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| data science for social justice: Nature of Science for Social Justice Hagop A. Yacoubian, Lena Hansson, 2020-09-08 This edited volume brings closer two contemporary science education research areas: Nature of Science (NOS) and Social Justice (SJ). It starts a dialogue on the characteristics of NOS for SJ with the purpose of advancing the existing discussion and creating new avenues for research. Using a variety of approaches and perspectives, the authors of the different chapters engage in a dialogue on the construct of NOS for SJ, its characteristics, as well as ways of addressing it in science classrooms. Issues addressed are related to why a school science aiming at SJ should address NOS; what NOS-related content, skills and attitudes form the basis when aiming at SJ; and how school science can address NOS for SJ. Through a set of theoretical and empirical chapters, the authors suggest answers, but they also pose new questions on what NOS for SJ can mean, and what issues need to be taken into consideration in future research and practice. Chapter “Nature of Science for Social Justice: Why, What and How?” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com |
| data science for social justice: 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 justice: Teaching Science for Social Justice Angela Calabrese Barton, Jason L. Ermer, Tanahia A. Burkett-Benton, Margery D. Osborne, 2018-08-24 How might science education reflect the values of a socially just and democratic society? How do urban youth living in poverty construct science in their lives in ways that are enriching, empowering, and transformative? Using a combination of in-depth case studies and rigorous theory, this volume: Offers a series of teaching stories that describes youth’s practices of science, providing valuable insight to help teachers work with inner-city youth.Explores the importance of inclusiveness, membership rules, and the purposes and goals of good science, including utility, pragmatism, and doing good for others.Shows how science connects to the lives of youth both in and out of school. Builds on and critiques current reform initiatives in science education.Features stories taken from six years of teaching and research in after-school science programs with children and youth in homeless shelters.Illustrates how the children’s unique situations framed their constructions of science in compelling and challenging ways. |
| data science for social justice: 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 justice: Developing Students’ Statistical Reasoning Joan Garfield, Dani Ben-Zvi, 2008-09-08 Increased attention is being paid to the need for statistically educated citizens: statistics is now included in the K-12 mathematics curriculum, increasing numbers of students are taking courses in high school, and introductory statistics courses are required in college. However, increasing the amount of instruction is not sufficient to prepare statistically literate citizens. A major change is needed in how statistics is taught. To bring about this change, three dimensions of teacher knowledge need to be addressed: their knowledge of statistical content, their pedagogical knowledge, and their statistical-pedagogical knowledge, i.e., their specific knowledge about how to teach statistics. This book is written for mathematics and statistics educators and researchers. It summarizes the research and highlights the important concepts for teachers to emphasize, and shows the interrelationships among concepts. It makes specific suggestions regarding how to build classroom activities, integrate technological tools, and assess students’ learning. This is a unique book. While providing a wealth of examples through lessons and data sets, it is also the best attempt by members of our profession to integrate suggestions from research findings with statistics concepts and pedagogy. The book’s message about the importance of listening to research is loud and clear, as is its message about alternative ways of teaching statistics. This book will impact instructors, giving them pause to consider: Is what I’m doing now really the best thing for my students? What could I do better? J. Michael Shaughnessy, Professor, Dept of Mathematical Sciences, Portland State University, USA This is a much-needed text for linking research and practice in teaching statistics. The authors have provided a comprehensive overview of the current state-of-the-art in statistics education research. The insights they have gleaned from the literature should be tremendously helpful for those involved in teaching and researching introductory courses. Randall E. Groth, Assistant Professor of Mathematics Education, Salisbury University, USA |
| data science for social justice: Oxford Bibliographies Edward J. Mullen, Offers peer-reviewed annotated bibliographies on social work as a discipline grounded in social theory and the improvement of peoples' lives. Bibliographies are browseable by subject area and keyword searchable. Contains a My OBO function that allows users to create personalized bibliographies of individual citations from different bibliographies. |
| data science for social justice: Big Data, Crime and Social Control Aleš Završnik, 2017-09-20 From predictive policing to self-surveillance to private security, the potential uses to of big data in crime control pose serious legal and ethical challenges relating to privacy, discrimination, and the presumption of innocence. The book is about the impacts of the use of big data analytics on social and crime control and on fundamental liberties. Drawing on research from Europe and the US, this book identifies the various ways in which law and ethics intersect with the application of big data in social and crime control, considers potential challenges to human rights and democracy and recommends regulatory solutions and best practice. This book focuses on changes in knowledge production and the manifold sites of contemporary surveillance, ranging from self-surveillance to corporate and state surveillance. It tackles the implications of big data and predictive algorithmic analytics for social justice, social equality, and social power: concepts at the very core of crime and social control. This book will be of interest to scholars and students of criminology, sociology, politics and socio-legal studies. |
