Data Science for Social Good

Download Data Science for Social Good PDF Online Free

Author :
Publisher : Springer Nature
ISBN 13 : 3030789853
Total Pages : 107 pages
Book Rating : 4.0/5 (37 download)

DOWNLOAD NOW!


Book Synopsis Data Science for Social Good by : Massimo Lapucci

Download or read book Data Science for Social Good written by Massimo Lapucci and published by Springer Nature. This book was released on 2021-10-13 with total page 107 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 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.

Big Data and Social Science

Download Big Data and Social Science PDF Online Free

Author :
Publisher : CRC Press
ISBN 13 : 100020863X
Total Pages : 320 pages
Book Rating : 4.0/5 (2 download)

DOWNLOAD NOW!


Book Synopsis Big Data and Social Science by : Ian Foster

Download or read book Big Data and Social Science written by Ian Foster and published by CRC Press. This book was released on 2020-11-17 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Sustainable Development Through Data Analytics and Innovation

Download Sustainable Development Through Data Analytics and Innovation PDF Online Free

Author :
Publisher : Springer Nature
ISBN 13 : 3031125274
Total Pages : 195 pages
Book Rating : 4.0/5 (311 download)

DOWNLOAD NOW!


Book Synopsis Sustainable Development Through Data Analytics and Innovation by : Jorge Marx Gómez

Download or read book Sustainable Development Through Data Analytics and Innovation written by Jorge Marx Gómez and published by Springer Nature. This book was released on 2022-09-26 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sustainable development is based on the idea that societies should advance without compromising their future development requirements. This book explores how the application of data analytics and digital technologies can ensure that development changes are executed on the basis of factual data and information. It addresses how innovations that rely on digital technologies can support sustainable development across all sectors and all social, economic, and environmental aspects and help us achieve the Sustainable Development Goals (SDGs). The book also highlights techniques, processes, models, tools, and practices used to achieve sustainable development through data analysis. The various topics covered in this book are critically evaluated, not only theoretically, but also from an application perspective. It will be of interest to researchers and students, especially those in the fields of applied data analytics, business intelligence and knowledge management.

Social Transformation – Digital Way

Download Social Transformation – Digital Way PDF Online Free

Author :
Publisher : Springer
ISBN 13 : 9811313431
Total Pages : 749 pages
Book Rating : 4.8/5 (113 download)

DOWNLOAD NOW!


Book Synopsis Social Transformation – Digital Way by : Jyotsna Kumar Mandal

Download or read book Social Transformation – Digital Way written by Jyotsna Kumar Mandal and published by Springer. This book was released on 2018-08-23 with total page 749 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 52nd Annual Convention of the Computer Society of India, CSI 2017, held in Kolkata, India, in January 2018. The 59 revised papers presented were carefully reviewed and selected from 157 submissions. The theme of CSI 2017, Social Transformation – Digital Way, was selected to highlight the importance of technology for both central and state governments at their respective levels to achieve doorstep connectivity with its citizens. The papers are organized in the following topical sections: Signal processing, microwave and communication engineering; circuits and systems; data science and data analytics; bio computing; social computing; mobile, nano, quantum computing; data mining; security and forensics; digital image processing; and computational intelligence.

Data Science for Undergraduates

Download Data Science for Undergraduates PDF Online Free

Author :
Publisher : National Academies Press
ISBN 13 : 0309475627
Total Pages : 139 pages
Book Rating : 4.3/5 (94 download)

DOWNLOAD NOW!


Book Synopsis Data Science for Undergraduates by : National Academies of Sciences, Engineering, and Medicine

Download or read book Data Science for Undergraduates written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2018-10-11 with total page 139 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science is emerging as a field that is revolutionizing science and industries alike. Work across nearly all domains is becoming more data driven, affecting both the jobs that are available and the skills that are required. As more data and ways of analyzing them become available, more aspects of the economy, society, and daily life will become dependent on data. It is imperative that educators, administrators, and students begin today to consider how to best prepare for and keep pace with this data-driven era of tomorrow. Undergraduate teaching, in particular, offers a critical link in offering more data science exposure to students and expanding the supply of data science talent. Data Science for Undergraduates: Opportunities and Options offers a vision for the emerging discipline of data science at the undergraduate level. This report outlines some considerations and approaches for academic institutions and others in the broader data science communities to help guide the ongoing transformation of this field.

