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| data science public policy: Public Policy Analytics Ken Steif, 2021-08-18 Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand ‘spatial process’ and develop spatial analytics; how to develop ‘useful’ predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and ‘Planning’ are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government. |
| data science public policy: Data Science for Public Policy Jeffrey C. Chen, Edward A. Rubin, Gary J. Cornwall, 2021-09-01 This textbook presents the essential tools and core concepts of data science to public officials, policy analysts, and economists among others in order to further their application in the public sector. An expansion of the quantitative economics frameworks presented in policy and business schools, this book emphasizes the process of asking relevant questions to inform public policy. Its techniques and approaches emphasize data-driven practices, beginning with the basic programming paradigms that occupy the majority of an analyst’s time and advancing to the practical applications of statistical learning and machine learning. The text considers two divergent, competing perspectives to support its applications, incorporating techniques from both causal inference and prediction. Additionally, the book includes open-sourced data as well as live code, written in R and presented in notebook form, which readers can use and modify to practice working with data. |
| data science public policy: 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 public policy: Data Analysis for Business, Economics, and Policy Gábor Békés, Gábor Kézdi, 2021-05-06 A comprehensive textbook on data analysis for business, applied economics and public policy that uses case studies with real-world data. |
| data science public policy: Data Science in the Public Interest Joshua D. Hawley, 2020 This book is about how new and underutilized types of big data sources can inform public policy decisions related to workforce development. Hawley describes how government is currently using data to inform decisions about the workforce at the state and local levels. He then moves beyond standardized performance metrics designed to serve federal agency requirements and discusses how government can improve data gathering and analysis to provide better, up-to-date information for government decision making-- |
| data science public policy: 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 public policy: Data Analysis for Politics and Policy Edward R. Tufte, 1974 Introduction to data analysis; Predictions and projections: some issues of research design; Two-variable linear regression; Multiple regression. |
| data science public policy: R for Political Data Science Francisco Urdinez, Andres Cruz, 2020-11-18 R for Political Data Science: A Practical Guide is a handbook for political scientists new to R who want to learn the most useful and common ways to interpret and analyze political data. It was written by political scientists, thinking about the many real-world problems faced in their work. The book has 16 chapters and is organized in three sections. The first, on the use of R, is for those users who are learning R or are migrating from another software. The second section, on econometric models, covers OLS, binary and survival models, panel data, and causal inference. The third section is a data science toolbox of some the most useful tools in the discipline: data imputation, fuzzy merge of large datasets, web mining, quantitative text analysis, network analysis, mapping, spatial cluster analysis, and principal component analysis. Key features: Each chapter has the most up-to-date and simple option available for each task, assuming minimal prerequisites and no previous experience in R Makes extensive use of the Tidyverse, the group of packages that has revolutionized the use of R Provides a step-by-step guide that you can replicate using your own data Includes exercises in every chapter for course use or self-study Focuses on practical-based approaches to statistical inference rather than mathematical formulae Supplemented by an R package, including all data As the title suggests, this book is highly applied in nature, and is designed as a toolbox for the reader. It can be used in methods and data science courses, at both the undergraduate and graduate levels. It will be equally useful for a university student pursuing a PhD, political consultants, or a public official, all of whom need to transform their datasets into substantive and easily interpretable conclusions. |
| data science public policy: Federal Data Science Feras A. Batarseh, Ruixin Yang, 2017-09-21 Federal Data Science serves as a guide for federal software engineers, government analysts, economists, researchers, data scientists, and engineering managers in deploying data analytics methods to governmental processes. Driven by open government (2009) and big data (2012) initiatives, federal agencies have a serious need to implement intelligent data management methods, share their data, and deploy advanced analytics to their processes. Using federal data for reactive decision making is not sufficient anymore, intelligent data systems allow for proactive activities that lead to benefits such as: improved citizen services, higher accountability, reduced delivery inefficiencies, lower costs, enhanced national insights, and better policy making. No other government-dedicated work has been found in literature that addresses this broad topic. This book provides multiple use-cases, describes federal data science benefits, and fills the gap in this critical and timely area. Written and reviewed by academics, industry experts, and federal analysts, the problems and challenges of developing data systems for government agencies is presented by actual developers, designers, and users of those systems, providing a unique and valuable real-world perspective. - Offers a range of data science models, engineering tools, and federal use-cases - Provides foundational observations into government data resources and requirements - Introduces experiences and examples of data openness from the US and other countries - A step-by-step guide for the conversion of government towards data-driven policy making - Focuses on presenting data models that work within the constraints of the US government - Presents the why, the what, and the how of injecting AI into federal culture and software systems |