| data science for social justice: Handbook of Research on Social Justice and Equity in Education Keengwe, Jared, 2022-05-06 There is growing pressure on teachers and other educators to understand and adopt the best ways to work with the various races, cultures, and languages that diverse learners represent in the ever-increasing culturally-diverse learning environments. Establishing sound cross-cultural pedagogy is also critical given that racial, cultural, and linguistic integration has the potential to increase academic success for all learners. To that end, there is also a need for educators to prepare graduates who will better meet the needs of culturally diverse learners as well as support their students to become successful global citizens. The Handbook of Research on Social Justice and Equity in Education highlights cross-cultural perspectives, challenges, and opportunities pertaining to promoting cultural competence, equity, and social justice in education. It also explores multiple concepts of building a bridge from a monocultural pedagogical framework to cross-cultural knowledge. Covering topics such as diversity education and global citizenship, this major reference work is ideal for academicians, researchers, practitioners, policymakers, instructors, and students. |
| data science for social justice: Research Anthology on Instilling Social Justice in the Classroom Management Association, Information Resources, 2020-11-27 The issue of social justice has been brought to the forefront of society within recent years, and educational institutions have become an integral part of this critical conversation. Classroom settings are expected to take part in the promotion of inclusive practices and the development of culturally proficient environments that provide equal and effective education for all students regardless of race, gender, socio-economic status, and disability, as well as from all walks of life. The scope of these practices finds itself rooted in curriculum, teacher preparation, teaching practices, and pedagogy in all educational environments. Diversity within school administrations, teachers, and students has led to the need for socially just practices to become the norm for the progression and advancement of education worldwide. In a modern society that is fighting for the equal treatment of all individuals, the classroom must be a topic of discussion as it stands as a root of the problem and can be a major step in the right direction moving forward. Research Anthology on Instilling Social Justice in the Classroom is a comprehensive reference source that provides an overview of social justice and its role in education ranging from concepts and theories for inclusivity, tools, and technologies for teaching diverse students, and the implications of having culturally competent and diverse classrooms. The chapters dive deeper into the curriculum choices, teaching theories, and student experience as teachers strive to instill social justice learning methods within their classrooms. These topics span a wide range of subjects from STEM to language arts, and within all types of climates: PK-12, higher education, online or in-person instruction, and classrooms across the globe. This book is ideal for in-service and preservice teachers, administrators, social justice researchers, practitioners, stakeholders, researchers, academicians, and students interested in how social justice is currently being implemented in all aspects of education. |
| data science for social justice: 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 justice: Improving Equity in Data Science Colby Tofel-Grehl, Emmanuel Schanzer, 2024-06-03 Improving Equity in Data Science offers a comprehensive look at the ways in which data science can be conceptualized and engaged more equitably within the K-16 classroom setting, moving beyond merely broadening participation in educational opportunities. This book makes the case for field wide definitions, literacies and practices for data science teaching and learning that can be commonly discussed and used, and provides examples from research of these practices and literacies in action. Authors share stories and examples of research wherein data science advances equity and empowerment through the critical examination of social, educational, and political topics. In the first half of the book, readers will learn how data science can deliberately be embedded within K-12 spaces to empower students to use it to identify and address inequity. The latter half will focus on equity of access to data science learning opportunities in higher education, with a final synthesis of lessons learned and presentation of a 360-degree framework that links access, curriculum, and pedagogy as multiple facets collectively essential to comprehensive data science equity work. Practitioners and teacher educators will be able to answer the question, “how can data science serve to move equity efforts in computing beyond basic inclusion to empowerment?” whether the goal is to simply improve definitions and approaches to research on data science or support teachers of data science in creating more equitable and inclusive environments within their classrooms. |
| data science for social justice: 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 justice: Social Media, Social Justice and the Political Economy of Online Networks Jeffrey Blevins, James Lee, 2021-01-15 While social network analyses often demonstrate the usefulness of social media networks to affective publics and otherwise marginalized social justice groups, this book explores the domination and manipulation of social networks by more powerful political groups. Jeffrey Layne Blevins and James Lee look at the ways in which social media conversations about race turn politically charged, and in many cases, ugly. Studies show that social media is an important venue for news and political information, while focusing national attention on racially involved issues. Perhaps less understood, however, is the effective quality of this discourse, and its connection to popular politics, especially when Twitter trolls and social media mobs go on the attack. Taking on prominent case studies from the past few years, including the Ferguson protests and the Black Lives Matter movement, the 2016 presidential election, and the rise of fake news, this volume presents data visualization sets alongside careful scholarly analysis. The resulting volume provides new insight into social media, legacy news, and social justice. |
| data science for social justice: Information Technology and Social Justice Rooksby, Emma, Weckert, John, 2006-10-31 The term digital divide is still used regularly to characterize the injustice associated with inequalities in access to information and communication technologies (ICTs). As the debate continues and becomes more sophisticated, more and more aspects of the distribution of ICTs are singled out as relevant to characterizations of the digital divide and of its moral status. The best way to articulate the digital divide is to relate it to other aspects of social and distributive justice, using a mixture of pre-existing theories within moral and political philosophy. These theories are complemented with contributions from sociology, communication studies, information systems, and a range of other disciplines. Information Technology and Social Justice presents conceptual frameworks for understanding and tackling digital divides. It includes information on access and skills, access and motivation, and other various levels of access. It also presents a detailed analysis of the benefits and value of access to ICTs. |
| data science for social justice: Mathematics for Social Justice: Resources for the College Classroom Gizem Karaali, Lily S. Khadjavi, 2019-07-09 Mathematics for Social Justice offers a collection of resources for mathematics faculty interested in incorporating questions of social justice into their classrooms. The book begins with a series of essays from instructors experienced in integrating social justice themes into their pedagogy; these essays contain political and pedagogical motivations as well as nuts-and-bolts teaching advice. The heart of the book is a collection of fourteen classroom-tested modules featuring ready-to-use activities and investigations for the college mathematics classroom. The mathematical tools and techniques used are relevant to a wide variety of courses including college algebra, math for the liberal arts, calculus, differential equations, discrete mathematics, geometry, financial mathematics, and combinatorics. The social justice themes include human trafficking, income inequality, environmental justice, gerrymandering, voting methods, and access to education. The volume editors are leaders of the national movement to include social justice material into mathematics teaching. Gizem Karaali is Associate Professor of Mathematics at Pomona College. She is one of the founding editors of The Journal of Humanistic Mathematics, and an associate editor for The Mathematical Intelligencer and Numeracy ; she also serves on the editorial board of the MAA's Carus Mathematical Monographs. Lily Khadjavi is Associate Professor of Mathematics at Loyola Marymount University and is a past co-chair of the Infinite Possibilities Conference. She has served on the boards of Building Diversity in Science, the Barbara Jordan-Bayard Rustin Coalition, and the Harvard Gender and Sexuality Caucus. |
| data science for social justice: Toward Information Justice Jeffrey Alan Johnson, 2018-01-09 This book presents a theory of information justice that subsumes the question of control and relates it to other issues that influence just social outcomes. Data does not exist by nature. Bureaucratic societies must provide standardized inputs for governing algorithms, a problem that can be understood as one of legibility. This requires, though, converting what we know about social objects and actions into data, narrowing the many possible representations of the objects to a definitive one using a series of translations. Information thus exists within a nexus of problems, data, models, and actions that the social actors constructing the data bring to it. This opens information to analysis from social and moral perspectives, while the scientistic view leaves us blind to the gains from such analysis—especially to the ways that embedded values and assumptions promote injustice. Toward Information Justice answers a key question for the 21st Century: how can an information-driven society be just? Many of those concerned with the ethics of data focus on control over data, and argue that if data is only controlled by the right people then just outcomes will emerge. There are serious problems with this control metaparadigm, however, especially related to the initial creation of data and prerequisites for its use. This text is suitable for academics in the fields of information ethics, political theory, philosophy of technology, and science and technology studies, as well as policy professionals who rely on data to reach increasingly problematic conclusions about courses of action. |