Advances in Data Science

Download Advances in Data Science PDF Online Free

Author :
Publisher : John Wiley & Sons
ISBN 13 : 1786305763
Total Pages : 258 pages
Book Rating : 4.7/5 (863 download)

DOWNLOAD NOW!


Book Synopsis Advances in Data Science by : Edwin Diday

Download or read book Advances in Data Science written by Edwin Diday and published by John Wiley & Sons. This book was released on 2020-02-05 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science unifies statistics, data analysis and machine learning to achieve a better understanding of the masses of data which are produced today, and to improve prediction. Special kinds of data (symbolic, network, complex, compositional) are increasingly frequent in data science. These data require specific methodologies, but there is a lack of reference work in this field. Advances in Data Science fills this gap. It presents a collection of up-to-date contributions by eminent scholars following two international workshops held in Beijing and Paris. The 10 chapters are organized into four parts: Symbolic Data, Complex Data, Network Data and Clustering. They include fundamental contributions, as well as applications to several domains, including business and the social sciences.

Data Science Thinking

Download Data Science Thinking PDF Online Free

Author :
Publisher : Springer
ISBN 13 : 3319950924
Total Pages : 404 pages
Book Rating : 4.3/5 (199 download)

DOWNLOAD NOW!


Book Synopsis Data Science Thinking by : Longbing Cao

Download or read book Data Science Thinking written by Longbing Cao and published by Springer. This book was released on 2018-08-17 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective.

Trends of Data Science and Applications

Download Trends of Data Science and Applications PDF Online Free

Author :
Publisher : Springer Nature
ISBN 13 : 9813368152
Total Pages : 341 pages
Book Rating : 4.8/5 (133 download)

DOWNLOAD NOW!


Book Synopsis Trends of Data Science and Applications by : Siddharth Swarup Rautaray

Download or read book Trends of Data Science and Applications written by Siddharth Swarup Rautaray and published by Springer Nature. This book was released on 2021-03-21 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes an extended version of selected papers presented at the 11th Industry Symposium 2021 held during January 7–10, 2021. The book covers contributions ranging from theoretical and foundation research, platforms, methods, applications, and tools in all areas. It provides theory and practices in the area of data science, which add a social, geographical, and temporal dimension to data science research. It also includes application-oriented papers that prepare and use data in discovery research. This book contains chapters from academia as well as practitioners on big data technologies, artificial intelligence, machine learning, deep learning, data representation and visualization, business analytics, healthcare analytics, bioinformatics, etc. This book is helpful for the students, practitioners, researchers as well as industry professional.

Machine Learning and Data Science

Download Machine Learning and Data Science PDF Online Free

Author :
Publisher : John Wiley & Sons
ISBN 13 : 1119775612
Total Pages : 276 pages
Book Rating : 4.1/5 (197 download)

DOWNLOAD NOW!


Book Synopsis Machine Learning and Data Science by : Prateek Agrawal

Download or read book Machine Learning and Data Science written by Prateek Agrawal and published by John Wiley & Sons. This book was released on 2022-08-09 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: MACHINE LEARNING AND DATA SCIENCE Written and edited by a team of experts in the field, this collection of papers reflects the most up-to-date and comprehensive current state of machine learning and data science for industry, government, and academia. Machine learning (ML) and data science (DS) are very active topics with an extensive scope, both in terms of theory and applications. They have been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. Simultaneously, their applications provide important challenges that can often be addressed only with innovative machine learning and data science algorithms. These algorithms encompass the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. They also tackle related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.

Data Science and Social Research

Download Data Science and Social Research PDF Online Free

Author :
Publisher : Springer
ISBN 13 : 3319554778
Total Pages : 292 pages
Book Rating : 4.3/5 (195 download)

DOWNLOAD NOW!


Book Synopsis Data Science and Social Research by : N. Carlo Lauro

Download or read book Data Science and Social Research written by N. Carlo Lauro and published by Springer. This book was released on 2017-11-17 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources. This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices.

Big Data and Social Science

Download Big Data and Social Science PDF Online Free

Author :
Publisher : CRC Press
ISBN 13 : 1498751415
Total Pages : 377 pages
Book Rating : 4.4/5 (987 download)

DOWNLOAD NOW!


Book Synopsis Big Data and Social Science by : Ian Foster

Download or read book Big Data and Social Science written by Ian Foster and published by CRC Press. This book was released on 2016-09-15 with total page 377 pages. Available in PDF, EPUB and Kindle. Book excerpt: Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems. Big Data and Social Science: A Practical Guide to Methods and Tools shows 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.

