This comprehensive guide provides a step-by-step approach to data collection, cleaning, formatting, and storage, using Python and R.

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.

Teaches creative, effective visualization techniques for tables, time series, maps, text, and networks for beginners in R and Python.

First published in 1986, Social Science and Social Policy addresses major questions concerned with the social utility of social science. The book is divided into four parts. The first part considers the place of social science in the policy-making process and criticizes the rational model which gives a central place to analysis. In part two, five different methodologies for policy research are considered: the use of continuous surveys, public opinion polls, social indicators, evaluation research and social experimentations and the use of qualitative methods. The advantages and drawbacks of each are considered with extensive use of examples. In the third part, the role of theory is examined. Particular attention is paid to the issue of health inequality. In part four, general questions are raised about the use and abuse of social science, including questions about how it can be most effectively disseminated to make maximum impact. The book is aimed at a general readership and requires no special methodological expertise. It will appeal particularly to undergraduates and graduate students taking courses in social policy, public policy applied sociology and a range of applied social sciences such as criminology, health studies, education and social work.

The disintegration and questioning of global governance structures and a re-orientation toward national politics combined with the spread of technological innovations such as big data, social media, and phenomena like fake news, populism, or questions of global health policies make it necessary for the introduction of new methods of inquiry and the adaptation of established methods in Foreign Policy Analysis (FPA). This accessible handbook offers concise chapters from expert international contributors covering a diverse range of new and established FPA methods. Embracing methodological pluralism and a belief in the value of an open discussion about methods’ assumptions and diverging positions, it provides new, state-of-the-art research approaches, as well as introductions to a range of established methods. Each chapter follows the same approach, introducing the method and its development, discussing strengths, requirements, limitations, and potential pitfalls while illustrating the method’s application using examples from empirical research. Embracing methodological pluralism and problem-oriented research that engages with real-world questions, the authors examine quantitative and qualitative traditions, rationalist and interpretivist perspectives, as well as different substantive backgrounds. The book will be of interest to a wide range of scholars and students in global politics, foreign policy, and methods-related classes across the social sciences.

"This entirely updated and enlarged Second Edition of a landmark reference/text continues to provide comprehensive coverage of every important aspect of policy studies--discussing concepts, methods, utilization, formation, and implementation both internationally and across each level of government."

The Office of the Under Secretary of Defense (Personnel & Readiness), referred to throughout this report as P&R, is responsible for the total force management of all Department of Defense (DoD) components including the recruitment, readiness, and retention of personnel. Its work and policies are supported by a number of organizations both within DoD, including the Defense Manpower Data Center (DMDC), and externally, including the federally funded research and development centers (FFRDCs) that work for DoD. P&R must be able to answer questions for the Secretary of Defense such as how to recruit people with an aptitude for and interest in various specialties and along particular career tracks and how to assess on an ongoing basis service members' career satisfaction and their ability to meet new challenges. P&R must also address larger-scale questions, such as how the current realignment of forces to the Asia-Pacific area and other regions will affect recruitment, readiness, and retention. While DoD makes use of large-scale data and mathematical analysis in intelligence, surveillance, reconnaissance, and elsewhereâ€"exploiting techniques such as complex network analysis, machine learning, streaming social media analysis, and anomaly detectionâ€"these skills and capabilities have not been applied as well to the personnel and readiness enterprise. Strengthening Data Science Methods for Department of Defense Personnel and Readiness Missions offers and roadmap and implementation plan for the integration of data analysis in support of decisions within the purview of P&R.

This book examines how Indigenous Peoples around the world are demanding greater data sovereignty, and challenging the ways in which governments have historically used Indigenous data to develop policies and programs. In the digital age, governments are increasingly dependent on data and data analytics to inform their policies and decision-making. However, Indigenous Peoples have often been the unwilling targets of policy interventions and have had little say over the collection, use and application of data about them, their lands and cultures. At the heart of Indigenous Peoples’ demands for change are the enduring aspirations of self-determination over their institutions, resources, knowledge and information systems. With contributors from Australia, Aotearoa New Zealand, North and South America and Europe, this book offers a rich account of the potential for Indigenous data sovereignty to support human flourishing and to protect against the ever-growing threats of data-related risks and harms. The Open Access version of this book, available at https://www.taylorfrancis.com/books/e/9780429273957, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license

This volume presents techniques and theories drawn from mathematics, statistics, computer science, and information science to analyze problems in business, economics, finance, insurance, and related fields. The authors present proposals for solutions to common problems in related fields. To this end, they are showing the use of mathematical, statistical, and actuarial modeling, and concepts from data science to construct and apply appropriate models with real-life data, and employ the design and implementation of computer algorithms to evaluate decision-making processes. This book is unique as it associates data science - data-scientists coming from different backgrounds - with some basic and advanced concepts and tools used in econometrics, operational research, and actuarial sciences. It, therefore, is a must-read for scholars, students, and practitioners interested in a better understanding of the techniques and theories of these fields.