Julia Lane – författare

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 sciencesPresents the key data science tools in a non-intimidating way to both social and data scientists while keeping the focus on research questions and purposesIllustrates social science and data science principles through real-world problemsLinks computer science concepts to practical social science researchPromotes good scientific practiceProvides freely available workbooks with data, code, and practical programming exercises, through Binder and GitHubNew to the Second Edition:Increased use of examples from different areas of social sciencesNew chapter on dealing with Bias and Fairness in Machine Learning modelsExpanded chapters focusing on Machine Learning and Text AnalysisRevamped hands-on Jupyter notebooks to reinforce concepts covered in each chapterThis 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.

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 sciencesPresents the key data science tools in a non-intimidating way to both social and data scientists while keeping the focus on research questions and purposesIllustrates social science and data science principles through real-world problemsLinks computer science concepts to practical social science researchPromotes good scientific practiceProvides freely available workbooks with data, code, and practical programming exercises, through Binder and GitHubNew to the Second Edition:Increased use of examples from different areas of social sciencesNew chapter on dealing with Bias and Fairness in Machine Learning modelsExpanded chapters focusing on Machine Learning and Text AnalysisRevamped hands-on Jupyter notebooks to reinforce concepts covered in each chapterThis 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.

Massive amounts of data on human beings can now be analyzed. Pragmatic purposes abound, including selling goods and services, winning political campaigns, and identifying possible terrorists. Yet 'big data' can also be harnessed to serve the public good: scientists can use big data to do research that improves the lives of human beings, improves government services, and reduces taxpayer costs. In order to achieve this goal, researchers must have access to this data - raising important privacy questions. What are the ethical and legal requirements? What are the rules of engagement? What are the best ways to provide access while also protecting confidentiality? Are there reasonable mechanisms to compensate citizens for privacy loss? The goal of this book is to answer some of these questions. The book's authors paint an intellectual landscape that includes legal, economic, and statistical frameworks. The authors also identify new practical approaches that simultaneously maximize the utility of data access while minimizing information risk.

Massive amounts of data on human beings can now be analyzed. Pragmatic purposes abound, including selling goods and services, winning political campaigns, and identifying possible terrorists. Yet 'big data' can also be harnessed to serve the public good: scientists can use big data to do research that improves the lives of human beings, improves government services, and reduces taxpayer costs. In order to achieve this goal, researchers must have access to this data - raising important privacy questions. What are the ethical and legal requirements? What are the rules of engagement? What are the best ways to provide access while also protecting confidentiality? Are there reasonable mechanisms to compensate citizens for privacy loss? The goal of this book is to answer some of these questions. The book's authors paint an intellectual landscape that includes legal, economic, and statistical frameworks. The authors also identify new practical approaches that simultaneously maximize the utility of data access while minimizing information risk.

This cross-disciplinary collection considers the intersection of affect and mothering, with the aim of expanding both the experiential and theoretical frameworks that guide our understanding of mothering and of theories of affect. It brings together creative, reflective and theoretical pieces to question and re-conceptualize motherhood through