Ethics and Data Science

Download Ethics and Data Science PDF Online Free

Author :
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492078212
Total Pages : 37 pages
Book Rating : 4.4/5 (92 download)

DOWNLOAD NOW!


Book Synopsis Ethics and Data Science by : Mike Loukides

Download or read book Ethics and Data Science written by Mike Loukides and published by "O'Reilly Media, Inc.". This book was released on 2018-07-25 with total page 37 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes: A sample checklist that you can adapt for your own procedures Five framing guidelines (the Five C’s) for building data products: consent, clarity, consistency, control, and consequences Suggestions for building ethics into your data-driven culture Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today.

Business Models

Download Business Models PDF Online Free

Author :
Publisher : CRC Press
ISBN 13 : 100009779X
Total Pages : 169 pages
Book Rating : 4.0/5 ( download)

DOWNLOAD NOW!


Book Synopsis Business Models by : Iwona Otola

Download or read book Business Models written by Iwona Otola and published by CRC Press. This book was released on 2020-07-09 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the beginning of time, running a business has involved using logic by which the business operates. This logic is called the business model in management science, which increasingly is focusing on issues surrounding business models. Research trends related to business models include value creation, value chain operationalization, and social and ecological aspects, as well as innovation and digital transformation. Business Models: Innovation, Digital Transformation, and Analytics examines how innovation, digital transformation, and the composition of value affect the existence and development of business models. The book starts by addressing the conceptual development of business models and by discussing the essence of innovation in those models. Chapters in the book investigate how: Business models can analyze digital transformation scenarios Individual business model elements effect selected performance measures as well as how the elements are significant for the enterprise value composition The environment effects the profitability of the high-growth enterprise business models Employer branding business models are perceived by the generation Z workforce To implement responsible business models in the enterprise Cyber risk is captured in business models Decision algorithms are important to business analytics This book is a compendium of knowledge about the use of business models in the context of innovative activities, digital transformation, and value composition. It attempts to combine the theory and practice and offers a look at business models currently used in companies, especially high-growth enterprises, in various countries of the world and indicates the prospects for their development.

Social Big Data Analytics

Download Social Big Data Analytics PDF Online Free

Author :
Publisher :
ISBN 13 : 9789813366534
Total Pages : 0 pages
Book Rating : 4.3/5 (665 download)

DOWNLOAD NOW!


Book Synopsis Social Big Data Analytics by : Bilal Abu-Salih

Download or read book Social Big Data Analytics written by Bilal Abu-Salih and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on data and how modern business firms use social data, specifically Online Social Networks (OSNs) incorporated as part of the infrastructure for a number of emerging applications such as personalized recommendation systems, opinion analysis, expertise retrieval, and computational advertising. This book identifies how in such applications, social data offers a plethora of benefits to enhance the decision making process. This book highlights that business intelligence applications are more focused on structured data; however, in order to understand and analyse the social big data, there is a need to aggregate data from various sources and to present it in a plausible format. Big Social Data (BSD) exhibit all the typical properties of big data: wide physical distribution, diversity of formats, non-standard data models, independently-managed and heterogeneous semantics but even further valuable with marketing opportunities. The book provides a review of the current state-of-the-art approaches for big social data analytics as well as to present dissimilar methods to infer value from social data. The book further examines several areas of research that benefits from the propagation of the social data. In particular, the book presents various technical approaches that produce data analytics capable of handling big data features and effective in filtering out unsolicited data and inferring a value. These approaches comprise advanced technical solutions able to capture huge amounts of generated data, scrutinise the collected data to eliminate unwanted data, measure the quality of the inferred data, and transform the amended data for further data analysis. Furthermore, the book presents solutions to derive knowledge and sentiments from BSD and to provide social data classification and prediction. The approaches in this book also incorporate several technologies such as semantic discovery, sentiment analysis, affective computing and machine learning. This book has additional special feature enriched with numerous illustrations such as tables, graphs and charts incorporating advanced visualisation tools in accessible an attractive display.

Data Activism and Social Change

Download Data Activism and Social Change PDF Online Free

Author :
Publisher :
ISBN 13 : 9783319783208
Total Pages : 178 pages
Book Rating : 4.7/5 (832 download)

DOWNLOAD NOW!


