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.

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.

Mathematicians have skills that, if deepened in the right ways, would enable them to use data to answer questions important to them and others, and report those answers in compelling ways. Data science combines parts of mathematics, statistics, computer science. Gaining such power and the ability to teach has reinvigorated the careers of mathematicians. This handbook will assist mathematicians to better understand the opportunities presented by data science. As it applies to the curriculum, research, and career opportunities, data science is a fast-growing field. Contributors from both academics and industry present their views on these opportunities and how to advantage them.

This collection focuses on the role of higher education institutions concerning datafication as a complex phenomenon. It explores how the universities can develop data literac(ies) shaping tomorrow skills and “formae mentis” to face the most deleterious effects of datafication, but also to engage in creative and constructive ways with data. Notably, the book spots data practices within the two most relevant sides of academics’ professional practice, namely, research and teaching. Hence, the collection seeks to reflect on faculty’s professional learning about data infrastructures and practices. The book draws on a range of studies covering the higher education response to the several facets of data in society, from data surveillance and the algorithmic control of human behaviour to empowerment through the use of open data. The research reported ranges from literature overviews to multi-case and in-depth case studies illustrating institutional and educational responses to different problems connected to data. The ultimate intention is to provide conceptual bases and practical examples relating to universities’ faculty development policies to overcome data practices and discourses' fragmentation and contradictions: in a nutshell, to build “fair data cultures” in higher education.

On January 30-31, 2019 the Board on Mathematical Sciences and Analytics, in collaboration with the Board on Energy and Environmental Systems and the Computer Science and Telecommunications Board, convened a workshop in Washington, D.C. to explore the frontiers of mathematics and data science needs for sustainable urban communities. The workshop strengthened the emerging interdisciplinary network of practitioners, business leaders, government officials, nonprofit stakeholders, academics, and policy makers using data, modeling, and simulation for urban and community sustainability, and addressed common challenges that the community faces. Presentations highlighted urban sustainability research efforts and programs under way, including research into air quality, water management, waste disposal, and social equity and discussed promising urban sustainability research questions that improved use of big data, modeling, and simulation can help address. This publication summarizes the presentation and discussion of the workshop.

This volume brings together experts on various aspects of education to address many of the emerging issues and problems that affect how data are being used or misused in educational contexts. Readers will learn about the importance of using data effectively, responsibly, and ethically to fully understand how cognitive fallacies occur and how they impact decisionmaking. They will understand how codes of ethics deal with the use of data within education as well as in other disciplines. Chapters provide a landscape view of the regulations that pertain to data use and policies that have emerged, including the impact of accountability on data use and data ethics. The text covers data ethics in local education agencies, professional development, educator preparation, testing programs, and educational technology. Chapter authors recommend steps to improve awareness among educators, stakeholders, and other interested groups and suggest actions that can be taken to enhance educators’ capacity to use data responsibly. A final use case chapter describes the importance of data ethics in terms of equity in schools and includes salient examples of ethical dilemmas, with questions and reflections on how ethics and equity apply to each situation. The conclusion addresses data ethics in terms of professionalism and poses several recommendations to challenge educators in ways to raise awareness of and integrate data ethics into educational practice. Book Features: Discusses how accountability affects effective data, including the pressure on schools and districts to perform better on test scores or other indicators. Outlines ten recommendations for how professional development can incorporate data ethics in practice.Reviews the expectations and realities of preparing educators for data literacy, including an example of one teacher education program’s integrated, curriculum-wide approach. Considers the role of testing companies in ethical data use, including issues around equity in assessment data.Explores how educational technologies, platforms, and applications impact data use. Contributors: Wayne Camara, Michelle Croft, Amanda Datnow, Chris Dede, Edward Dieterle, Sherman Dorn, Paul Gibbs, Edith S. Gummer, Beth Holland, Taryn A. Hochleitner, Jo Beth Jimerson, Marie Lockton, Ellen B. Mandinach, Sharon L. Nichols, Diana Nunnaley, Brennan McMahon Parton, Amelia Vance, Alina von Davier, Casey Waughn, Haley Weddle

Community Data offers a new toolkit for data storytelling in community settings, one purpose-built for goals like inclusion, empowerment, and impact. Data science and visualization has spread into new domains it was designed for - community organizing, education, journalism, civic governance, and more. The dominant computational methods and processes, which have not changed in response, are causing significant discriminatory and harmful impacts, documented by leading scholars across a variety of populations. Informed by 15 years of collaborations in academic and professional settings with nonprofits and marginalized populations, the book articulates a new approach for aligning the processes and media of data work with social good outcomes, learning from the practices of newspapers, museums, community groups, artists, and libraries. This book introduces a community-driven framework as a response to the urgent need to realign data theories and methods around justice and empowerment to avoid further replicating harmful power dynamics and ensure everyone has a seat at the table in data-centered community processes. It offers a broader toolbox for working with data and presenting it, pushing beyond the limited vocabulary of surveys, spreadsheets, charts and graphs.

This edited volume surveys a variety of topics in statistics and the social sciences in memory of the late Stephen Fienberg. The book collects submissions from a wide range of contemporary authors to explore the fields in which Fienberg made significant contributions, including contingency tables and log-linear models, privacy and confidentiality, forensics and the law, the decennial census and other surveys, the National Academies, Bayesian theory and methods, causal inference and causes of effects, mixed membership models, and computing and machine learning. Each section begins with an overview of Fienberg’s contributions and continues with chapters by Fienberg’s students, colleagues, and collaborators exploring recent advances and the current state of research on the topic. In addition, this volume includes a biographical introduction as well as a memorial concluding chapter comprised of entries from Stephen and Joyce Fienberg’s close friends, former students, colleagues, and other loved ones, as well as a photographic tribute.

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.