| data science for social justice: Photovoice for Social Justice Jean M. Breny, Shannon L. McMorrow, 2020-12-13 Photovoice for Social Justice, the latest volume in SAGE′s Qualitative Research Methods Series, helps readers in the health and social sciences learn the foundations and applications of this exciting qualitative method. Authors Jean M. Breny and Shannon L. McMorrow approach photovoice as not only a community-based participatory research method, but as a method for social justice, centering community participants, organizations, and policy makers at the heart of this research method. Special topics relating to social justice include a focus on ethics and working with marginalized communities, sensitive concerns during data collection, and presenting the work to communities and policymakers, as well as academics. Written for students and researchers new to photovoice, this brief text takes readers from the process of conceptualizing and implementing a photovoice study to analyzing data and finally presenting the results of the study. The book concludes with suggestions for future iterations of photovoice, including web based resources and digital storytelling. The authors take into account the realities of photovoice as a method by providing practical, applied tools including sample consent forms, presentations, recruitment flyers, and photo-taking tips. Using Photovoice for Social Justice, new and experienced researchers can design, implement, and analyze their photovoice projects. |
| data science for social justice: Quantitative Research Methods in Communication Erica Scharrer, Srividya Ramasubramanian, 2021-05-13 This textbook is an advanced introduction to quantitative methods for students in communication and allied social science disciplines that focuses on why and how to conduct research that contributes to social justice. Today’s researchers are inspired by the potential for scholarship to make a difference for society, to push toward more just and equitable ends, and to engage in dialogue with members of the public so that they can make decisions about how to navigate the social, cultural, and political world equipped with accurate, fair, and up-to-date knowledge. This book illustrates the mechanics and the meaning behind quantitative research methods by illustrating each step in the research design process with research addressing questions of social justice. It provides practical guidance for researchers who wish to engage in the transformation of structures, practices, and understandings in society through community and civic engagement and policy formation. It contains step-by-step guidance in quantitative methods—from conceptualization through all the stages of execution of a study, including providing a detailed guide for statistical analysis—and demonstrates how researchers can engage with social justice issues in systematic, rigorous, ethical, and meaningful ways. This text serves as a core or supplementary textbook for graduate and advanced undergraduate courses in research methods for communication and social sciences and fills a gap for a methods text that is responsive to the desire of scholars to conduct socially impactful research. |
| data science for social justice: 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 justice: Social Justice Talk Chris Hass, 2020 The author shows how K-5 teachers can introduce the importance, discuss, and explore social justice practices for younger students-- |
| data science for social justice: Handbook of Social Justice Theory and Research Clara Sabbagh, Manfred Schmitt, 2016-02-08 The International Society for Justice Research (ISJR) aims to provide a platform for interdisciplinary justice scholars who are encouraged to present and exchange their ideas. This exchange has yielded a fruitful advance of theoretical and empirically-oriented justice research. This volume substantiates this academic legacy and the research prospects of the ISJR in the field of justice theory and research. Included are themes and topics such as the theory of the justice motive, the mapping of the multifaceted forms of justice (distributive, procedural) and justice in context-bound spheres (e.g. non-humans). It presents a comprehensive state of the art overview in the field of justice research theory and it puts forth an agenda for future interdisciplinary and international justice research. It is worth noting that authors in this proposed volume represent ISJR's leading scholarship. Thus, the compilation of their research within a single framework exposes potential readers to high quality academic work that embodies the past, current and future trends of justice research. |
| data science for social justice: Research Justice Andrew Jolivétte, 2015-07-22 Challenging traditional models for conducting social science research within marginalized populations, -research justice- is a strategic framework and methodological intervention that aims to transform structural inequalities in research. This book is the first to offer a close analysis of that framework and present a radical approach to socially just, community-centered research. It is built around a vision of equal political power and legitimacy for different forms of knowledge, including the cultural, spiritual, and experiential, with the goal of greater equality in public policies and laws that rely on data and research to produce social change. |
