvard Business Review describing Data Science as the "sexiest job of the 21st century". Transportation professionals and researchers need to be able to use data and databases in order to establish quantitative, empirical facts, and to validate and challenge their mathematical models, whose axioms have traditionally often been assumed rather than rigorously tested against data. This book takes a highly practical approach to learning about Data Science tools and their application to investigating transport issues. The focus is principally on practical, professional work with real data and tools, including business and ethical issues."Transport modeling practice was developed in a data poor world, and many of our current techniques and skills are building on that sparsity. In a new data rich world, the required tools are different and the ethical questions around data and privacy are definitely different. I am not sure whether current professionals have these skills; and I am certainly not convinced that our current transport modeling tools will survive in a data rich environment. This is an exciting time to be a data scientist in the transport field. We are trying to get to grips with the opportunities that big data sources offer; but at the same time such data skills need to be fused with an understanding of transport, and of transport modeling. Those with these combined skills can be instrumental at providing better, faster, cheaper data for transport decision- making; and ultimately contribute to innovative, efficient, data driven modeling techniques of the future. It is not surprising that this course, this book, has been authored by the Institute for Transport Studies. To do this well, you need a blend of academic rigor and practical pragmatism. There are few educational or research establishments better equipped to do that than ITS Leeds". - Tom van Vuren, Divisional Director, Mott MacDonald"WSP is proud to be a thought leader in the world of transport modelling, planning and economics, and has a wide range of opportunities for people with skills in these areas. The evidence base and forecasts we deliver to effectively implement strategies and schemes are ever more data and technology focused a trend we have helped shape since the 1970's, but with particular disruption and opportunity in recent years. As a result of these trends, and to suitably skill the next generation of transport modellers, we asked the world-leading Institute for Transport Studies, to boost skills in these areas, and they have responded with a new MSc programme which you too can now study via this book." - Leighton Cardwell, Technical Director, WSP."From processing and analysing large datasets, to automation of modelling tasks sometimes requiring different software packages to "talk" to each other, to data visualization, SYSTRA employs a range of techniques and tools to provide our clients with deeper insights and effective solutions. This book does an excellent job in giving you the skills to manage, interrogate and analyse databases, and develop powerful presentations. Another important publication from ITS Leeds." - Fitsum Teklu, Associate Director (Modelling & Appraisal) SYSTRA Ltd"Urban planning has relied for decades on statistical and computational practices that have little to do with mainstream data science. Information is still often used as evidence on the impact of new infrastructure even when it hardly contains any valid evidence. This book is an extremely welcome effort to provide young professionals with the skills needed to analyse how cities and transport networks actually work. The book is also highly relevant to anyone who will later want to build digital solutions to optimise urban travel based on emerging data sources". - Yaron Hollander, author of "T
Data Science for Web3: A comprehensive guide to decoding blockchain data with data analysis basics and machine learning cases
by Gabriela Castillo ArecoBe part of the future of Web3, decoding blockchain data to build trust in the next-generation internetKey FeaturesBuild a deep understanding of the fundamentals of blockchain analyticsExtract actionable business insights by modeling blockchain dataShowcase your work and gain valuable experience to seize opportunities in the Web3 ecosystemPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionData is the new oil and Web3 is generating it at an unprecedented rate. Complete with practical examples, detailed explanations, and ideas for portfolio development, this comprehensive book serves as a step-by-step guide covering the industry best practices, tools, and resources needed to easily navigate the world of data in Web3. You’ll begin by acquiring a solid understanding of key blockchain concepts and the fundamental data science tools essential for Web3 projects. The subsequent chapters will help you explore the main data sources that can help address industry challenges, decode smart contracts, and build DeFi- and NFT-specific datasets. You’ll then tackle the complexities of feature engineering specific to blockchain data and familiarize yourself with diverse machine learning use cases that leverage Web3 data. The book includes interviews with industry leaders providing insights into their professional journeys to drive innovation in the Web 3 environment. Equipped with experience in handling crypto data, you’ll be able to demonstrate your skills in job interviews, academic pursuits, or when engaging potential clients. By the end of this book, you’ll have the essential tools to undertake end-to-end data science projects utilizing blockchain data, empowering you to help shape the next-generation internet.What you will learnUnderstand the core components of blockchain transactions and blocksIdentify reliable sources of on-chain and off-chain data to build robust datasetsUnderstand key Web3 business questions and how data science can offer solutionsBuild your skills to create and query NFT- and DeFi-specific datasetsImplement a machine learning toolbox with real-world use cases in the Web3 spaceWho this book is forThis book is designed for data professionals—data analysts, data scientists, or data engineers— and business professionals, aiming to acquire the skills for extracting data from the Web3 ecosystem, as it demonstrates how to effectively leverage data tools for in-depth analysis of blockchain transactional data. If you seek hands-on experience, you'll find value in the shared repository, enabling you to experiment with the provided solutions. While not mandatory, a basic understanding of statistics, machine learning, and Python will enhance your learning experience.