Book Synopsis Data Activism and Social Change by : Miren Gutiérrez

Download or read book Data Activism and Social Change written by Miren Gutiérrez and published by . This book was released on 2018 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book efficiently contributes to our understanding of the interplay between data, technology and communicative practice on the one hand, and democratic participation on the other. It addresses the emergence of proactive data activism, a new sociotechnical phenomenon in the field of action that arises as a reaction to massive datafication, and makes affirmative use of data for advocacy and social change. By blending empirical observation and in-depth qualitative interviews, Gutiérrez brings to the fore a debate about the social uses of the data infrastructure and examines precisely how people employ it, in combination with other technologies, to collaborate and act for social change.

Social Data Science Xennials

Download Social Data Science Xennials PDF Online Free

Author :
Publisher : Springer Nature
ISBN 13 : 303060358X
Total Pages : 113 pages
Book Rating : 4.0/5 (36 download)

DOWNLOAD NOW!


Book Synopsis Social Data Science Xennials by : Gian Marco Campagnolo

Download or read book Social Data Science Xennials written by Gian Marco Campagnolo and published by Springer Nature. This book was released on 2020-11-30 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores the tension between analogue and digital as part of an evolving research programme and focuses on the sequencing of methods within it. The book will be an invaluable reference for scholars who routinely engage in critical sociological analysis of the digital workplace and find it easier to treat the digital as an object of study. It describes how the transformations taking place in the 10-year arc of a career spent doing fieldwork in the IT sector led the author to progressively embrace new forms of data and methods. In a time where sociological imagination takes the shape of whatever new phenomenon can be studied by transactional data and machine learning methods, it is a reminder that longstanding engagement with a particular field of practice is the basis of empirical social science expertise. ‘This short book by Gian Marco Campagnolo is remarkably wide-ranging. It draws on theoretical perspectives as varied as Harold Garfinkel’s ethnomethodology and Andrew Abbott’s ‘linked ecologies’ to discuss topics as diverse as the adoption of packaged enterprise software in the public sector in Italy and the careers of often influential industry analysts. Campagnolo’s methods are primarily qualitative and ethnographic, but he shows a proper appreciation for quantitative methods such as text mining and sequence analysis. The book ends with a discussion of the famously difficult issue of achieving ‘explainability’ in machine learning. Campagnolo tantalisingly suggests the usefulness here of how ethnomethodologists view ‘accountability’: as a practical accomplishment that is hampered, rather than fostered, by efforts to give full explanations.’ —Donald MacKenzie, Professor of Sociology, Edinburgh University, Scotland ‘The author adopts a ‘processual’ perspective on social data science as means of exploring and reflecting on the emergence of an academic career within this new domain of interdisciplinary inquiry. This is certainly a novel and interesting approach given the fact that ‘data science’ is work in progress and is characterized by a number of competing occupational groups that are struggling to define this emerging field.’ —William Housley, Professor, University of Cardiff, UK ‘Having myself written about the relationships between ethnography and computer science, I see this book as a timely contribution in that it extends the existing debate to data science. Data science is an emerging discipline that is gaining central stage in industry and in the public discourse. The aim of this book to indicate the importance of interdisciplinarity in this field is commendable.’ —Giolo Fele, Professor, University of Trento, Italy 'This book provides two entwined accounts: a reflective personal journey across different projects and methods and a grounded, genealogically sound analysis of the approaches and contributions of social science to understanding the digital society. These dual accounts are adroitly communicated. Their bold combination yields a unique and invaluable contribution to fundamental discussions in the social sciences, as well as an exemplar for how to combine ethnographic and data-driven analysis in a theoretically and epistemologically informed manner. With this book, Campagnolo brings us close to the methods and opens up an inspiring and challenging agenda for combining old and new forms of inquiry into sociological problems.' —Anne Beaulieu, Director Data Research Centre, University of Groningen, Netherlands

Feature Engineering for Machine Learning and Data Analytics

Download Feature Engineering for Machine Learning and Data Analytics PDF Online Free

Author :
Publisher : CRC Press
ISBN 13 : 1351721275
Total Pages : 400 pages
Book Rating : 4.3/5 (517 download)

DOWNLOAD NOW!


Book Synopsis Feature Engineering for Machine Learning and Data Analytics by : Guozhu Dong

Download or read book Feature Engineering for Machine Learning and Data Analytics written by Guozhu Dong and published by CRC Press. This book was released on 2018-03-14 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features. The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively. This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics.