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| data science for social impact: Data Science for Social Good Massimo Lapucci, Ciro Cattuto, 2021-10-13 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. |
| data science for social impact: Data Science for Social Good Massimo Lapucci, Ciro Cattuto, 2021 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. |
| data science for social impact: Data Analysis for Social Science Elena Llaudet, Kosuke Imai, 2022-11-29 Data analysis has become a necessary skill across the social sciences, and recent advancements in computing power have made knowledge of programming an essential component. Yet most data science books are intimidating and overwhelming to a non-specialist audience, including most undergraduates. This book will be a shorter, more focused and accessible version of Kosuke Imai's Quantitative Social Science book, which was published by Princeton in 2018 and has been adopted widely in graduate level courses of the same title. This book uses the same innovative approach as Quantitative Social Science , using real data and 'R' to answer a wide range of social science questions. It assumes no prior knowledge of statistics or coding. It starts with straightforward, simple data analysis and culminates with multivariate linear regression models, focusing more on the intuition of how the math works rather than the math itself. The book makes extensive use of data visualizations, diagrams, pictures, cartoons, etc., to help students understand and recall complex concepts, provides an easy to follow, step-by-step template of how to conduct data analysis from beginning to end, and will be accompanied by supplemental materials in the appendix and online for both students and instructors-- |
| data science for social impact: Big Data and Social Science Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, Julia Lane, 2016-08-10 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. |
| data science for social impact: Big Data and Social Science Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, Julia Lane, 2020-11-17 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. |
| data science for social impact: Data Science Applied to Sustainability Analysis Jennifer Dunn, Prasanna Balaprakash, 2021-05-11 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 |
| data science for social impact: Data Science Thinking Longbing Cao, 2018-08-17 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. |
| data science for social impact: Statistics for Empowerment and Social Engagement Jim Ridgway, 2023-03-10 “This book is a remarkable achievement” Gerd Gigerenzer This book offers practical approaches to working in a new field of knowledge - Civic Statistics - which sets out to engage with, and overcome well documented and long-standing problems in teaching quantitative skills. The book includes 23 peer-reviewed chapters, written in coordination by an international group of experts from ten countries. The book aims to support and enhance the work of teachers and lecturers working both at the high school and tertiary (university) levels. It is designed to promote and improve the critical understanding of quantitative evidence relevant to burning social issues – such as epidemics, climate change, poverty, migration, natural disasters, inequality, employment, and racism. Effective citizen engagement with social issues requires active participation and a broad understanding of data and statistics about societal issues. However, many statistics curricula are not designed to teach relevant skills nor to improve learners' statistical literacy. Evidence about social issues is provided to the public via print and digital media, official statistics offices, and other information channels, and a great deal of data is accessible both as aggregated summaries and as individual records. Chapters illustrate the approaches needed to teach and promote the knowledge, skills, dispositions, and enabling processes associated with critical understanding of Civic Statistics presented in many forms. These include: statistical analysis of authentic multivariate data; use of dynamic data visualisations; deconstructing texts about the social and economic well-being of societies and communities. Chapters discuss: the development of curricula and educational resources; use of emerging technologies and visualizations; preparation of teachers and teaching approaches; sources for relevant datasets and rich texts about Civic Statistics; ideas regarding future research, assessment, collaborations between different stakeholders; and other systemic issues. |