Data Science for Wind Energy
by Yu DingData Science for Wind Energy provides an in-depth discussion on how data science methods can improve decision making for wind energy applications, near-ground wind field analysis and forecast, turbine power curve fitting and performance analysis, turbine reliability assessment, and maintenance optimization for wind turbines and wind farms. A broad set of data science methods covered, including time series models, spatio-temporal analysis, kernel regression, decision trees, kNN, splines, Bayesian inference, and importance sampling. More importantly, the data science methods are described in the context of wind energy applications, with specific wind energy examples and case studies. Please also visit the author’s book site at https://aml.engr.tamu.edu/book-dswe.Features Provides an integral treatment of data science methods and wind energy applications Includes specific demonstration of particular data science methods and their use in the context of addressing wind energy needs Presents real data, case studies and computer codes from wind energy research and industrial practice Covers material based on the author's ten plus years of academic research and insights
Data Science Foundations: Geometry and Topology of Complex Hierarchic Systems and Big Data Analytics (Chapman & Hall/CRC Computer Science & Data Analysis)
by Fionn Murtagh"Data Science Foundations is most welcome and, indeed, a piece of literature that the field is very much in need of…quite different from most data analytics texts which largely ignore foundational concepts and simply present a cookbook of methods…a very useful text and I would certainly use it in my teaching."- Mark Girolami, Warwick University Data Science encompasses the traditional disciplines of mathematics, statistics, data analysis, machine learning, and pattern recognition. This book is designed to provide a new framework for Data Science, based on a solid foundation in mathematics and computational science. It is written in an accessible style, for readers who are engaged with the subject but not necessarily experts in all aspects. It includes a wide range of case studies from diverse fields, and seeks to inspire and motivate the reader with respect to data, associated information, and derived knowledge.
The Data Science Framework: A View from the EDISON Project
by Juan J. Cuadrado-Gallego Yuri DemchenkoThis edited book first consolidates the results of the EU-funded EDISON project (Education for Data Intensive Science to Open New science frontiers), which developed training material and information to assist educators, trainers, employers, and research infrastructure managers in identifying, recruiting and inspiring the data science professionals of the future. It then deepens the presentation of the information and knowledge gained to allow for easier assimilation by the reader.The contributed chapters are presented in sequence, each chapter picking up from the end point of the previous one. After the initial book and project overview, the chapters present the relevant data science competencies and body of knowledge, the model curriculum required to teach the required foundations, profiles of professionals in this domain, and use cases and applications. The text is supported with appendices on related process models.The book can be used to develop new courses in data science, evaluate existing modules and courses, draft job descriptions, and plan and design efficient data-intensive research teams across scientific disciplines.
Data Science: From Research to Application (Lecture Notes on Data Engineering and Communications Technologies #45)
by Zahra Narimani Mahdi Bohlouli Bahram Sadeghi Bigham Mahdi Vasighi Ebrahim AnsariThis book presents outstanding theoretical and practical findings in data science and associated interdisciplinary areas. Its main goal is to explore how data science research can revolutionize society and industries in a positive way, drawing on pure research to do so. The topics covered range from pure data science to fake news detection, as well as Internet of Things in the context of Industry 4.0.Data science is a rapidly growing field and, as a profession, incorporates a wide variety of areas, from statistics, mathematics and machine learning, to applied big data analytics. According to Forbes magazine, “Data Science” was listed as LinkedIn’s fastest-growing job in 2017.This book presents selected papers from the International Conference on Contemporary Issues in Data Science (CiDaS 2019), a professional data science event that provided a real workshop (not “listen-shop”) where scientists and scholars had the chance to share ideas, form new collaborations, and brainstorm on major challenges; and where industry experts could catch up on emerging solutions to help solve their concrete data science problems.Given its scope, the book will benefit not only data scientists and scientists from other domains, but also industry experts, policymakers and politicians.