| data science public policy: 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 public policy: Quantitative Social Science Kosuke Imai, Lori D. Bougher, 2021-03-16 Princeton University Press published Imai's textbook, Quantitative Social Science: An Introduction, an introduction to quantitative methods and data science for upper level undergrads and graduates in professional programs, in February 2017. What is distinct about the book is how it leads students through a series of applied examples of statistical methods, drawing on real examples from social science research. The original book was prepared with the statistical software R, which is freely available online and has gained in popularity in recent years. But many existing courses in statistics and data sciences, particularly in some subject areas like sociology and law, use STATA, another general purpose package that has been the market leader since the 1980s. We've had several requests for STATA versions of the text as many programs use it by default. This is a translation of the original text, keeping all the current pedagogical text but inserting the necessary code and outputs from STATA in their place-- |
| data science public policy: Research Handbook in Data Science and Law Vanessa Mak, Eric Tjong Tjin Tai, Anna Berlee, 2018-12-28 The use of data in society has seen an exponential growth in recent years. Data science, the field of research concerned with understanding and analyzing data, aims to find ways to operationalize data so that it can be beneficially used in society, for example in health applications, urban governance or smart household devices. The legal questions that accompany the rise of new, data-driven technologies however are underexplored. This book is the first volume that seeks to map the legal implications of the emergence of data science. It discusses the possibilities and limitations imposed by the current legal framework, considers whether regulation is needed to respond to problems raised by data science, and which ethical problems occur in relation to the use of data. It also considers the emergence of Data Science and Law as a new legal discipline. |
| data science public policy: Data Science for Economics and Finance Sergio Consoli, Diego Reforgiato Recupero, Michaela Saisana, 2021 This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications. |
| data science public policy: The University of Chicago Magazine , 1917 |
| data science public policy: 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 public policy: 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 public policy: Journal of Public Policy and Marketing Thomas C. Kinnear, 1984-05 |
| data science public policy: Policy Practice and Digital Science Marijn Janssen, Maria A. Wimmer, Ameneh Deljoo, 2015-06-03 The explosive growth in data, computational power, and social media creates new opportunities for innovating the processes and solutions of Information and communications technology (ICT) based policy-making and research. To take advantage of these developments in the digital world, new approaches, concepts, instruments and methods are needed to navigate the societal and computational complexity. This requires extensive interdisciplinary knowledge of public administration, policy analyses, information systems, complex systems and computer science. This book provides the foundation for this new interdisciplinary field, in which various traditional disciplines are blending. Both policy makers, executors and those in charge of policy implementations acknowledge that ICT is becoming more important and is changing the policy-making process, resulting in a next generation policy-making based on ICT support. Web 2.0 and even Web 3.0 point to the specific applications of social networks, semantically enriched and linked data, whereas policy-making has also to do with the use of the vast amount of data, predictions and forecasts, and improving the outcomes of policy-making, which is confronted with an increasing complexity and uncertainty of the outcomes. The field of policy-making is changing and driven by developments like open data, computational methods for processing data, opining mining, simulation and visualization of rich data sets, all combined with public engagement, social media and participatory tools. |
| data science public policy: Doing Data Science Cathy O'Neil, Rachel Schutt, 2013-10-09 Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course. |