| data science for social impact: Networks for Social Impact Michelle Shumate, Katherine R. Cooper, 2022-01-07 A broad review of how nonprofits, businesses, and governments work together to tackle social problems Networks for Social Impact takes a systems approach to explain how and when networks make a social impact. Michelle Shumate and Katherine R. Cooper argue that network design and management is not a one-size-fits-all formula. Instead, they show that the type of social issue, the mechanism for social impact, environment, and resources available each determine appropriate choices. Drawing on research from public administration, psychology, business, network science, social work, and communication, this book synthesizes what we know about how to best design and manage networks. It includes illustrations from thirty original case studies which describe groups of organizations addressing issues such as gender-based violence, educational outcomes, senior care, veterans' services, mental health and wellness, and climate change. Additionally, the volume examines critical issues that leaders address in creating and managing networks, including social issue analysis, network governance, securing and managing funding, dealing with power and conflict, using data effectively, and managing change. Each chapter includes tools for network leaders to use to handle these issues. This book is neither an overly idealistic, pro-collaboration account of the benefits of network approaches, nor is it a critical view of these efforts. Instead, this clear and concise volume highlights the opportunities and challenges of networks. |
| data science for social impact: Big Data Research for Social Sciences and Social Impact Miltiadis D. Lytras, Anna Visvizi, Kwok Tai Chui, 2020-03-19 A new era of innovation is enabled by the integration of social sciences and information systems research. In this context, the adoption of Big Data and analytics technology brings new insight to the social sciences. It also delivers new, flexible responses to crucial social problems and challenges. We are proud to deliver this edited volume on the social impact of big data research. It is one of the first initiatives worldwide analyzing of the impact of this kind of research on individuals and social issues. The organization of the relevant debate is arranged around three pillars: Section A: Big Data Research for Social Impact: • Big Data and Their Social Impact; • (Smart) Citizens from Data Providers to Decision-Makers; • Towards Sustainable Development of Online Communities; • Sentiment from Online Social Networks; • Big Data for Innovation. Section B. Techniques and Methods for Big Data driven research for Social Sciences and Social Impact: • Opinion Mining on Social Media; • Sentiment Analysis of User Preferences; • Sustainable Urban Communities; • Gender Based Check-In Behavior by Using Social Media Big Data; • Web Data-Mining Techniques; • Semantic Network Analysis of Legacy News Media Perception. Section C. Big Data Research Strategies: • Skill Needs for Early Career Researchers—A Text Mining Approach; • Pattern Recognition through Bibliometric Analysis; • Assessing an Organization’s Readiness to Adopt Big Data; • Machine Learning for Predicting Performance; • Analyzing Online Reviews Using Text Mining; • Context–Problem Network and Quantitative Method of Patent Analysis. Complementary social and technological factors including: • Big Social Networks on Sustainable Economic Development; Business Intelligence. |
| data science for social impact: Roundtable on Data Science Postsecondary Education National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Division on Engineering and Physical Sciences, Board on Science Education, Computer Science and Telecommunications Board, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, 2020-09-02 Established in December 2016, the National Academies of Sciences, Engineering, and Medicine's Roundtable on Data Science Postsecondary Education was charged with identifying the challenges of and highlighting best practices in postsecondary data science education. Convening quarterly for 3 years, representatives from academia, industry, and government gathered with other experts from across the nation to discuss various topics under this charge. The meetings centered on four central themes: foundations of data science; data science across the postsecondary curriculum; data science across society; and ethics and data science. This publication highlights the presentations and discussions of each meeting. |
| data science for social impact: Big Data and Social Science Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter, Julia Lane, 2020-11-17 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. |
| data science for social impact: Data Science for Undergraduates National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Board on Science Education, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Computer Science and Telecommunications Board, Committee on Envisioning the Data Science Discipline: The Undergraduate Perspective, 2018-11-11 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. |