Data Science from Scratch: First Principles with Python
by Joel GrusData science libraries, frameworks, modules, and toolkits are great for doing data science, but they're also a good way to dive into the discipline without actually understanding data science. In this book, you'll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today's messy glut of data holds answers to questions no one's even thought to ask. This book provides you with the know-how to dig those answers out.Get a crash course in PythonLearn the basics of linear algebra, statistics, and probability--and understand how and when they're used in data scienceCollect, explore, clean, munge, and manipulate dataDive into the fundamentals of machine learningImplement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clusteringExplore recommender systems, natural language processing, network analysis, MapReduce, and databases
Data Science from Scratch: First Principles with Python
by Joel GrusData science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. With this updated second edition, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.
Data Science Fundamentals with R, Python, and Open Data
by Marco CremoniniData Science Fundamentals with R, Python, and Open Data Introduction to essential concepts and techniques of the fundamentals of R and Python needed to start data science projects Organized with a strong focus on open data, Data Science Fundamentals with R, Python, and Open Data discusses concepts, techniques, tools, and first steps to carry out data science projects, with a focus on Python and RStudio, reflecting a clear industry trend emerging towards the integration of the two. The text examines intricacies and inconsistencies often found in real data, explaining how to recognize them and guiding readers through possible solutions, and enables readers to handle real data confidently and apply transformations to reorganize, indexing, aggregate, and elaborate. This book is full of reader interactivity, with a companion website hosting supplementary material including datasets used in the examples and complete running code (R scripts and Jupyter notebooks) of all examples. Exam-style questions are implemented and multiple choice questions to support the readers’ active learning. Each chapter presents one or more case studies. Written by a highly qualified academic, Data Science Fundamentals with R, Python, and Open Data discuss sample topics such as: Data organization and operations on data frames, covering reading CSV dataset and common errors, and slicing, creating, and deleting columns in R Logical conditions and row selection, covering selection of rows with logical condition and operations on dates, strings, and missing values Pivoting operations and wide form-long form transformations, indexing by groups with multiple variables, and indexing by group and aggregations Conditional statements and iterations, multicolumn functions and operations, data frame joins, and handling data in list/dictionary format Data Science Fundamentals with R, Python, and Open Data is a highly accessible learning resource for students from heterogeneous disciplines where Data Science and quantitative, computational methods are gaining popularity, along with hard sciences not closely related to computer science, and medical fields using stochastic and quantitative models.
The Data Science Handbook
by Field CadyPractical, accessible guide to becoming a data scientist, updated to include the latest advances in data science and related fields. Becoming a data scientist is hard. The job focuses on mathematical tools, but also demands fluency with software engineering, understanding of a business situation, and deep understanding of the data itself. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. The focus of The Data Science Handbook is on practical applications and the ability to solve real problems, rather than theoretical formalisms that are rarely needed in practice. Among its key points are: An emphasis on software engineering and coding skills, which play a significant role in most real data science problems.Extensive sample code, detailed discussions of important libraries, and a solid grounding in core concepts from computer science (computer architecture, runtime complexity, and programming paradigms).A broad overview of important mathematical tools, including classical techniques in statistics, stochastic modeling, regression, numerical optimization, and more.Extensive tips about the practical realities of working as a data scientist, including understanding related jobs functions, project life cycles, and the varying roles of data science in an organization.Exactly the right amount of theory. A solid conceptual foundation is required for fitting the right model to a business problem, understanding a tool’s limitations, and reasoning about discoveries. Data science is a quickly evolving field, and this 2nd edition has been updated to reflect the latest developments, including the revolution in AI that has come from Large Language Models and the growth of ML Engineering as its own discipline. Much of data science has become a skillset that anybody can have, making this book not only for aspiring data scientists, but also for professionals in other fields who want to use analytics as a force multiplier in their organization.
