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| data science and social work: Data Analysis for Social Workers Denise Montcalm, David Daniel Royse, 2002 This user-friendly text is designed specifically for social work students who are intimidated by the prospect of taking a Statistics course. With its engaging, conversational writing style and numerous examples and problems, social work students will quickly learn to understand and interpret both quantitative and qualitative data. The text's flexibility makes it suitable for a variety of teaching styles. Instructors who want their students to get the actual feel of computing a chi square can use the Formula Alerts to calculate certain statistics manually; other instructors may want students to use the step-by-step computer applications to produce the same results. |
| data science and social work: Artificial Intelligence and Social Work Milind Tambe, Eric Rice, 2018-11-29 An introductory guide with real-life examples on using AI to help homeless youth, diabetes patients, and other social welfare interventions. |
| data science and social work: 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 and social work: Science and Pseudoscience in Social Work Practice Bruce A. Thyer, PhD, LCSW, BCBA-D, Monica G. Pignotti, PhD, LMSW, 2015-05-15 Science is a way of thinking about and investigating the accuracy of assumptions about the world. It is a process for solving problems in which we learn from our mistakes. Social work has a long history of social reform and helping efforts. Let us continue this by paying attention to the important message of this book. --Eileen Gambrill, PhD, School of Social Welfare University of California at Berkeley (From the Foreword) Although many psychosocial interventions used in social work practice have strong research evidence supporting their efficacy, a surprising number do not, potentially resulting in harmful outcomes. In this book, the authors cast a critical eye on the reality of commonly used scientific and pseudoscientific practices in social work today. Stressing the need for separating research-based practices from those not supported by adequate levels of evidence, they examine the scientific and pseudoscientific bases for popular social work interventions used in a variety of treatment settings. The text examines the misuse of legitimate research and describes how social work education training can and should discourage pseudoscience. The concluding chapter describes pathways through which social work practice can become more firmly grounded in contemporary scientific research. This engaging book is intended for courses in critical thinking and evidence-based practice and is a valuable resource for all social work students and practitioners. Key Features: Promotes critical thinking regarding the evidence-based research--or lack thereof--behind a variety of social work interventions Written by renowned social work educators Addresses the history and characteristics of pseudoscience Examines pseudoscience practices in assessment and work with children, adolescents, adults, and individuals with developmental difficulties |
| data science and social work: Introduction to Data Science for Social and Policy Research Jose Manuel Magallanes Reyes, 2017-09-21 This comprehensive guide provides a step-by-step approach to data collection, cleaning, formatting, and storage, using Python and R. |
| data science and social work: Annals of the American Academy of Political and Social Science , 1914 |
| data science and social work: Graduate Research Methods in Social Work Matthew P. DeCarlo, Cory R. Cummings, Kate Agnelli, 2020-07-10 |
| data science and social work: Shaping a Science of Social Work John S. Brekke, Jeane W. Anastas, 2019 Shaping a Science of Social Work provides a basic framework for a social work science in terms of basic constructs, domains, and characteristics, considered within the context of academic disciplinarity and professional identity. Centered on the formation of social work science from a realist/critical-realist position, contributions from eminent scholars offer detailed and rigorous analyses of various essential issues. |
| data science and social work: 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 and social work: 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 and social work: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| data science and social work: 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 and social work: Data Science and Social Research N. Carlo Lauro, Enrica Amaturo, Maria Gabriella Grassia, Biagio Aragona, Marina Marino, 2017-11-17 This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources. This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in academia as well as in statistical institutes and offices. |
| data science and social work: Decoding the Social World Sandra Gonzalez-Bailon, 2017-12-22 How data science and the analysis of networks help us solve the puzzle of unintended consequences. Social life is full of paradoxes. Our intentional actions often trigger outcomes that we did not intend or even envision. How do we explain those unintended effects and what can we do to regulate them? In Decoding the Social World, Sandra González-Bailón explains how data science and digital traces help us solve the puzzle of unintended consequences—offering the solution to a social paradox that has intrigued thinkers for centuries. Communication has always been the force that makes a collection of people more than the sum of individuals, but only now can we explain why: digital technologies have made it possible to parse the information we generate by being social in new, imaginative ways. And yet we must look at that data, González-Bailón argues, through the lens of theories that capture the nature of social life. The technologies we use, in the end, are also a manifestation of the social world we inhabit. González-Bailón discusses how the unpredictability of social life relates to communication networks, social influence, and the unintended effects that derive from individual decisions. She describes how communication generates social dynamics in aggregate (leading to episodes of “collective effervescence”) and discusses the mechanisms that underlie large-scale diffusion, when information and behavior spread “like wildfire.” She applies the theory of networks to illuminate why collective outcomes can differ drastically even when they arise from the same individual actions. By opening the black box of unintended effects, González-Bailón identifies strategies for social intervention and discusses the policy implications—and how data science and evidence-based research embolden critical thinking in a world that is constantly changing. |