| data science for social justice: Data and Society Anne Beaulieu, Sabina Leonelli, 2021-10-27 Data and Society: A Critical Introduction investigates the growing importance of data as a technological, social, economic and scientific resource. It explains how data practices have come to underpin all aspects of human life and explores what this means for those directly involved in handling data. The book fosters informed debate over the role of data in contemporary society explains the significance of data as evidence beyond the Big Data hype spans the technical, sociological, philosophical and ethical dimensions of data provides guidance on how to use data responsibly includes data stories that provide concrete cases and discussion questions. Grounded in examples spanning genetics, sport and digital innovation, this book fosters insight into the deep interrelations between technical, social and ethical aspects of data work. |
| data science for social justice: Doing Qualitative Research in Social Work Ian Shaw, Sally Holland, 2014-04-15 Bringing key developments and debates together in a single volume, this book provides an authoritative guide for students and practitioners embarking on qualitative research in social work and related fields. Frequently illustrated with contemporary and classic case examples from the authors’ own empirical research and from international published work, and with self-directed learning tasks, the book provides insight into the difficulties and complexities of carrying out research, as well as sharing ‘success’ stories from the field. Shaw and Holland have long experience of writing for practitioners and students and in making complex concepts accessible and readable, making this an ideal text for those engaging in qualitative social work research at any level. Ian Shaw is a Professor of Social Work at the University of York and at the University of Aalborg. Sally Holland is a Reader in Social Work at the School of Social Sciences in Cardiff University. |
| data science for social justice: Race Decoded Catherine Bliss, 2012-05-23 In 2000, with the success of the Human Genome Project, scientists declared the death of race in biology and medicine. But within five years, many of these same scientists had reversed course and embarked upon a new hunt for the biological meaning of race. Drawing on personal interviews and life stories, Race Decoded takes us into the world of elite genome scientists—including Francis Collins, director of the NIH; Craig Venter, the first person to create a synthetic genome; and Spencer Wells, National Geographic Society explorer-in-residence, among others—to show how and why they are formulating new ways of thinking about race. In this original exploration, Catherine Bliss reveals a paradigm shift, both at the level of science and society, from colorblindness to racial consciousness. Scientists have been fighting older understandings of race in biology while simultaneously promoting a new grand-scale program of minority inclusion. In selecting research topics or considering research design, scientists routinely draw upon personal experience of race to push the public to think about race as a biosocial entity, and even those of the most privileged racial and social backgrounds incorporate identity politics in the scientific process. Though individual scientists may view their positions differently—whether as a black civil rights activist or a white bench scientist—all stakeholders in the scientific debates are drawing on memories of racial discrimination to fashion a science-based activism to fight for social justice. |
| data science for social justice: Data Scientists at Work Sebastian Gutierrez, 2014-12-12 Data Scientists at Work is a collection of interviews with sixteen of the world's most influential and innovative data scientists from across the spectrum of this hot new profession. Data scientist is the sexiest job in the 21st century, according to the Harvard Business Review. By 2018, the United States will experience a shortage of 190,000 skilled data scientists, according to a McKinsey report. Through incisive in-depth interviews, this book mines the what, how, and why of the practice of data science from the stories, ideas, shop talk, and forecasts of its preeminent practitioners across diverse industries: social network (Yann LeCun, Facebook); professional network (Daniel Tunkelang, LinkedIn); venture capital (Roger Ehrenberg, IA Ventures); enterprise cloud computing and neuroscience (Eric Jonas, formerly Salesforce.com); newspaper and media (Chris Wiggins, The New York Times); streaming television (Caitlin Smallwood, Netflix); music forecast (Victor Hu, Next Big Sound); strategic intelligence (Amy Heineike, Quid); environmental big data (André Karpištšenko, Planet OS); geospatial marketing intelligence (Jonathan Lenaghan, PlaceIQ); advertising (Claudia Perlich, Dstillery); fashion e-commerce (Anna Smith, Rent the Runway); specialty retail (Erin Shellman, Nordstrom); email marketing (John Foreman, MailChimp); predictive sales intelligence (Kira Radinsky, SalesPredict); and humanitarian nonprofit (Jake Porway, DataKind). The book features a stimulating foreword by Google's Director of Research, Peter Norvig. Each of these data scientists shares how he or she tailors the torrent-taming techniques of big data, data visualization, search, and statistics to specific jobs by dint of ingenuity, imagination, patience, and passion. Data Scientists at Work parts the curtain on the interviewees’ earliest data projects, how they became data scientists, their discoveries and surprises in working with data, their thoughts on the past, present, and future of the profession, their experiences of team collaboration within their organizations, and the insights they have gained as they get their hands dirty refining mountains of raw data into objects of commercial, scientific, and educational value for their organizations and clients. |