| data science public policy: The Role of Public Policy in K-12 Science Education George E. DeBoer, 2011-01-01 The goal of this volume of Research in Science Education is to examine the relationship between science education policy and practice and the special role that science education researchers play in influencing policy. It has been suggested that the science education research community is isolated from the political process, pays little attention to policy matters, and has little influence on policy. But to influence policy, it is important to understand how policy is made and how it is implemented. This volume sheds light on the intersection between policy and practice through both theoretical discussions and practical examples. This book was written primarily about science education policy development in the context of the highly decentralized educational system of the United States. But, because policy development is fundamentally a social activity involving knowledge, values, and personal and community interests, there are similarities in how education policy gets enacted and implemented around the world. This volume is meant to be useful to science education researchers and to practitioners such as teachers and administrators because it provides information about which aspects of the science education enterprise are affected by state, local, and national policies. It also provides helpful information for researchers and practitioners who wonder how they might influence policy. In particular, it points out how the values of people who are affected by policy initiatives are critical to the implementation of those policies. |
| data science public policy: Open Access and the Public Domain in Digital Data and Information for Science National Research Council, Policy and Global Affairs, Board on International Scientific Organizations, U.S. National Committee for CODATA, 2004-06-14 This symposium, which was held on March 10-11, 2003, at UNESCO headquarters in Paris, brought together policy experts and managers from the government and academic sectors in both developed and developing countries to (1) describe the role, value, and limits that the public domain and open access to digital data and information have in the context of international research; (2) identify and analyze the various legal, economic, and technological pressures on the public domain in digital data and information, and their potential effects on international research; and (3) review the existing and proposed approaches for preserving and promoting the public domain and open access to scientific and technical data and information on a global basis, with particular attention to the needs of developing countries. |
| data science public policy: Breaking the Social Media Prism Chris Bail, 2022-09-27 A revealing look at how user behavior is powering deep social divisions online—and how we might yet defeat political tribalism on social media In an era of increasing social isolation, platforms like Facebook and Twitter are among the most important tools we have to understand each other. We use social media as a mirror to decipher our place in society but, as Chris Bail explains, it functions more like a prism that distorts our identities, empowers status-seeking extremists, and renders moderates all but invisible. Breaking the Social Media Prism challenges common myths about echo chambers, foreign misinformation campaigns, and radicalizing algorithms, revealing that the solution to political tribalism lies deep inside ourselves. Drawing on innovative online experiments and in-depth interviews with social media users from across the political spectrum, this book explains why stepping outside of our echo chambers can make us more polarized, not less. Bail takes you inside the minds of online extremists through vivid narratives that trace their lives on the platforms and off—detailing how they dominate public discourse at the expense of the moderate majority. Wherever you stand on the spectrum of user behavior and political opinion, he offers fresh solutions to counter political tribalism from the bottom up and the top down. He introduces new apps and bots to help readers avoid misperceptions and engage in better conversations with the other side. Finally, he explores what the virtual public square might look like if we could hit reset and redesign social media from scratch through a first-of-its-kind experiment on a new social media platform built for scientific research. Providing data-driven recommendations for strengthening our social media connections, Breaking the Social Media Prism shows how to combat online polarization without deleting our accounts. |
| data science public policy: Social Science Information and Public Policy Making , Robert Rich reports the results of the Continuous National Survey (CNS), an administrative experiment with a two-year lifespan, designed to facilitate the use of research data by public officials in federal agencies. |
| data science public policy: Handbook on Science and Public Policy Dagmar Simon, Stefan Kuhlmann, Julia Stamm, Weert Canzler, 2019 This Handbook assembles state-of-the-art insights into the co-evolutionary and precarious relations between science and public policy. Beyond this, it also offers a fresh outlook on emerging challenges for science (including technology and innovation) in changing societies, and related policy requirements, as well as the challenges for public policy in view of science-driven economic, societal, and cultural changes. In short, this book deals with science as a policy-triggered project as well as public policy as a science-driven venture. |