Data Science Handbook: A Practical Approach
by Kolla Bhanu PrakashDATA SCIENCE HANDBOOK This desk reference handbook gives a hands-on experience on various algorithms and popular techniques used in real-time in data science to all researchers working in various domains. Data Science is one of the leading research-driven areas in the modern era. It is having a critical role in healthcare, engineering, education, mechatronics, and medical robotics. Building models and working with data is not value-neutral. We choose the problems with which we work, make assumptions in these models, and decide on metrics and algorithms for the problems. The data scientist identifies the problem which can be solved with data and expert tools of modeling and coding. The book starts with introductory concepts in data science like data munging, data preparation, and transforming data. Chapter 2 discusses data visualization, drawing various plots and histograms. Chapter 3 covers mathematics and statistics for data science. Chapter 4 mainly focuses on machine learning algorithms in data science. Chapter 5 comprises of outlier analysis and DBSCAN algorithm. Chapter 6 focuses on clustering. Chapter 7 discusses network analysis. Chapter 8 mainly focuses on regression and naive-bayes classifier. Chapter 9 covers web-based data visualizations with Plotly. Chapter 10 discusses web scraping. The book concludes with a section discussing 19 projects on various subjects in data science. Audience The handbook will be used by graduate students up to research scholars in computer science and electrical engineering as well as industry professionals in a range of industries such as healthcare.
Data Science in Agriculture and Natural Resource Management (Studies in Big Data #96)
by G. P. Obi Reddy Mehul S. Raval J. Adinarayana Sanjay ChaudharyThis book aims to address emerging challenges in the field of agriculture and natural resource management using the principles and applications of data science (DS). The book is organized in three sections, and it has fourteen chapters dealing with specialized areas. The chapters are written by experts sharing their experiences very lucidly through case studies, suitable illustrations and tables. The contents have been designed to fulfil the needs of geospatial, data science, agricultural, natural resources and environmental sciences of traditional universities, agricultural universities, technological universities, research institutes and academic colleges worldwide. It will help the planners, policymakers and extension scientists in planning and sustainable management of agriculture and natural resources. The authors believe that with its uniqueness the book is one of the important efforts in the contemporary cyber-physical systems.
Data Science in Applications (Studies in Computational Intelligence #1084)
by Gintautas Dzemyda Jolita Bernatavičienė Janusz KacprzykThis book provides an overview of a wide range of relevant applications and reveals how to solve them. Many of the latest applications in finance, technology, education, medicine and other important and relevant fields are data-driven. The volumes of data are enormous. Specific methods need to be developed or adapted to solve a particular problem. It illustrates data science in applications. These applications have in common the discovery of knowledge in data and the use of this knowledge to make real decisions. The set of examples presented serves as a recipe book for their direct application to similar problems or as a guide for the development of new, more sophisticated approaches. The intended readership is data scientists looking for appropriate solutions to their problems. In addition, the examples provided serves as material for lectures at universities.
Data Science in Context: Foundations, Challenges, Opportunities
by Alfred Z. Spector Peter Norvig Chris Wiggins Jeannette M. WingData science is the foundation of our modern world. It underlies applications used by billions of people every day, providing new tools, forms of entertainment, economic growth, and potential solutions to difficult, complex problems. These opportunities come with significant societal consequences, raising fundamental questions about issues such as data quality, fairness, privacy, and causation. In this book, four leading experts convey the excitement and promise of data science and examine the major challenges in gaining its benefits and mitigating its harms. They offer frameworks for critically evaluating the ingredients and the ethical considerations needed to apply data science productively, illustrated by extensive application examples. The authors' far-ranging exploration of these complex issues will stimulate data science practitioners and students, as well as humanists, social scientists, scientists, and policy makers, to study and debate how data science can be used more effectively and more ethically to better our world.