| data science for social impact: Machine Learning and Principles and Practice of Knowledge Discovery in Databases Michael Kamp, Irena Koprinska, Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas, Linara Adilova, Yamuna Krishnamurthy, Bo Kang, Christine Largeron, Jefrey Lijffijt, Tiphaine Viard, Pascal Welke, Massimiliano Ruocco, Erlend Aune, Claudio Gallicchio, Gregor Schiele, Franz Pernkopf, Michaela Blott, Holger Fröning, Günther Schindler, Riccardo Guidotti, Anna Monreale, Salvatore Rinzivillo, Przemyslaw Biecek, Eirini Ntoutsi, Mykola Pechenizkiy, Bodo Rosenhahn, Christopher Buckley, Daniela Cialfi, Pablo Lanillos, Maxwell Ramstead, Tim Verbelen, Pedro M. Ferreira, Giuseppina Andresini, Donato Malerba, Ibéria Medeiros, Philippe Fournier-Viger, M. Saqib Nawaz, Sebastian Ventura, Meng Sun, Min Zhou, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Giovanni Ponti, Lorenzo Severini, Rita Ribeiro, João Gama, Ricard Gavaldà, Lee Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Damian Roqueiro, Diego Saldana Miranda, Konstantinos Sechidis, Guilherme Graça, 2022-02-18 This two-volume set constitutes the refereed proceedings of the workshops which complemented the 21th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2021. Due to the COVID-19 pandemic the conference and workshops were held online. The 104 papers were thoroughly reviewed and selected from 180 papers submited for the workshops. This two-volume set includes the proceedings of the following workshops:Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI 2021)Workshop on Parallel, Distributed and Federated Learning (PDFL 2021)Workshop on Graph Embedding and Mining (GEM 2021)Workshop on Machine Learning for Irregular Time-series (ML4ITS 2021)Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM 2021)Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD 2021)Workshop on Bias and Fairness in AI (BIAS 2021)Workshop on Workshop on Active Inference (IWAI 2021)Workshop on Machine Learning for Cybersecurity (MLCS 2021)Workshop on Machine Learning in Software Engineering (MLiSE 2021)Workshop on MIning Data for financial applications (MIDAS 2021)Sixth Workshop on Data Science for Social Good (SoGood 2021)Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2021)Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2020)Workshop on Machine Learning for Buildings Energy Management (MLBEM 2021) |
| data science for social impact: A Hands-On Introduction to Data Science Chirag Shah, 2020-04-02 An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines. |
| data science for social impact: The Impact of the Social Sciences Simon Bastow, Patrick Dunleavy, Jane Tinkler, 2014-01-17 The impact agenda is set to shape the way in which social scientists prioritise the work they choose to pursue, the research methods they use and how they publish their findings over the coming decade, but how much is currently known about how social science research has made a mark on society? Based on a three year research project studying the impact of 360 UK-based academics on business, government and civil society sectors, this groundbreaking new book undertakes the most thorough analysis yet of how academic research in the social sciences achieves public policy impacts, contributes to economic prosperity, and informs public understanding of policy issues as well as economic and social changes. The Impact of the Social Sciences addresses and engages with key issues, including: identifying ways to conceptualise and model impact in the social sciences developing more sophisticated ways to measure academic and external impacts of social science research explaining how impacts from individual academics, research units and universities can be improved. This book is essential reading for researchers, academics and anyone involved in discussions about how to improve the value and impact of funded research. |
| data science for social impact: Data Feminism Catherine D'Ignazio, Lauren F. Klein, 2020-03-31 A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed. |
| data science for social impact: Philanthropy and the Future of Science and Technology Evan S. Michelson, 2020-06-14 An increasingly important and often overlooked issue in science and technology policy is recognizing the role that philanthropies play in setting the direction of research. In an era where public and private resources for science are strained, the practices that foundations adopt to advance basic and applied research needs to be better understood. This first-of-its-kind study provides a detailed assessment of the current state of science philanthropy. This examination is particularly timely, given that science philanthropies will have an increasingly important and outsized role to play in advancing responsible innovation and in shaping how research is conducted. Philanthropy and the Future of Science and Technology surveys the landscape of contemporary philanthropic involvement in science and technology by combining theoretical insights drawn from the responsible research and innovation (RRI) framework with empirical analysis investigating an array of detailed examples and case studies. Insights from interviews conducted with foundation representatives, scholars, and