| data science and social work: Social Work Research Methods Reginald O. York, 2020 KEY FEATURES: A ‵‵learn by doing approach woven throughout the text helps students apply knowledge to practice. A three-part structure introduces the fundamentals of research methods, the different types of social work research, and the use of data analysis for evaluation of social work practice. Chapter-opening vignettes illustrate the value of chapter content to the practicing social worker. Chapter-ending practice exercises provide opportunities to apply chapter knowledge and gain a better understanding of research competencies. Practical guidelines for data analysis show students how to use the Internet to analyze data with simple step-by-step instructions. Discussion questions provide opportunities to spark class discussions and help students reflect on critical concepts. Chapter tests, lists of key learnings, and glossaries at the end of chapters serve as convenient tools for reviewing key concepts and definitions. |
| data science and social work: Practice-Based Research in Social Work Sarah-Jane Dodd, Irwin Epstein, 2012-03-12 This unique textbook explores practice-based research (PBR), using numerous practice examples to actively encourage and engage students and practitioners to embrace research as a meaningful support for their practice. Whilst evidence-based practice gives practitioners access to information about universal best practices, it does not prioritize practitioner-generated knowledge or promote new research-based interventions relevant to their own practice circumstances as PBR does. This book discusses the evolution of PBR as a distinct social work research approach, describes its principles and methods and presents a range of exemplars illustrating the application of PBR within different practice methods in different practice settings. The chapters cover: Identifying the research question in a PBR model Designing a study and identifying a methodology Sampling Literature reviews Gathering data Ethics Analyzing data and interpreting results Putting research into practice Viewing the practitioner as central to the research process, and research as a necessary component of practice, this invaluable book emphasizes the seamless integration of practice and research. It is about research in social work practice rather than research on social work practice. Each chapter includes an overview, an introduction, and a key concepts summary. Practice-Based Research in Social Work is a very accessible text suitable for social work students, particularly MSW students, and practitioners. |
| data science and social work: Human-Centered Data Science Cecilia Aragon, Shion Guha, Marina Kogan, Michael Muller, Gina Neff, 2022-03-01 Best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of large datasets. Human-centered data science is a new interdisciplinary field that draws from human-computer interaction, social science, statistics, and computational techniques. This book, written by founders of the field, introduces best practices for addressing the bias and inequality that may result from the automated collection, analysis, and distribution of very large datasets. It offers a brief and accessible overview of many common statistical and algorithmic data science techniques, explains human-centered approaches to data science problems, and presents practical guidelines and real-world case studies to help readers apply these methods. The authors explain how data scientists’ choices are involved at every stage of the data science workflow—and show how a human-centered approach can enhance each one, by making the process more transparent, asking questions, and considering the social context of the data. They describe how tools from social science might be incorporated into data science practices, discuss different types of collaboration, and consider data storytelling through visualization. The book shows that data science practitioners can build rigorous and ethical algorithms and design projects that use cutting-edge computational tools and address social concerns. |
| data science and social work: Programming with Python for Social Scientists Phillip D. Brooker, 2019-12-09 As data become ′big′, fast and complex, the software and computing tools needed to manage and analyse them are rapidly developing. Social scientists need new tools to meet these challenges, tackle big datasets, while also developing a more nuanced understanding of - and control over - how these computing tools and algorithms are implemented. Programming with Python for Social Scientists offers a vital foundation to one of the most popular programming tools in computer science, specifically for social science researchers, assuming no prior coding knowledge. It guides you through the full research process, from question to publication, including: the fundamentals of why and how to do your own programming in social scientific research, questions of ethics and research design, a clear, easy to follow ′how-to′ guide to using Python, with a wide array of applications such as data visualisation, social media data research, social network analysis, and more. Accompanied by numerous code examples, screenshots, sample data sources, this is the textbook for social scientists looking for a complete introduction to programming with Python and incorporating it into their research design and analysis. |