| data science for social justice: A Scientific Framework for Compassion and Social Justice Jacob A. Sadavoy, Michelle L. Zube, 2021-07-21 A Scientific Framework for Compassion and Social Justice provides readers with an in-depth understanding of the behavior analytic principles that maintain social justice issues and highlights behavior analytic principles that promote self-awareness and compassion. Expanding on the goals of the field of applied behavioral analysis (ABA), this collection of essays from subject-matter experts in various fields combines personal experiences, scientific explanations, and effective strategies to promote a better existence; a better world. Chapters investigate the self-imposed barriers that contribute to human suffering and offer scientific explanations as to how the environment can systematically be shaped and generate a sociocultural system that promotes harmony, equality, fulfilment, and love. The goal of this text is to help the reader focus overwhelming feelings of confusion and upheaval into action and to make a stand for social justice while mobilizing others to take value-based actions. The lifelong benefit of these essays extends beyond ABA practitioners to readers in gender studies, diversity studies, education, public health, and other mental health fields. |
| data science for social justice: Social Studies, Literacy, and Social Justice in the Elementary Classroom Ruchi Agarwal-Rangnath, 2022-09-23 Elementary-aged children are often positioned as not developmentally ready to learn about race, racism, and injustice. Yet, the classroom materials used in most schools misrepresent history, withhold knowledge about racial injustice, or fail to uplift stories of resilience and resistance. For almost a decade, this groundbreaking resource has been one of the most highly used textbooks in justice-oriented social studies methods courses for grades 3-8. The author has thoroughly revised her bestseller to provide additional lessons that are more deeply situated within the current context of converging pandemics--COVID-19, racism, and impending environmental catastrophe. Grounded in the daily realities of public schools, Agarwal-Rangnath shows teachers how to use primary and other sources that will offer students new ways of thinking about history while meeting language arts standards for information text proficiency and critical thinking. Educators will also learn how to teach language arts and social studies as complementary subjects. New for the Second Edition: More concrete connections between theory and practice. Additional lesson examples that are centered in today's context of converging pandemics. Reflection questions that challenge readers to think about ways to navigate curricular constraints and standardization in the classroom. |
| data science for social justice: Design Justice Sasha Costanza-Chock, 2020-03-03 An exploration of how design might be led by marginalized communities, dismantle structural inequality, and advance collective liberation and ecological survival. What is the relationship between design, power, and social justice? “Design justice” is an approach to design that is led by marginalized communities and that aims expilcitly to challenge, rather than reproduce, structural inequalities. It has emerged from a growing community of designers in various fields who work closely with social movements and community-based organizations around the world. This book explores the theory and practice of design justice, demonstrates how universalist design principles and practices erase certain groups of people—specifically, those who are intersectionally disadvantaged or multiply burdened under the matrix of domination (white supremacist heteropatriarchy, ableism, capitalism, and settler colonialism)—and invites readers to “build a better world, a world where many worlds fit; linked worlds of collective liberation and ecological sustainability.” Along the way, the book documents a multitude of real-world community-led design practices, each grounded in a particular social movement. Design Justice goes beyond recent calls for design for good, user-centered design, and employment diversity in the technology and design professions; it connects design to larger struggles for collective liberation and ecological survival. |
| data science for social justice: Social Justice and Social Work Michael J. Austin, 2013-03-26 This unique and timely book, edited by Michael J. Austin, introduces and connects social justice to the core values of social work across the curriculum. It presents the history and philosophy that supports social justice and ties it to ethical concepts that will help readers understand social justice as a core social work value. The book further conveys the importance of amplifying client voice; explores organization-based advocacy; and describes how an understanding of social justice can inform practice and outlines implications for education and practice. |
| data science for social justice: Street Data Shane Safir, Jamila Dugan, 2021-02-12 Radically reimagine our ways of being, learning, and doing Education can be transformed if we eradicate our fixation on big data like standardized test scores as the supreme measure of equity and learning. Instead of the focus being on fixing and filling academic gaps, we must envision and rebuild the system from the student up—with classrooms, schools and systems built around students’ brilliance, cultural wealth, and intellectual potential. Street data reminds us that what is measurable is not the same as what is valuable and that data can be humanizing, liberatory and healing. By breaking down street data fundamentals: what it is, how to gather it, and how it can complement other forms of data to guide a school or district’s equity journey, Safir and Dugan offer an actionable framework for school transformation. Written for educators and policymakers, this book · Offers fresh ideas and innovative tools to apply immediately · Provides an asset-based model to help educators look for what’s right in our students and communities instead of seeking what’s wrong · Explores a different application of data, from its capacity to help us diagnose root causes of inequity, to its potential to transform learning, and its power to reshape adult culture Now is the time to take an antiracist stance, interrogate our assumptions about knowledge, measurement, and what really matters when it comes to educating young people. |