practitioners from a variety of sectors add real-world perspective. A wide range of philanthropic interventions are explored, focusing on support for individuals, institutions, and networks, with attention paid to the role that science philanthropies play in helping to establish and coordinate multi-sectoral funding partnerships. Novel approaches to science philanthropy are also considered, including the emergence of crowdfunding and the development of new institutional mechanisms to advance scientific research. The discussion concludes with an imaginative look into the future, outlining a series of lessons learned that can guide how new and established science philanthropies operate and envisioning alternative scenarios for the future that can inform how science philanthropy progresses over the coming decades. This book offers a major contribution to the advancement of philanthropic investment in science and technology. Thus, it will be of considerable interest to researchers and students in public policy, public administration, political science, science and technology studies, sociology of science, and related disciplines. |
| data science for social impact: Science and Public Policy Sarah Vorpahl, Nicholas Montoni, 2020-12-07 Science and policy exist symbiotically: science informs the government, which funds science to continue innovating and producing new information. While it is true that few policymakers are themselves scientists, it is thanks to scientists that the United States put a person on the moon and the Surgeon General puts warning labels on alcohol and tobacco products. Similarly, science as we know it could not be conducted without policy and support from decision makers. The government continually spends vast sums of money on fundamental and applied research at universities and laboratories because policymakers know how important continued innovation is to our world. When the authors were navigating their grad school experience, and figuring out what to do next, the same question kept coming up: how does someone actually get into science policy? The reason the answer to that question seemed so out-of-reach is because there is no one way to get involved in policy as a scientist. There are actually myriad ways for scientists to get involved in policy, regardless of research experience, background, interests, or career stage. Not only does this book outline some of these avenues, it also serves as a sort of “Civics 100” to help scientists who are new to policy understand how things work in local, state, and federal government. |
| data science for social impact: ECML PKDD 2020 Workshops Irena Koprinska, Michael Kamp, Annalisa Appice, Corrado Loglisci, Luiza Antonie, Albrecht Zimmermann, Riccardo Guidotti, Özlem Özgöbek, Rita P. Ribeiro, Ricard Gavaldà, João Gama, Linara Adilova, Yamuna Krishnamurthy, Pedro M. Ferreira, Donato Malerba, Ibéria Medeiros, Michelangelo Ceci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras, Peter Christen, Eirini Ntoutsi, Erich Schubert, Arthur Zimek, Anna Monreale, Przemyslaw Biecek, Salvatore Rinzivillo, Benjamin Kille, Andreas Lommatzsch, Jon Atle Gulla, 2021-02-01 This volume constitutes the refereed proceedings of the workshops which complemented the 20th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2020. Due to the COVID-19 pandemic the conference and workshops were held online. The 43 papers presented in volume were carefully reviewed and selected from numerous submissions. The volume presents the papers that have been accepted for the following workshops: 5th Workshop on Data Science for Social Good, SoGood 2020; Workshop on Parallel, Distributed and Federated Learning, PDFL 2020; Second Workshop on Machine Learning for Cybersecurity, MLCS 2020, 9th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2020, Workshop on Data Integration and Applications, DINA 2020, Second Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning, EDML 2020, Second International Workshop on eXplainable Knowledge Discovery in Data Mining, XKDD 2020; 8th International Workshop on News Recommendation and Analytics, INRA 2020. The papers from INRA 2020 are published open access and licensed under the terms of the Creative Commons Attribution 4.0 International License. |
| data science for social impact: Artificial Intelligence, Machine Learning, and Data Science Technologies Neeraj Mohan, Ruchi Singla, Priyanka Kaushal, Seifedine Kadry, 2021-10-11 This book provides a comprehensive, conceptual, and detailed overview of the wide range of applications of Artificial Intelligence, Machine Learning, and Data Science and how these technologies have an impact on various domains such as healthcare, business, industry, security, and how all countries around the world are feeling this impact. The book aims at low-cost solutions which could be implemented even in developing countries. It highlights the significant impact these technologies have on various industries and on us as humans. It provides a virtual picture of forthcoming better human life shadowed by the new technologies and their applications and discusses the impact Data Science has on business applications. The book will also include an overview of the different AI applications and their correlation between each other. The audience is graduate and postgraduate students, researchers, academicians, institutions, and professionals who are interested in exploring key technologies like Artificial Intelligence, Machine Learning, and Data Science. |