| data science and social work: Teaching Nonprofit Management Karabi C. Bexboruah, Heather L. Carpenter, 2020-04-24 This peer-reviewed edited volume provides strategies and practices for teaching nonprofit management theories and concepts in the context of the undergraduate, graduate, and online classroom environments. |
| data science and social work: Data Mining For Dummies Meta S. Brown, 2014-09-04 Delve into your data for the key to success Data mining is quickly becoming integral to creating value and business momentum. The ability to detect unseen patterns hidden in the numbers exhaustively generated by day-to-day operations allows savvy decision-makers to exploit every tool at their disposal in the pursuit of better business. By creating models and testing whether patterns hold up, it is possible to discover new intelligence that could change your business's entire paradigm for a more successful outcome. Data Mining for Dummies shows you why it doesn't take a data scientist to gain this advantage, and empowers average business people to start shaping a process relevant to their business's needs. In this book, you'll learn the hows and whys of mining to the depths of your data, and how to make the case for heavier investment into data mining capabilities. The book explains the details of the knowledge discovery process including: Model creation, validity testing, and interpretation Effective communication of findings Available tools, both paid and open-source Data selection, transformation, and evaluation Data Mining for Dummies takes you step-by-step through a real-world data-mining project using open-source tools that allow you to get immediate hands-on experience working with large amounts of data. You'll gain the confidence you need to start making data mining practices a routine part of your successful business. If you're serious about doing everything you can to push your company to the top, Data Mining for Dummies is your ticket to effective data mining. |
| data science and social work: Interpersonal Social Work Skills for Community Practice Donna Hardina, PhD, 2012-07-23 Specifically dedicated to the skills that social workers need to advance community practice, this creative book is long overdue. Grounded in the wisdom and evidence of well-honed interpersonal social work skills...Donna Hardina's new text takes community practice to a higher level than ever before developed in book form; indeed she displays the most thorough understanding of research on community practice that I have read in any community practice text.--Journal of Teaching in Social Work Community organization has been a major component of social work practice since the late 19th century. It requires a diverse set of abilities, interpersonal skills being among the most important. This textbook describes the essential interpersonal skills that social workers need in community practice and helps students cultivate them. Drawing from empirical literature on community social work practice and the authorís own experience working with community organizers, the book focuses on developing the macro-level skills that are especially useful for community organizing. It covers relationship-building, interviewing, recruitment, community assessment, facilitating group decision-making and task planning, creating successful interventions, working with organizations, and program evaluation, along with examples of specific applications. For clarity and ease of use, the author employs a framework drawn from a variety of community practice models, including social action and social planning, transformative/popular education and community development approaches, and multicultural and feminist approaches. The text is linked to the competencies outlined in the Council of Social Work Educationís (2008) Educational Policy and Accreditation Standards (EPAS), as well as ethics and values identified in the National Association of Social Workersí (NASW) Code of Ethics, and the International Federation of Social Workersí statement of ethical principles. Most chapters begin with a quote from a community organizer explaining how interpersonal skills are used in practice, and student exercises conclude each chapter. The text also addresses other important skills such as legislative advocacy, lobbying, and supervision. Key Features: Describes the essential skills social workers need in community practice and how to acquire them Includes examples of specific applications drawn from empirical literature and the authorís experience working with community organizers Grounded in social justice, strengths-based, and human rights perspectives Linked to competencies outlined in EPAS and values identified in the NASW Code of Ethics Based on a variety of community practice models |
| data science and social work: Handbook of Statistical Analysis and Data Mining Applications Ken Yale, Robert Nisbet, Gary D. Miner, 2017-11-09 Handbook of Statistical Analysis and Data Mining Applications, Second Edition, is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers, both academic and industrial, through all stages of data analysis, model building and implementation. The handbook helps users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application. This book is an ideal reference for users who want to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques and discusses their application to real problems in ways accessible and beneficial to practitioners across several areas—from science and engineering, to medicine, academia and commerce. - Includes input by practitioners for practitioners - Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models - Contains practical advice from successful real-world implementations - Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions - Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications |
| data science and social work: Failing at Fairness Myra Sadker, David Sadker, 2010-05-11 Failing at Fairness, the result of two decades of research, shows how gender bias makes it impossible for girls to receive an education equal to that given to boys. Girls' learning problems are not identified as often as boys' are Boys receive more of their teachers' attention Girls start school testing higher in every academic subject, yet graduate from high school scoring 50 points lower than boys on the SAT Hard-hitting and eye-opening, Failing at Fairness should be read by every parent, especially those with daughters. |
| data science and social work: The Signal and the Noise Nate Silver, 2015-02-03 One of the more momentous books of the decade. —The New York Times Book Review Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger—all by the time he was thirty. He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of the website FiveThirtyEight. Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the “prediction paradox”: The more humility we have about our ability to make predictions, the more successful we can be in planning for the future. In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball to global pandemics, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good—or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary—and dangerous—science. Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise. With everything from the health of the global economy to our ability to fight terrorism dependent on the quality of our predictions, Nate Silver’s insights are an essential read. |
| data science and social work: Introduction to Data Science for Social and Policy Research Jose Manuel Magallanes Reyes, 2017-09-21 Real-world data sets are messy and complicated. Written for students in social science and public management, this authoritative but approachable guide describes all the tools needed to collect data and prepare it for analysis. Offering detailed, step-by-step instructions, it covers collection of many different types of data including web files, APIs, and maps; data cleaning; data formatting; the integration of different sources into a comprehensive data set; and storage using third-party tools to facilitate access and shareability, from Google Docs to GitHub. Assuming no prior knowledge of R and Python, the author introduces programming concepts gradually, using real data sets that provide the reader with practical, functional experience. |
| data science and social work: 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 and social work: Grand Challenges for Social Work and Society Richard P. Barth, Jill Theresa Messing, Trina R. Shanks, James Herbert Williams, 2022 Grand challenges represent a focused method of attacking the most deeply significant problems of a discipline, organization, or society itself. Since the concept was first introduced over a century ago, more than 600 governments, foundations, and professions subsequently adopted this language and approach, often to excellent effect. In 2012, the social work profession launched its own national initiative, with aim of using science, innovation, and new forms of collaboration to accelerate progress toward critically needed social solutions. There was also strong corollary interest in changing the profession itself, introducing new forms of practice and problem-solving. The American Academy of Social Work and Social Welfare served as the first home of the Grand Challenges initiative in social work; in 2017, as the initiative grew more complex, it became an independent organization-- |
| data science and social work: How Does Social Science Work? Paul Diesing, 1992-03-15 The culmination of a lifetime spent in a variety of fields - sociology, anthropology, economics, psychology, and philosophy of science - How Does Social Science Work? takes an innovative, sometimes iconoclastic look at social scientists at work in many disciplines. It describes how they investigate and the kinds of truth they produce, illuminating the weaknesses and dangers inherent in their research.At once an analysis, a critique, and a synthesis, this major study begins by surveying philosophical approaches to hermeneutics, to examine the question of how social science ought to work. It illustrates many of its arguments with untraditional examples, such as the reception of the work of the political biographer Robert Caro to show the hermeneutical problems of ethnographers. The major part of the book surveys sociological, political, and psychological studies of social science to get a rounded picture of how social science works,Paul Diesling warns that social science exists between two opposite kinds of degeneration, a value-free professionalism that lives only for publications that show off the latest techniques, and a deep social concern that uses science for propaganda. He argues for greater self-awareness and humility among social scientists, although he notes that some social scientists . . . will angrily reject the thought that their personality affects their research in any way.This profound and sometimes witty book will appeal to students and practitioners in the social sciences who are ready to take a fresh look at their field. An extensive bibliography provides a wealth of references across an array of social science disciplines. |
