Disciplining Statistics contrasts the different ways that statistical knowledge was developed and used in England and France during the nineteenth century.

In Disciplining Statistics Libby Schweber compares the science of population statistics in England and France during the nineteenth century, demonstrating radical differences in the interpretation and use of statistical knowledge. Through a comparison of vital statistics and demography, Schweber describes how the English government embraced statistics, using probabilistic interpretations of statistical data to analyze issues related to poverty and public health. The French were far less enthusiastic. Political and scientific élites in France struggled with the “reality” of statistical populations, wrestling with concerns about the accuracy of figures that aggregated heterogeneous groups such as the rich and poor and rejecting probabilistic interpretations. Tracing the introduction and promotion of vital statistics and demography, Schweber identifies the institutional conditions that account for the contrasting styles of reasoning. She shows that the different reactions to statistics stemmed from different criteria for what counted as scientific knowledge. The French wanted certain knowledge, a one-to-one correspondence between observations and numbers. The English adopted an instrumental approach, using the numbers to influence public opinion and evaluate and justify legislation. Schweber recounts numerous attempts by vital statisticians and demographers to have their work recognized as legitimate scientific pursuits. While the British scientists had greater access to government policy makers, and were able to influence policy in a way that their French counterparts were not, ultimately neither the vital statisticians nor the demographers were able to institutionalize their endeavors. By 1885, both fields had been superseded by new forms of knowledge. Disciplining Statistics highlights how the development of “scientific” knowledge was shaped by interrelated epistemological, political, and institutional considerations.

This book provides collaborative research teams with a systematic approach for addressing complex real-world problems like widespread poverty, global climate change, organised crime, and escalating health care costs. The three core domains are Synthesising disciplinary and stakeholder knowledge,Understanding and managing diverse unknowns, andProviding integrated research support for policy and practice change. Each of these three domains is organised around five questions For what and for whom?Which knowledge, unknowns and aspects of policy or practice?How?Context?Outcome? This simple framework lays the foundations for developing compilations of concepts, methods and case studies about applying systems thinking, scoping and boundary setting, framing, dealing with values, harnessing and managing differences, undertaking dialogue, building models, applying common metrics, accepting unknowns, advocacy, end-user engagement, understanding authorisation, dealing with organisational facilitators and barriers, and much more. The book makes a case for a new research style—integrative applied research—and a new discipline of Integration and Implementation Sciences or I2S. It advocates for progressing these through an I2S Development Drive. It builds on theory and practice-based research in multi-, inter- and transdisciplinarity, post-normal science, systemic intervention, integrated assessment, sustainability science, team science, mode 2, action research and other approaches. The book concludes with 24 commentaries by Simon Bronitt; L. David Brown; Marcel Bursztyn and Maria Beatriz Maury; Lawrence Cram; Ian Elsum; Holly J. Falk-Krzesinski; Fasihuddin; Howard Gadlin and L. Michelle Bennett; Budi Haryanto; Julie Thompson Klein; Ted Lefroy; Catherine Lyall; M. Duane Nellis; Linda Neuhauser; Deborah O’Connell with Damien Farine, Michael O’Connor and Michael Dunlop; Michael O’Rourke; Christian Pohl; Merritt Polk; Alison Ritter; Alice Roughley; Michael Smithson; Daniel Walker; Michael Wesley; and Glenn Withers. These begin a process of appraisal, discussion and debate across diverse networks.

The need to manage, analyze, and extract knowledge from data is pervasive across industry, government, and academia. Scientists, engineers, and executives routinely encounter enormous volumes of data, and new techniques and tools are emerging to create knowledge out of these data, some of them capable of working with real-time streams of data. The nation's ability to make use of these data depends on the availability of an educated workforce with necessary expertise. With these new capabilities have come novel ethical challenges regarding the effectiveness and appropriateness of broad applications of data analyses. The field of data science has emerged to address the proliferation of data and the need to manage and understand it. Data science is a hybrid of multiple disciplines and skill sets, draws on diverse fields (including computer science, statistics, and mathematics), encompasses topics in ethics and privacy, and depends on specifics of the domains to which it is applied. Fueled by the explosion of data, jobs that involve data science have proliferated and an array of data science programs at the undergraduate and graduate levels have been established. Nevertheless, data science is still in its infancy, which suggests the importance of envisioning what the field might look like in the future and what key steps can be taken now to move data science education in that direction. This study will set forth a vision for the emerging discipline of data science at the undergraduate level. This interim report lays out some of the information and comments that the committee has gathered and heard during the first half of its study, offers perspectives on the current state of data science education, and poses some questions that may shape the way data science education evolves in the future. The study will conclude in early 2018 with a final report that lays out a vision for future data science education.

Pulling aside the curtain of ‘Big Data’ buzz, this book introduces C-suite and other non-technical senior leaders to the essentials of obtaining and maintaining accurate, reliable data, especially for decision-making purposes. Bad data begets bad decisions, and an understanding of data fundamentals — how data is generated, organized, stored, evaluated, and maintained — has never been more important when solving problems such as the pandemic-related supply chain crisis. This book addresses the data-related challenges that businesses face, answering questions such as: What are the characteristics of high-quality data? How do you get from bad data to good data? What procedures and practices ensure high-quality data? How do you know whether your data supports the decisions you need to make? This clear and valuable resource will appeal to C-suite executives and top-line managers across industries, as well as business analysts at all career stages and data analytics students.

The results of a survey on school violence, which was conducted with a national sample of 1,234 public elementary, middle, & secondary schools in all states in 1997. It requested information on 4 main topics: the incidence of crime & violence that occurred in public schools during the 1996-97 academic year; principals' perceptions about the seriousness of a variety of discipline issues in their schools; the types of disciplinary actions schools took against students for serious offenses; & the kind of security measures & violence prevention programs that were in place in public schools. Charts & tables.