| data science for social impact: Data Science in Education Using R Ryan A. Estrellado, Emily Freer, Joshua M. Rosenberg, Isabella C. Velásquez, 2020-10-26 Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a learn by doing approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development. |
| data science for social impact: A New Age of Reason Larry Weber, 2024-08-06 Leverage technology to propel humankind toward a better future A New Age of Reason: Harnessing the Power of Tech for Good provides a roadmap for integrating emerging world-changing technologies, such as AI/robotics, chips/sensors, and quantum computing, to solve some of today’s thorniest and most pressing problems like climate change and world hunger. The author offers inspiring examples of companies using technology to positively impact humanity. The book provides an actionable playbook to transform your organization around this mission, including how to develop a tech for good strategy, how to evolve the C Suite to deliver on this mission, how to market it, as well as measure outcomes. The author also discusses the latest technology breakthroughs delivering positive world outcomes, such as: Extending a surgeon’s “eyes and hands” via robotics surgical systems to improve patient outcomes Computer vision tech that enables farmers to maximize crops to feed our burgeoning population AI/robotics that identify and fight wildfires Bringing together a collective of major thinkers on this subject and providing guidance for a better future, A New Age of Reason: Harnessing the Power of Tech for Good is a timely read for all executive leaders seeking to harness the new wave of technology to solve key societal problems and have a positive impact on the world. |
| data science for social impact: Machine Learning and Principles and Practice of Knowledge Discovery in Databases Irena Koprinska, Paolo Mignone, Riccardo Guidotti, Szymon Jaroszewicz, Holger Fröning, Francesco Gullo, Pedro M. Ferreira, Damian Roqueiro, Gaia Ceddia, Slawomir Nowaczyk, João Gama, Rita Ribeiro, Ricard Gavaldà, Elio Masciari, Zbigniew Ras, Ettore Ritacco, Francesca Naretto, Andreas Theissler, Przemyslaw Biecek, Wouter Verbeke, Gregor Schiele, Franz Pernkopf, Michaela Blott, Ilaria Bordino, Ivan Luciano Danesi, Giovanni Ponti, Lorenzo Severini, Annalisa Appice, Giuseppina Andresini, Ibéria Medeiros, Guilherme Graça, Lee Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Diego Saldana, Konstantinos Sechidis, Arif Canakoglu, Sara Pido, Pietro Pinoli, Albert Bifet, Sepideh Pashami, 2023-01-30 This volume constitutes the papers of several workshops which were held in conjunction with the International Workshops of ECML PKDD 2022 on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022, held in Grenoble, France, during September 19–23, 2022. The 73 revised full papers and 6 short papers presented in this book were carefully reviewed and selected from 143 submissions. ECML PKDD 2022 presents the following workshops: Workshop on Data Science for Social Good (SoGood 2022) Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2022) Workshop on Explainable Knowledge Discovery in Data Mining (XKDD 2022) Workshop on Uplift Modeling (UMOD 2022) Workshop on IoT, Edge and Mobile for Embedded Machine Learning (ITEM 2022) Workshop on Mining Data for Financial Application (MIDAS 2022) Workshop on Machine Learning for Cybersecurity (MLCS 2022) Workshop on Machine Learning for Buildings Energy Management (MLBEM 2022) Workshop on Machine Learning for Pharma and Healthcare Applications (PharML 2022) Workshop on Data Analysis in Life Science (DALS 2022) Workshop on IoT Streams for Predictive Maintenance (IoT-PdM 2022) |
| data science for social impact: I Bytes Financial Services Industry ITShades.com, 2021-02-06 This document brings together a set of latest data points and publicly available information relevant for Financial Services Industry. We are very excited to share this content and believe that readers will benefit from this periodic publication immensely. |