| data science and social work: 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 and social work: 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 and social work: Ethics and Data Science Mike Loukides, Hilary Mason, DJ Patil, 2018-07-25 As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes: A sample checklist that you can adapt for your own procedures Five framing guidelines (the Five C’s) for building data products: consent, clarity, consistency, control, and consequences Suggestions for building ethics into your data-driven culture Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today. |
| data science and social work: Environmental Social Work Mel Gray, John Coates, Tiani Hetherington, 2013 Divided into three parts, this field-defining work explores what environmental social work is, and how it can be put into practice. It focuses on theory, discussing ecological and social justice, as well as sustainability, spirituality and human rights. |
| data science and social work: Data Visualization Kieran Healy, 2018-12-18 An accessible primer on how to create effective graphics from data This book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way. Data Visualization builds the reader’s expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective “small multiple” plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible. Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings. Provides hands-on instruction using R and ggplot2 Shows how the “tidyverse” of data analysis tools makes working with R easier and more consistent Includes a library of data sets, code, and functions |
| data science and social work: Criminal Defense-Based Forensic Social Work Ashley Ratliff, Maren Willins, 2018-10-04 This book draws upon the Colorado Model of Criminal Defense-Based Forensic Social Work – a holistic, client-centered, collaborative approach that uses a trauma-informed care framework – to outline the numerous roles and skills of a forensic social worker. The comprehensive, developmentally informed model employs a past (e.g., mitigation themes and life history compilation), present (e.g., client contact and current functioning support), and future (e.g., reentry services) framework to provide mitigation narratives for defendants and to create a comprehensive approach to service. The text starts with an overview of practice standards, ethical considerations, and legal frameworks. Next, chapters examine the unique roles that a forensic social worker must take on and the skills they need to possess. These include using clinical interventions with clients in nonclinical settings, working with clients of different identities and backgrounds, assisting with reentry planning for incarcerated clients, and collaborating with experts outside of the defense team. Finally, the authors provide strategies for practitioners to engage in their own self-care. Interwoven with four case studies using the Colorado Model, this book will be valuable reading for graduate schools of social work, law school programs which have clinics or direct practice components to legal studies, and at defender agencies who contract with or employ social workers on staff. |
| data science and social work: Social Science Research Anol Bhattacherjee, 2012-04-01 This book is designed to introduce doctoral and graduate students to the process of conducting scientific research in the social sciences, business, education, public health, and related disciplines. It is a one-stop, comprehensive, and compact source for foundational concepts in behavioral research, and can serve as a stand-alone text or as a supplement to research readings in any doctoral seminar or research methods class. This book is currently used as a research text at universities on six continents and will shortly be available in nine different languages. |
| data science and social work: Social Work and Science in the 21st Century Leon H. Ginsberg, Christopher R. Larrison, Larry Nackerud, John R. Barner, Lauren A. Ricciardelli, 2019-03-11 Social Work and Science in the 21st Century enhances the inclusion of natural science concepts and knowledge into social work education and practice. The book highlights basic scientific theories and ideas in a broad array of natural science fields, including chemistry, physics, astronomy, geometry, numbers, and big data. A number of chapters focus on how knowledge from the natural sciences can enhance social work practice in areas as diverse as medicine, substance abuse, mental health, and intellectual and developmental disabilities, while other chapters on water, human geography, climate change, execution and the death penalty, and the life cycle are designed to highlight the natural science behind social issues. The information presented in the book is complex enough to spark the reader's continued interest in knowing more about the natural sciences, but basic enough to allow readers with limited understanding of the natural sciences--at both the bachelor's and master's levels--to feel comfortable exploring its contents. |
| data science and social work: Data Science Jing He, Philip S. Yu, Yong Shi, Xingsen Li, Zhijun Xie, Guangyan Huang, Jie Cao, Fu Xiao, 2020-02-01 This book constitutes the refereed proceedings of the 6th International Conference on Data Science, ICDS 2019, held in Ningbo, China, during May 2019. The 64 revised full papers presented were carefully reviewed and selected from 210 submissions. The research papers cover the areas of Advancement of Data Science and Smart City Applications, Theory of Data Science, Data Science of People and Health, Web of Data, Data Science of Trust and Internet of Things. |