| data science public policy: Computational Social Science R. Michael Alvarez, 2016-03-07 Quantitative research in social science research is changing rapidly. Researchers have vast and complex arrays of data with which to work: we have incredible tools to sift through the data and recognize patterns in that data; there are now many sophisticated models that we can use to make sense of those patterns; and we have extremely powerful computational systems that help us accomplish these tasks quickly. This book focuses on some of the extraordinary work being conducted in computational social science - in academia, government, and the private sector - while highlighting current trends, challenges, and new directions. Thus, Computational Social Science showcases the innovative methodological tools being developed and applied by leading researchers in this new field. The book shows how academics and the private sector are using many of these tools to solve problems in social science and public policy. |
| data science public policy: Innovation and Public Policy Austan Goolsbee, Benjamin F. Jones, 2022-03-25 A calculation of the social returns to innovation /Benjamin F. Jones and Lawrence H. Summers --Innovation and human capital policy /John Van Reenen --Immigration policy levers for US innovation and start-ups /Sari Pekkala Kerr and William R. Kerr --Scientific grant funding /Pierre Azoulay and Danielle Li --Tax policy for innovation /Bronwyn H. Hall --Taxation and innovation: what do we know? /Ufuk Akcigit and Stefanie Stantcheva --Government incentives for entrepreneurship /Josh Lerner. |
| data science public policy: Geospatial Health Data Paula Moraga, 2019-11-26 Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. The book covers the following topics: Manipulate and transform point, areal, and raster data, Bayesian hierarchical models for disease mapping using areal and geostatistical data, Fit and interpret spatial and spatio-temporal models with the Integrated Nested Laplace Approximations (INLA) and the Stochastic Partial Differential Equation (SPDE) approaches, Create interactive and static visualizations such as disease maps and time plots, Reproducible R Markdown reports, interactive dashboards, and Shiny web applications that facilitate the communication of insights to collaborators and policy makers. The book features fully reproducible examples of several disease and environmental applications using real-world data such as malaria in The Gambia, cancer in Scotland and USA, and air pollution in Spain. Examples in the book focus on health applications, but the approaches covered are also applicable to other fields that use georeferenced data including epidemiology, ecology, demography or criminology. The book provides clear descriptions of the R code for data importing, manipulation, modeling and visualization, as well as the interpretation of the results. This ensures contents are fully reproducible and accessible for students, researchers and practitioners. |
| data science public policy: The Data Shake Grazia Concilio, Paola Pucci, Lieven Raes, Geert Mareels, 2021-03-05 This open access book represents one of the key milestones of PoliVisu, an H2020 research and innovation project funded by the European Commission under the call “Policy-development in the age of big data: data-driven policy-making, policy-modelling and policy-implementation”. It investigates the operative and organizational implications related to the use of the growing amount of available data on policy making processes, highlighting the experimental dimension of policy making that, thanks to data, proves to be more and more exploitable towards more effective and sustainable decisions. The first section of the book introduces the key questions highlighted by the PoliVisu project, which still represent operational and strategic challenges in the exploitation of data potentials in urban policy making. The second section explores how data and data visualisations can assume different roles in the different stages of a policy cycle and profoundly transform policy making. |
| data science public policy: The Science of Science Policy Julia I. Lane, Kaye Husbands Fealing, John H. Marburger, III, Stephanie S. Shipp, 2011-03-18 Basic scientific research and technological development have had an enormous impact on innovation, economic growth, and social well-being. Yet science policy debates have long been dominated by advocates for particular scientific fields or missions. In the absence of a deeper understanding of the changing framework in which innovation occurs, policymakers cannot predict how best to make and manage investments to exploit our most promising and important opportunities. Since 2005, a science of science policy has developed rapidly in response to policymakers' increased demands for better tools and the social sciences' capacity to provide them. The Science of Science Policy: A Handbook brings together some of the best and brightest minds working in science policy to explore the foundations of an evidence-based platform for the field. The contributions in this book provide an overview of the current state of the science of science policy from three angles: theoretical, empirical, and policy in practice. They offer perspectives from the broader social science, behavioral science, and policy communities on the fascinating challenges and prospects in this evolving arena. Drawing on domestic and international experiences, the text delivers insights about the critical questions that create a demand for a science of science policy. |
| data science public policy: Handbook on Using Administrative Data for Research and Evidence-based Policy Shawn Cole, Iqbal Dhaliwal, Anja Sautmann, 2021 This Handbook intends to inform Data Providers and researchers on how to provide privacy-protected access to, handle, and analyze administrative data, and to link them with existing resources, such as a database of data use agreements (DUA) and templates. Available publicly, the Handbook will provide guidance on data access requirements and procedures, data privacy, data security, property rights, regulations for public data use, data architecture, data use and storage, cost structure and recovery, ethics and privacy-protection, making data accessible for research, and dissemination for restricted access use. The knowledge base will serve as a resource for all researchers looking to work with administrative data and for Data Providers looking to make such data available. |