| data science for social impact: Next Generation Evidence Kelly Fitzsimmons, Tamar Bauer, 2023-12-15 Next Generation Evidence serves as a prequel to Show Me the Evidence: Obama's Fight for Rigor and Results in Social Policy by Ron Haskins and Greg Margolis. While Show Me the Evidence highlighted the importance of prioritizing funding for programs with evidence, Next Generation Evidence looks at how we can build the pipeline of evidence-producing programs. Evidence is remarkably powerful; it helps us understand the needs of communities, make decisions in times of change and scarcity, and build and do more of what works. However, practitioners face a number of structural and practical hurdles to building and using evidence. Traditional evaluation and research methods are often not timely, affordable, meaningful, or inclusive for helping practitioners make decisions to increase their impact for people and communities. Too often and for too long, evaluation was a thing done to practitioners and the communities they serve, relegating them to a passive role when they should be regarded as leaders of this work. Worse, their data and evidence has been used against them in disempowering thumbs-up, thumbs-down circumstances, rather than for learning and improvement that leads to impact. Next Generation Evidence features innovative thinking from leaders across policy, philanthropy, research, and practice. Together, these leaders lay out a vision for a stronger, more equitable data and evidence ecosystem that centers on the voices of people and communities most directly impacted by the problems we seek to solve. Throughout the book, case studies featuring practitioners at various stages in their evidence-building journey highlight concrete illustrations of how continuous evidence building can benefit organizations and outcomes for communities. |
| data science for social impact: Foundations of Data Science Avrim Blum, John Hopcroft, Ravindran Kannan, 2020-01-23 This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data. |
| data science for social impact: The SAGE Handbook of Social Media Marketing Annmarie Hanlon, Tracy L. Tuten, 2022-06-16 Social media marketing has become indispensable for marketers who utilize social media to achieve marketing objectives ranging from customer care to advertising to commerce. This Handbook explores the foundations and methodologies in analysing the important aspects of social media for organisations and consumers. It investigates critical areas concerning communities, culture, communication and content, and considers social media sales. This Handbook brings together the critical factors in social media marketing as the essential reference set for researchers in this area of continued growth. It is essential reading for postgraduate students, researchers, and practitioners in a range of disciplines exploring the area. Part 1: Foundations of Social Media Marketing Part 2: Methodologies and Theories in Social Media Part 3: Channels and Platforms in Social Media Part 4: Tools, Tactics, and Techniques in Social Media Marketing Part 5: Management and Metrics in Social Media Part 6: Ethical Issues in Social Media |
| data science for social impact: Data Science for COVID-19 Utku Kose, Deepak Gupta, Victor Hugo Costa de Albuquerque, Ashish Khanna, 2021-10-22 Data Science for COVID-19, Volume 2: Societal and Medical Perspectives presents the most current and leading-edge research into the applications of a variety of data science techniques for the detection, mitigation, treatment and elimination of the COVID-19 virus. At this point, Cognitive Data Science is the most powerful tool for researchers to fight COVID-19. Thanks to instant data-analysis and predictive techniques, including Artificial Intelligence, Machine Learning, Deep Learning, Data Mining, and computational modeling for processing large amounts of data, recognizing patterns, modeling new techniques, and improving both research and treatment outcomes is now possible. - Provides a leading-edge survey of Data Science techniques and methods for research, mitigation and the treatment of the COVID-19 virus - Integrates various Data Science techniques to provide a resource for COVID-19 researchers and clinicians around the world, including the wide variety of impacts the virus is having on societies and medical practice - Presents insights into innovative, data-oriented modeling and predictive techniques from COVID-19 researchers around the world, including geoprocessing and tracking, lab data analysis, and theoretical views on a variety of technical applications - Includes real-world feedback and user experiences from physicians and medical staff from around the world for medical treatment perspectives, public safety policies and impacts, sociological and psychological perspectives, the effects of COVID-19 in agriculture, economies, and education, and insights on future pandemics |