| data science and social work: Social Data Science Xennials Gian Marco Campagnolo, 2020-11-30 This book explores the tension between analogue and digital as part of an evolving research programme and focuses on the sequencing of methods within it. The book will be an invaluable reference for scholars who routinely engage in critical sociological analysis of the digital workplace and find it easier to treat the digital as an object of study. It describes how the transformations taking place in the 10-year arc of a career spent doing fieldwork in the IT sector led the author to progressively embrace new forms of data and methods. In a time where sociological imagination takes the shape of whatever new phenomenon can be studied by transactional data and machine learning methods, it is a reminder that longstanding engagement with a particular field of practice is the basis of empirical social science expertise. ‘This short book by Gian Marco Campagnolo is remarkably wide-ranging. It draws on theoretical perspectives as varied as Harold Garfinkel’s ethnomethodology and Andrew Abbott’s ‘linked ecologies’ to discuss topics as diverse as the adoption of packaged enterprise software in the public sector in Italy and the careers of often influential industry analysts. Campagnolo’s methods are primarily qualitative and ethnographic, but he shows a proper appreciation for quantitative methods such as text mining and sequence analysis. The book ends with a discussion of the famously difficult issue of achieving ‘explainability’ in machine learning. Campagnolo tantalisingly suggests the usefulness here of how ethnomethodologists view ‘accountability’: as a practical accomplishment that is hampered, rather than fostered, by efforts to give full explanations.’ —Donald MacKenzie, Professor of Sociology, Edinburgh University, Scotland ‘The author adopts a ‘processual’ perspective on social data science as means of exploring and reflecting on the emergence of an academic career within this new domain of interdisciplinary inquiry. This is certainly a novel and interesting approach given the fact that ‘data science’ is work in progress and is characterized by a number of competing occupational groups that are struggling to define this emerging field.’ —William Housley, Professor, University of Cardiff, UK ‘Having myself written about the relationships between ethnography and computer science, I see this book as a timely contribution in that it extends the existing debate to data science. Data science is an emerging discipline that is gaining central stage in industry and in the public discourse. The aim of this book to indicate the importance of interdisciplinarity in this field is commendable.’ —Giolo Fele, Professor, University of Trento, Italy 'This book provides two entwined accounts: a reflective personal journey across different projects and methods and a grounded, genealogically sound analysis of the approaches and contributions of social science to understanding the digital society. These dual accounts are adroitly communicated. Their bold combination yields a unique and invaluable contribution to fundamental discussions in the social sciences, as well as an exemplar for how to combine ethnographic and data-driven analysis in a theoretically and epistemologically informed manner. With this book, Campagnolo brings us close to the methods and opens up an inspiring and challenging agenda for combining old and new forms of inquiry into sociological problems.' —Anne Beaulieu, Director Data Research Centre, University of Groningen, Netherlands |
| data science and social work: Social Work in Health Settings Toba Schwaber Kerson, 1997 As the most comprehensive text of its kind, Social Work in Health Settings introduces social work students to a range of clients and provides an overview of many social work settings and services in the health arena. If you're a practitioner, you'll find the book useful for examining and evaluating your practice. This second edition features 18 new chapters and chapter subjects and rewritten and updated versions of the 14 chapters which were part of the first edition. |
| data science and social work: The Routledge International Handbook of Social Work and Disaster Practice Carole Adamson, Margaret Alston, Bindi Bennett, Jennifer Boddy, Heather Boetto, Louise Harms, Raewyn Tudor, 2024-12-24 This handbook addresses the diversity and complexity of social work practice in the context of disasters. Drawing on international perspectives, with the inclusion of case studies, this handbook provides a resource for students, practitioners, educators, and researchers seeking to prepare the social work profession for contemporary challenges associated with disasters. Divided into five parts, it explores the following subject areas: 1. Conceptual aspects concerning social work’s relationship with disasters 2. Social work’s role in preventing and preparing for disasters, and response and recovery 3. Social work practice with specific populations 4. Social work education and training in disasters 5. Implications for social work organisations and policy Leading ideas, debates, and approaches from international authors will provide Global North and South perspectives. A critical examination of research and theories for practice, including concepts of human vulnerability and community resilience, will provide the foundation for detailing the practical contributions that social workers can make at the micro, meso, and macro levels of practice. Social work’s underpinning value base of social justice and human rights will also be explored in the context of the complex and dynamic nature of socio-cultural, political, and economic dimensions pertaining to disasters. It will therefore be of interest to all social work students, academics, and professionals as well as those working in allied disciplines, policy, and emergency management roles. |
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 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 minimum time …
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, released in …
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 from …
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 barriers …
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 collected, …