| data science for social justice: Digital Dead End Virginia Eubanks, 2012-09-21 The realities of the high-tech global economy for women and families in the United States. The idea that technology will pave the road to prosperity has been promoted through both boom and bust. Today we are told that universal broadband access, high-tech jobs, and cutting-edge science will pull us out of our current economic downturn and move us toward social and economic equality. In Digital Dead End, Virginia Eubanks argues that to believe this is to engage in a kind of magical thinking: a technological utopia will come about simply because we want it to. This vision of the miraculous power of high-tech development is driven by flawed assumptions about race, class, and gender. The realities of the information age are more complicated, particularly for poor and working-class women and families. For them, information technology can be both a tool of liberation and a means of oppression. But despite the inequities of the high-tech global economy, optimism and innovation flourished when Eubanks worked with a community of resourceful women living at her local YWCA. Eubanks describes a new approach to creating a broadly inclusive and empowering “technology for people,” popular technology, which entails shifting the focus from teaching technical skill to nurturing critical technological citizenship, building resources for learning, and fostering social movement. Important Notice: The digital edition of this book is missing some of the images found in the physical edition. |
| data science for social justice: Adventures in Social Research Earl R. Babbie, 2011 Click on the Supplements tab above for further details on the different versions of SPSS programs. |
| data science for social justice: Knowledge Justice Sofia Y. Leung, Jorge R. Lopez-McKnight, 2021-04-13 Black, Indigenous, and Peoples of Color--reimagine library and information science through the lens of critical race theory. In Knowledge Justice, Black, Indigenous, and Peoples of Color scholars use critical race theory (CRT) to challenge the foundational principles, values, and assumptions of Library and Information Science and Studies (LIS) in the United States. They propel CRT to center stage in LIS, to push the profession to understand and reckon with how white supremacy affects practices, services, curriculum, spaces, and policies. |
| data science for social justice: Data Conscience Brandeis Hill Marshall, 2022-08-19 DATA CONSCIENCE ALGORITHMIC S1EGE ON OUR HUM4N1TY EXPLORE HOW D4TA STRUCTURES C4N HELP OR H1NDER SOC1AL EQU1TY Data has enjoyed ‘bystander’ status as we’ve attempted to digitize responsibility and morality in tech. In fact, data’s importance should earn it a spot at the center of our thinking and strategy around building a better, more ethical world. It’s use—and misuse—lies at the heart of many of the racist, gendered, classist, and otherwise oppressive practices of modern tech. In Data Conscience: Algorithmic Siege on our Humanity, computer science and data inclusivity thought leader Dr. Brandeis Hill Marshall delivers a call to action for rebel tech leaders, who acknowledge and are prepared to address the current limitations of software development. In the book, Dr. Brandeis Hill Marshall discusses how the philosophy of “move fast and break things” is, itself, broken, and requires change. You’ll learn about the ways that discrimination rears its ugly head in the digital data space and how to address them with several known algorithms, including social network analysis, and linear regression A can’t-miss resource for junior-level to senior-level software developers who have gotten their hands dirty with at least a handful of significant software development projects, Data Conscience also provides readers with: Discussions of the importance of transparency Explorations of computational thinking in practice Strategies for encouraging accountability in tech Ways to avoid double-edged data visualization Schemes for governing data structures with law and algorithms |
| data science for social justice: Anti-Bias Education for Young Children and Ourselves Louise Derman-Sparks, Julie Olsen Edwards, 2020-04-07 Anti-bias education begins with you! Become a skilled anti-bias teacher with this practical guidance to confronting and eliminating barriers. |
| data science for social justice: Public Health and Social Justice Martin T. Donohoe, 2012-10-10 Praise for Public Health and Social Justice This compilation unifies ostensibly distant corners of our broad discipline under the common pursuit of health as an achievable, non-negotiable human right. It goes beyond analysis to impassioned suggestions for moving closer to the vision of health equity. —Paul Farmer, MD, PhD, Kolokotrones University Professor and chair, Department of Global Health and Social Medicine, Harvard Medical School; co-founder, Partners In Health This superb book is the best work yet concerning the relationships between public health and social justice. —Howard Waitzkin, MD, PhD, Distinguished Professor Emeritus, University of New Mexico This book gives public health professionals, researchers and advocates the essential knowledge they need to capture the energy that social justice brings to our enterprise. —Nicholas Freudenberg, DrPH, Distinguished Professor of Public Health, the City University of New York School of Public Health at Hunter College The breadth of topics selected provides a strong overview of social justice in medicine and public health for readers new to the topic. —William Wiist, DHSc, MPH, MS, senior scientist and head, Office of Health and Society Studies, Interdisciplinary Health Policy Institute, Northern Arizona University This book is a tremendous contribution to the literature of social justice and public health. —Catherine Thomasson, MD, executive director, Physicians for Social Responsibility This book will serve as an essential reference for students, teachers and practitioners in the health and human services who are committed to social responsibility. —Shafik Dharamsi, PhD, faculty of medicine, University of British Columbia |