Data Science in Cybersecurity and Cyberthreat Intelligence (Intelligent Systems Reference Library #177)
by Leslie F. Sikos Kim-Kwang Raymond ChooThis book presents a collection of state-of-the-art approaches to utilizing machine learning, formal knowledge bases and rule sets, and semantic reasoning to detect attacks on communication networks, including IoT infrastructures, to automate malicious code detection, to efficiently predict cyberattacks in enterprises, to identify malicious URLs and DGA-generated domain names, and to improve the security of mHealth wearables. This book details how analyzing the likelihood of vulnerability exploitation using machine learning classifiers can offer an alternative to traditional penetration testing solutions. In addition, the book describes a range of techniques that support data aggregation and data fusion to automate data-driven analytics in cyberthreat intelligence, allowing complex and previously unknown cyberthreats to be identified and classified, and countermeasures to be incorporated in novel incident response and intrusion detection mechanisms.
Data Science in Engineering Vol. 10: Proceedings of the 42nd IMAC, A Conference and Exposition on Structural Dynamics 2024 (Conference Proceedings of the Society for Experimental Mechanics Series)
by Thomas Matarazzo François Hemez Eleonora Maria Tronci Austin DowneyData Science in Engineering, Volume 10: Proceedings of the 42nd IMAC, A Conference and Exposition on Structural Dynamics, 2024, the tenth volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on: Novel Data-driven Analysis Methods Deep Learning Gaussian Process Analysis Real-time Video-based Analysis Applications to Nonlinear Dynamics and Damage Detection Data-driven System Prognostics
Data Science in Engineering, Volume 10: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics 2023 (Conference Proceedings of the Society for Experimental Mechanics Series)
by Ramin Madarshahian François HemezData Science in Engineering, Volume 10: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics, 2023, the tenth volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on:Novel Data-driven Analysis MethodsDeep Learning Gaussian Process AnalysisReal-time Video-based AnalysisApplications to Nonlinear Dynamics and Damage DetectionHigh-rate Structural Monitoring and Prognostics
Data Science in Engineering, Volume 9: Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics 2021 (Conference Proceedings of the Society for Experimental Mechanics Series)
by Ramin Madarshahian Francois HemezData Science and Engineering Volume 9: Proceedings of the 39th IMAC, A Conference and Exposition on Structural Dynamics, 2021, the ninth volume of nine from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on:Data Science in Engineering ApplicationsEngineering MathematicsComputational Methods in Engineering
Data Science in Engineering, Volume 9: Proceedings of the 40th IMAC, A Conference and Exposition on Structural Dynamics 2022 (Conference Proceedings of the Society for Experimental Mechanics Series)
by Ramin Madarshahian Francois HemezData Science in Engineering, Volume 9: Proceedings of the 40th IMAC, A Conference and Exposition on Structural Dynamics, 2022, the nineth volume of nine from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on:Novel Data-driven Analysis MethodsDeep Learning Gaussian Process AnalysisReal-time Video-based AnalysisApplications to Nonlinear Dynamics and Damage DetectionHigh-rate Structural Monitoring and Prognostics
Data Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving (Chapman And Hall/crc The R Ser. #26)
by Deborah Nolan Duncan Temple LangEffectively Access, Transform, Manipulate, Visualize, and Reason about Data and ComputationData Science in R: A Case Studies Approach to Computational Reasoning and Problem Solving illustrates the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts
Data Science in Societal Applications: Concepts and Implications (Studies in Big Data #114)
by Siddharth Swarup Rautaray Manjusha Pandey Nhu Gia NguyenThe book provides an insight into the practical applications and theoretical foundation of data science. The book discusses new ways of embracing agile approaches to various facets of data science, including machine learning and artificial intelligence, data mining, data visualization, and communication. The book includes contributions from academia and industry experts detailing the shortfalls of current tools and techniques used and generating the blueprint of the new technologies. The topics covered in the book range from theoretical and foundational research, platforms, methods, applications, and tools in data science. The chapters in the book add a social, geographical, and temporal dimension to data science research. The papers included are application-oriented that prepare and use data in discovery research. This book will provide researchers and practitioners with a detailed snapshot of current progress in data science. Moreover, it will stimulate new study, research, and the development of new applications.
Data Science Landscape: Towards Research Standards And Protocols (Studies in Big Data #38)
by Usha Mujoo Munshi Neeta VermaThe 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.