| data science public policy: Big Data in Education Ben Williamson, 2017-07-24 Big data has the power to transform education and educational research. Governments, researchers and commercial companies are only beginning to understand the potential that big data offers in informing policy ideas, contributing to the development of new educational tools and innovative ways of conducting research. This cutting-edge overview explores the current state-of-play, looking at big data and the related topic of computer code to examine the implications for education and schooling for today and the near future. Key topics include: · The role of learning analytics and educational data science in schools · A critical appreciation of code, algorithms and infrastructures · The rise of ‘cognitive classrooms’, and the practical application of computational algorithms to learning environments · Important digital research methods issues for researchers This is essential reading for anyone studying or working in today′s education environment! |
| data science public policy: 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 public policy: The Data Revolution Rob Kitchin, 2014-09-16 Carefully distinguishing between big data and open data, and exploring various data infrastructures, Kitchin vividly illustrates how the data landscape is rapidly changing and calls for a revolution in how we think about data. - Evelyn Ruppert, Goldsmiths, University of London Deconstructs the hype around the ‘data revolution’ to carefully guide us through the histories and the futures of ‘big data.’ The book skilfully engages with debates from across the humanities, social sciences, and sciences in order to produce a critical account of how data are enmeshed into enormous social, economic, and political changes that are taking place. - Mark Graham, University of Oxford Traditionally, data has been a scarce commodity which, given its value, has been either jealously guarded or expensively traded. In recent years, technological developments and political lobbying have turned this position on its head. Data now flow as a deep and wide torrent, are low in cost and supported by robust infrastructures, and are increasingly open and accessible. A data revolution is underway, one that is already reshaping how knowledge is produced, business conducted, and governance enacted, as well as raising many questions concerning surveillance, privacy, security, profiling, social sorting, and intellectual property rights. In contrast to the hype and hubris of much media and business coverage, The Data Revolution provides a synoptic and critical analysis of the emerging data landscape. Accessible in style, the book provides: A synoptic overview of big data, open data and data infrastructures An introduction to thinking conceptually about data, data infrastructures, data analytics and data markets Acritical discussion of the technical shortcomings and the social, political and ethical consequences of the data revolution An analysis of the implications of the data revolution to academic, business and government practices |
| data science public policy: New Horizons for a Data-Driven Economy José María Cavanillas, Edward Curry, Wolfgang Wahlster, 2016-04-04 In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe. This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment. |
| data science public policy: Systematic Thinking for Social Action Alice M. Rivlin, 1971-07-01 How can we identify who benefits from government programs aimed at solving our social problem and who pays for them? With so many problems, how can we allocate scarce funds to promote the maximum well-being of our citizens? In this book, originally presented as the third series of H. Rowan Gaither Lectures in Systems Science at the University of California (Berkeley). Alice M. Rivlin examines the contributions that systematic analysis has made to decisionmaking in the government's social action programs—education, health, manpower training, and income maintenance. Drawing on her own experience in government, Mrs. Rivlin indicates where the analysts have been helpful in finding solutions and where—because of inadequate data or methods—they have been no help at all. Mrs. Rivlin concludes by urging the widespread implementation of social experimentation and acceptability by the federal government. The first in such a way as to permit valid conclusions about their effectiveness; the second would encourage the adoption of better ways of delivering services by making those who administer programs responsive to their clients. Underlying both is the requirement from comprehensive, reliable performance measures. |