| data science for social impact: Artificial Intelligence, Social Harms and Human Rights Aleš Završnik, Katja Simončič, 2023-01-12 This book critically explores how and to what extent artificial intelligence (AI) can infringe human rights and/or lead to socially harmful consequences and how to avoid these. The European Union has outlined how it will use big data, machine learning, and AI to tackle a number of inherently social problems, including poverty, climate change, social inequality and criminality. The contributors of this book argue that the developments in AI must take place in an appropriate legal and ethical framework and they make recommendations to ensure that harm and human rights violations are avoided. The book is split into two parts: the first addresses human rights violations and harms that may occur in relation to AI in different domains (e.g. border control, surveillance, facial recognition) and the second part offers recommendations to address these issues. It draws on interdisciplinary research and speaks to policy-makers and criminologists, sociologists, scholars in STS studies, security studies scholars and legal scholars. |
| data science for social impact: Applied Data Science Martin Braschler, Thilo Stadelmann, Kurt Stockinger, 2019-06-13 This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors – some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science: first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors’ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry. |
| data science for social impact: Machine Learning and Knowledge Discovery in Databases Peggy Cellier, Kurt Driessens, 2020-03-27 This two-volume set constitutes the refereed proceedings of the workshops which complemented the 19th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in Würzburg, Germany, in September 2019. The 70 full papers and 46 short papers presented in the two-volume set were carefully reviewed and selected from 200 submissions. The two volumes (CCIS 1167 and CCIS 1168) present the papers that have been accepted for the following workshops: Workshop on Automating Data Science, ADS 2019; Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence and eXplainable Knowledge Discovery in Data Mining, AIMLAI-XKDD 2019; Workshop on Decentralized Machine Learning at the Edge, DMLE 2019; Workshop on Advances in Managing and Mining Large Evolving Graphs, LEG 2019; Workshop on Data and Machine Learning Advances with Multiple Views; Workshop on New Trends in Representation Learning with Knowledge Graphs; Workshop on Data Science for Social Good, SoGood 2019; Workshop on Knowledge Discovery and User Modelling for Smart Cities, UMCIT 2019; Workshop on Data Integration and Applications Workshop, DINA 2019; Workshop on Machine Learning for Cybersecurity, MLCS 2019; Workshop on Sports Analytics: Machine Learning and Data Mining for Sports Analytics, MLSA 2019; Workshop on Categorising Different Types of Online Harassment Languages in Social Media; Workshop on IoT Stream for Data Driven Predictive Maintenance, IoTStream 2019; Workshop on Machine Learning and Music, MML 2019; Workshop on Large-Scale Biomedical Semantic Indexing and Question Answering, BioASQ 2019. The chapter Supervised Human-guided Data Exploration is published open access under a Creative Commons Attribution 4.0 International license (CC BY). |
| data science for social impact: Environmental, Social, and Governance (ESG) Investing John Hill, 2020-01-30 Environmental, Social, and Governance (ESG) Investing: A Balanced Analysis of the Theory and Practice of a Sustainable Portfolio presents a balanced, thorough analysis of ESG factors as they are incorporated into the investment process. An estimated 25% of all new investments are in ESG funds, with a global total of $23 trillion and the U.S. accounting for almost $9 trillion. Many advocate the sustainability goals promoted by ESG, while others prefer to maximize returns and spend their earnings on social causes. The core problem facing those who want to promote sustainability goals is to define sustainability investing and measure its returns. This book examines theories and their practical implications, illuminating issues that other books leave in the shadows. - Provides a dispassionate examination of ESG investing - Presents the historical arguments for maximizing returns and competing theories to support an ESG approach - Reviews case studies of empirical evidence about relative returns of both traditional and ESG investment approaches |
| data science for social impact: ECML PKDD 2018 Workshops Carlos Alzate, Anna Monreale, Haytham Assem, Albert Bifet, Teodora Sandra Buda, Bora Caglayan, Brett Drury, Eva García-Martín, Ricard Gavaldà, Irena Koprinska, Stefan Kramer, Niklas Lavesson, Michael Madden, Ian Molloy, Maria-Irina Nicolae, Mathieu Sinn, 2019-02-15 This book constitutes revised selected papers from the workshops Nemesis, UrbReas, SoGood, IWAISe, and Green Data Mining, held at the 18th European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, in Dublin, Ireland, in September 2018. The 20 papers presented in this volume were carefully reviewed and selected from a total of 32 submissions. The workshops included are: Nemesis 2018: First Workshop on Recent Advances in Adversarial Machine Learning UrbReas 2018: First International Workshop on Urban Reasoning from Complex Challenges in Cities SoGood 2018: Third Workshop on Data Science for Social Good IWAISe 2018: Second International Workshop on Artificial Intelligence in Security Green Data Mining 2018: First International Workshop on Energy Efficient Data Mining and Knowledge Discovery |