| data science for social justice: Critical Perspectives on Social Justice in Speech-Language Pathology Horton, RaMonda, 2021-06-25 There is very little discussion of socially just approaches to speech-language pathology. Within other fields of clinically-oriented practice, social justice is a topic that has received a great deal of attention within the last few years. Pedagogy for addressing social justice has been developed in other disciplines. The field of communication disorders has failed to move forward and do the same. Discussion of social justice is important given the current sociopolitical climate and landscape that clients carry out in their day-to-day functioning. Speech-language pathologists (SLPs) have an opportunity to engage in practices that help address and alleviate some of the injustices that contribute to educational and health disparities experienced by communities of color. They may do this through the development and application of a socially just orientation of culturally competent practice that fosters changes beyond the individual level. Adapting such a framework makes it possible for SLPs to effectively advocate for and foster equity and inclusion for the individuals and broader communities impacted by SLP services. Critical Perspectives on Social Justice in Speech-Language Pathology addresses the socio-political contexts of how the field of speech-language pathology and service delivery can impact policy and debates related to social justice issues. It explores social position factors and the experiences of marginalized communities to explore how speech-language pathologists deliver services, train and prepare students, and carry out research in communities of color. It covers topic areas including disproportionality in special education, disability rights and ableism, achievement and opportunity gaps, health disparities, and LGBTQ+ rights with a focus on voice, communication, and gender-diverse populations. This book is essential for speech-language pathologists, administrators, practitioners, researchers, academicians, and students interested in how the SLP profession and discipline can contribute to or develop efforts to help address injustices faced by Black, Indigenous, and people of color (BIPOC) communities. |
| data science for social justice: Urban Science Education for the Hip-hop Generation Christopher Emdin, 2010 Christopher Emdin is an assistant professor of science education and director of secondary school initiatives at the Urban Science Education Center at Teachers College, Columbia University. He holds a Ph.D. in urban education with a concentration in mathematics, science and technology; a master's degree in natural sciences; and a bachelor's degree in physical anthropology, biology, and chemistry. His book, Urban Science Education for the Hip-Hop Generation is rooted in his experiences as student, teacher, administrator, and researcher in urban schools and the deep relationship between hip-hop culture and science that he discovered at every stage of his academic and professional journey. The book utilizes autobiography, outcomes of research studies, theoretical explorations, and accounts of students' experiences in schools to shed light on the causes for the lack of educational achievement of urban youth from the hip-hop generation. |
| data science for social justice: Social Justice and Library Work Stephen Bales, 2017-10-18 Although they may not have always been explicitly stated, library work has always had normative goals. Until recently, such goals have largely been abstract; they are things like knowledge creation, education, forwarding science, preserving history, supporting democracy, and safeguarding civilization. The modern spirit of social and cultural critique, however, has focused our attention on the concrete, material relationships that determine human potentiality and opportunity, and library workers are increasingly seeing the institution of the library, as well as library work, as embedded in a web of relations that extends beyond the library's traditional sphere of influence. In light of this critical consciousness, more and more library and information science professionals are coming to see themselves as change agents and front-line advocates of social justice issues. This book will serve as a guide for those library workers and related information professionals that disregard traditional ideas of library neutrality and static, idealized conceptions of Western culture. The book will work as an entry point for those just forming a consciousness oriented towards social justice work and will be also be of value to more experienced transformative library workers as an up-to-date supplement to their praxis. - Justifies the use of a variety of theoretical and practical resources for effecting positive change - Explores the role of the librarian as change agents |
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 enable a …
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 to …
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 …