| data science public policy: Ensuring the Integrity, Accessibility, and Stewardship of Research Data in the Digital Age Institute of Medicine, National Academy of Engineering, National Academy of Sciences, Committee on Science, Engineering, and Public Policy, Committee on Ensuring the Utility and Integrity of Research Data in a Digital Age, 2009-11-17 As digital technologies are expanding the power and reach of research, they are also raising complex issues. These include complications in ensuring the validity of research data; standards that do not keep pace with the high rate of innovation; restrictions on data sharing that reduce the ability of researchers to verify results and build on previous research; and huge increases in the amount of data being generated, creating severe challenges in preserving that data for long-term use. Ensuring the Integrity, Accessibility, and Stewardship of Research Data in the Digital Age examines the consequences of the changes affecting research data with respect to three issues - integrity, accessibility, and stewardship-and finds a need for a new approach to the design and the management of research projects. The report recommends that all researchers receive appropriate training in the management of research data, and calls on researchers to make all research data, methods, and other information underlying results publicly accessible in a timely manner. The book also sees the stewardship of research data as a critical long-term task for the research enterprise and its stakeholders. Individual researchers, research institutions, research sponsors, professional societies, and journals involved in scientific, engineering, and medical research will find this book an essential guide to the principles affecting research data in the digital age. |
| data science public policy: Kennedy and Roosevelt Michael Beschloss, 2016-08-16 The revealing story of Franklin Roosevelt, Joe Kennedy, and a political alliance that changed history, from a New York Times–bestselling author. When Franklin Roosevelt ran for president in 1932, he gained the support of Joseph Kennedy, a little-known businessman with Wall Street connections. Instrumental in Roosevelt’s victory, their partnership began a longstanding alliance between two of America’s most ambitious power brokers. Kennedy worked closely with FDR as the first chairman of the Securities and Exchange Commission, and later as ambassador to Great Britain. But at the outbreak of World War II, sensing a threat to his family and fortune, Kennedy lobbied against American intervention—putting him in direct conflict with Roosevelt’s intentions. Though he retreated from the spotlight to focus on the political careers of his sons, Kennedy’s relationship with Roosevelt would eventually come full circle in 1960, when Franklin Roosevelt Jr. campaigned for John F. Kennedy’s presidential win. With unprecedented access to Kennedy’s private diaries as well as firsthand interviews with Roosevelt’s family and White House aides, New York Times–bestselling author Michael Beschloss—called “the nation’s leading presidential historian” by Newsweek—presents an insightful study in contrasts. Roosevelt, the scion of a political dynasty, had a genius for the machinery of government; Kennedy, who built his own fortune, was a political outsider determined to build a dynasty of his own. From the author of The Conquerors and Presidential Courage, this is a “fascinating account of the complex, ambiguous relationship of two shrewd, ruthless, power-hungry men” (The New York Times Book Review). |
| data science public policy: Economy, Society and Public Policy The Core Team, 2019 Economy, Society, and Public Policy is a new way to learn economics. It is designed specifically for students studying social sciences, public policy, business studies, engineering and other disciplines who want to understand how the economy works and how it can be made to work better. Topical policy problems are used to motivate learning of key concepts and methods of economics. It engages, challenges and empowers students, and will provide them with the tools to articulate reasoned views on pressing policy problems. This project is the result of a worldwide collaboration between researchers, educators, and students who are committed to bringing the socially relevant insights of economics to a broader audience.KEY FEATURESESPP does not teach microeconomics as a body of knowledge separate from macroeconomicsStudents begin their study of economics by understanding that the economy is situated within society and the biosphereStudents study problems of identifying causation, not just correlation, through the use of natural experiments, lab experiments, and other quantitative methodsSocial interactions, modelled using simple game theory, and incomplete information, modelled using a series of principal-agent problems, are introduced from the beginning. As a result, phenomena studied by the other social sciences such as social norms and the exercise of power play a roleThe insights of diverse schools of thought, from Marx and the classical economists to Hayek and Schumpeter, play an integral part in the bookThe way economists think about public policy is central to ESPP. This is introduced in Units 2 and 3, rather than later in the course. |
| data science public policy: The Path to Becoming a Data-Driven Public Sector Oecd, Organisation for Economic Co-operation and Development, 2019-11-28 Twenty-first century governments must keep pace with the expectations of their citizens and deliver on the promise of the digital age. Data-driven approaches are particularly effective for meeting those expectations and rethinking the way governments and citizens interact. This report highlights the important role data can play in creating conditions that improve public services, increase the effectiveness of public spending and inform ethical and privacy considerations. It presents a data-driven public sector framework that can help countries or organisations assess the elements needed for using data to make better-informed decisions across public sectors. |
| data science public policy: Text as Data Justin Grimmer, Margaret E. Roberts, Brandon M. Stewart, 2022-03-29 A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry |
Data Science for Public Policy - interface-eu.org