| data science for social impact: COVID-19 Moones Rahmandoust, Seyed-Omid Ranaei-Siadat, 2021-08-13 This book highlights the overview of the COVID-19 pandemic from both the scientific and the social perspectives. The scientific part presents key facts of COVID-19, including the structure of the virus and the techniques for the diagnosis, treatment, and vaccine development against the disease, covering state-of-the-art findings and achievements worldwide. The social part is written by WHO professionals who worked on the frontier of the fight against the disease. It covers the global security situation during the pandemic, the WHO and governmental-level risk management measures, and the estimated impact that COVID-19 will eventually create on social life after it is globally controlled. |
| data science for social impact: Blockchain Applications for Smart Contract Technologies Derbali, Abdelkader Mohamed Sghaier, 2024-04-09 Blockchain technology has garnered much attention in recent years from both academic and business spheres. At its core, this technology enables the implementation of smart contracts, automated software applications that execute agreements on a secure and distributed blockchain ledger. This ledger, known for its transparency, facilitates trustless transactions, eliminating the need for centralized authority. Smart contracts, stored on the blockchain, automate processes such as goods sales, contract execution, and currency exchange, making them accessible to all users. Blockchain Applications for Smart Contract Technologies aims to present an exhaustive compilation of academic and industrial endeavors that advocate for the integration of blockchain and smart contracts in various sectors. Beyond offering a comprehensive understanding of blockchain and smart-contract fundamentals, the book seeks to spotlight specific research themes within these domains. With dedicated sections focused on applications in healthcare, finance, e-government, the Internet of Things (IoT), energy, identity, telecommunications, Metaverse, non-fungible tokens (NFTs), and notary services, the book becomes a valuable guiding resource for scholars and professionals alike. This book caters to scholars, researchers, and industry professionals that want to apply blockchain and smart-contract technologies in their fields. |
| data science for social impact: Lean Impact Ann Mei Chang, 2018-10-30 Despite enormous investments of time and money, are we making a dent on the social and environmental challenges of our time? What if we could exponentially increase our impact? Around the world, a new generation is looking beyond greater profits, for meaningful purpose. But, unlike business, few social interventions have achieved significant impact at scale. Inspired by the modern innovation practices, popularized by bestseller The Lean Startup, that have fueled technology breakthroughs touching every aspect of our lives, Lean Impact turns our attention to a new goal - radically greater social good. Social change is far more complicated than building a new app. It requires more listening, more care, and more stakeholders. To make a lasting difference, solutions must be embraced by beneficiaries, address root causes, and include an engine that can accelerate growth to reach the scale of the need. Lean Impact offers bold ideas to reach audacious goals through customer insight, rapid experimentation and iteration, and a relentless pursuit of impact. Ann Mei Chang brings a unique perspective from across sectors, from her years as a tech executive in Silicon Valley to her most recent experience as the Chief Innovation Officer at USAID. She vividly illustrates the book with real stories from interviews with over 200 organizations across the US and around the world. Whether you are a nonprofit, social enterprise, triple bottom line company, foundation, government agency, philanthropist, impact investor, or simply donate your time and money, Lean Impact is an essential guide to maximizing social impact and scale. |
| data science for social impact: RealWorld Evaluation Michael Bamberger, Linda Mabry, 2019-07-31 RealWorld Evaluation: Working Under Budget, Time, Data, and Political Constraints addresses the challenges of conducting program evaluations in real-world contexts where evaluators and their clients face budget and time constraints. The new Third Edition includes a new chapter on gender equality and women’s empowerment and discussion of digital technology and data science. |
| data science for social impact: Data Feminism Catherine D'Ignazio, Lauren F. Klein, 2023-10-03 A new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought. Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.” Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed. |
| data science for social impact: The Science of Market Research Cybellium, 2024-09-01 Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
