Search results for: social-data-science-hardback

Big Data and Social Science

Author : Ian Foster
File Size : 63.78 MB
Format : PDF, ePub
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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 workbooks with data, code, and 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.

Urban Analytics with Social Media Data

Author : Tan Yigitcanlar
File Size : 78.70 MB
Format : PDF, Mobi
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The use of data science and urban analytics has become a defining feature of smart cities. This timely book is a clear guide to the use of social media data for urban analytics. The book presents the foundations of urban analytics with social media data, along with real-world applications and insights on the platforms we use today. It looks at social media analytics platforms, cyberphysical data analytics platforms, crowd detection platforms, City-as-a-Platform, and city-as-a-sensor for platform urbanism. The book provides examples to illustrate how we apply and analyse social media data to determine disaster severity, assist authorities with pandemic policy, and capture public perception of smart cities. This will be a useful reference for those involved with and researching social, data, and urban analytics and informatics.

Big Data and Social Science

Author : Ian Foster
File Size : 63.95 MB
Format : PDF
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Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Toolsshows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. For more information, including sample chapters and news, please visit the author's website: http://www.bigdatasocialscience.com/

Introduction to Data Science for Social and Policy Research

Author : Jose Manuel Magallanes Reyes
File Size : 41.2 MB
Format : PDF
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Real-world data sets are messy and complicated. Written for students in social science and public management, this authoritative but approachable guide describes all the tools needed to collect data and prepare it for analysis. Offering detailed, step-by-step instructions, it covers collection of many different types of data including web files, APIs, and maps; data cleaning; data formatting; the integration of different sources into a comprehensive data set; and storage using third-party tools to facilitate access and shareability, from Google Docs to GitHub. Assuming no prior knowledge of R and Python, the author introduces programming concepts gradually, using real data sets that provide the reader with practical, functional experience.

Big Data Analytics

Author : Mrutyunjaya Panda
File Size : 55.38 MB
Format : PDF, ePub, Docs
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Social networking has increased drastically in recent years, resulting in an increased amount of data being created daily. Furthermore, diversity of issues and complexity of the social networks pose a challenge in social network mining. Traditional algorithm software cannot deal with such complex and vast amounts of data, necessitating the development of novel analytic approaches and tools. This reference work deals with social network aspects ofbig data analytics. It covers theory, practices and challenges in social networking. The book spans numerous disciplines like neural networking, deep learning, artificial intelligence, visualization, e-learning in higher education, e-healthcare, security and intrusion detection.

Societal Dimensions of Environmental Science

Author : Ricardo D. Lopez
File Size : 55.48 MB
Format : PDF
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Societal Dimensions of Environmental Science: Global Case Studies of Collaboration and Transformation, brings together several key examples of the successes and the challenges that exist for environmental stakeholders trying to strike a balance between science and the societal implications of the issues involved. This book provides important methods and approaches necessary for informed decision making and a better understanding of the common threads of learning, collaboration, negotiation, and compromise. It also explains that concepts and skills needed to better understand how specific project goals can be best achieved in the rapidly changing field of environmental management, by providing practical situations and solutions, across a global landscape. This book provides anyone who works in a community setting with the necessary tools and strategies for solving environmental problems and achieving the goals of an environmental project of any type and specifically addresses the topic of how to synthesize community engagement and the environmental science. It describes current environmental issues and lessons learned of what works and what doesn’t work in real situations, and why. It also highlights key examples, which can be used by both management practitioners and research scientists in their specific circumstances. Showcasing a unique compilation of the diverse and specific examples from societies in Asia, Oceania, North America, and the Middle East, with an equally diverse array of authorship, this book serves all policy makers, scientists, organizers, and community members that desire to build better group dynamics for addressing environmental issues.

Data Science

Author : John D. Kelleher
File Size : 22.99 MB
Format : PDF, Docs
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A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.

Rethinking Society for the 21st Century Volume 1 Socio Economic Transformations

Author : International Panel on Social Progress (IPSP)
File Size : 56.60 MB
Format : PDF
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This is the first of three volumes containing a report from the International Panel on Social Progress (IPSP). The IPSP is an independent association of top research scholars with the goal of assessing methods for improving the main institutions of modern societies. Written in accessible language by scholars across the social sciences and humanities, these volumes assess the achievements of world societies in past centuries, the current trends, the dangers that we are now facing, and the possible futures in the twenty-first century. It covers the main socio-economic, political, and cultural dimensions of social progress, global as well as regional issues, and the diversity of challenges and their interplay around the world. This particular volume covers topics such as economic inequality and growth, finance and corporations, labor, capitalism, and social justice.

Data Science

Author : John D. Kelleher
File Size : 87.88 MB
Format : PDF, ePub, Mobi
Download : 335
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A concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.

New Society

Author :
File Size : 74.12 MB
Format : PDF
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