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How do we scope projects that are actionable and result in (positive) social impact? inform? Data: What data do you have internally? What data do you need? What can you augment from …
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This article focuses on the implications for public administration of the latest-generation of data-driven technologies (data science and artificial intelligence, or DSAI) by developing and …
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public administrations with a knowledge base and guidance on the adoption of public sector data strategies, policy modelling and simulation tools and methodologies, and data technologies …
DATA SCIENCE + PUBLIC POLICY - Purdue University
Students will learn about the ethical, legal, and social implications of data science and big data, including issues such as privacy, informed consent, security, safety, liability, bias, and …
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In this report, we seek to show the potential that this has begun to unlock. The range of techniques that make up data science – new tools for analysing data, new datasets, and novel …
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Data Science for Public Policy - AVPN
§ Routine data collection, monitoring and synthesis related to Gram Panchayat, Assembly and Parliamentary Constituencies are necessary for effective governance and accountability. § …
Synthetic Data and Public Policy - Victoria University of …
Synthetic data is an emerging area of data science that can potentially support policy decision making through enabling research to work faster and with fewer errors while also ensuring …
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Combining datasets and performing large aggregate analyses are a powerful new way to improve service across large populations. Critically important in this task is the deduplication of …
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In this article, I argue that data science must embrace a political orientation. Data scientists must recognize themselves as political actors engaged in normative constructions of society and …
Machine Learning in Public Policy - RAND Corporation
ML can also advance public policy analysis by leverag-ing new data from unconventional sources (e.g., social media, images, and audio) and use nonlinear modeling to uncover previously …
Data Science for Social Good & Public Policy - redgealc.org
Matching between peoples and public social services (SEDESOL, Mexico) 7.5 million aditional people can potentially be better matched with services they need
Federal Data Strategy Data Ethics Framework
working with data in the public sector should have a foundational understanding of the Data Ethics Tenets. Federal leaders should also foster a data ethics-driven culture and lead by example.
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analyze public policies and economic behavior using the wealth of newly avail-able data sources being generated by business and government. Students will learn to conduct experiments and …
Government Analytics Using Machine Learning - World Bank
With the transition to data-driven policy making, machine-learning applications are a natural next step in government analytics, automatically leveraging data to improve the performance of …
Data Science Project Scoping Worksheet - Data Science and …
Data Science Project Scoping Worksheet Updated: October 1, 2021 This worksheet is designed for social good organizations (government agencies, nonprofits, social enterprises, and others) …
Data Analytics & Quantitative Analysis - Columbia University
The specialization in Data Analytics and Quantitative Analysis (DAQA) provides opportunities to pursue advanced work in computational and data analytics, econometrics and quantitative …
DATA ANALYTICS AND POLICY, MASTER OF SCIENCE
Students may choose to earn a concentration within one of the following specialized areas: political behavior and policy analysis, geospatial analysis, statistical analysis, or public …
Data Science Project Scoping Worksheet - Data Science and …
Data Science Project Scoping Worksheet This worksheet is designed for social good organizations (government agencies, non-profits, social enterprises, and others) to scope …
Data Science Project Scoping Worksheet
Data Science Project Scoping Worksheet Updated: Januar y, 2025 This worksheet is designed for social good organizations ( government agencies, nonprofits, social enterprises, and others) to …
scoping_jan_2020 - Data Science and Public Policy
Data (Science) can help build systems to improve policy and social outcomes in an efficient, effective, and equitable manner To get started, government agencies need to:
Data Science Project Scoping Worksheet - Data Science and …
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@datascifellows| #scopeathon - datasciencepublicpolicy.org
- Improve education quality in Pittsburgh Public Schools - Teach students how to scope data science projects - Minimize wait time for patients visiting ER - Minimize change in average …
Data Maturity Framework - Scorecard Questionnaire - Data …
Are there policies in place around who can use data, how they can use data, which parts can they use, and for what purposes? Intervenor Buy In Do the people who will act on the results buy in?
Data Science Project Scoping Worksheet - Data Science and …
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Improving deduplication of identities - Data Science and …
Key improvements include additional data-specific comparison metrics for dates of birth and identification numbers with known patterns (like So- cialSecurityNumbers).
