Finally, it considers the future impact of data science and offers principles for success in data science projects.Sage Research Methods Online (SRMO) is an online search tool that provides information and instruction on research methods in the health and social sciences. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It is closely related to the fields of data mining and machine learning, but broader in scope. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. It has never been easier for organizations to gather, store, and process data. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. The goal of data science is to improve decision making through the analysis of data. These include examples of studies with marginalized individuals in our society that reflect issues in social justice in addition to the traditional samples and populations studied by social researchers.Ī concise introduction to the emerging field of data science, explaining its evolution, relation to machine learning, current uses, data infrastructure issues, and ethical challenges. Mixed Method Procedures (Chapter 11) show readers how to identify the type of mixed method strategy, select the data collection and analysis approaches, and plan the overall structure of the study Examples, drawn from various disciplinary fields, are used throughout the book to deepen the readers understanding of the discussion.The latest developments in qualitative inquiry, including advocacy, participatory, and emancipatory approaches have been added to Chapter 10.Writing tips and considerations have been expanded and moved to the first part of the book to get your research plan started in the right direction.Ethical issues that may arise in quantitative, qualitative and mixed methods designs have been added to a new section in Chapter 3.Because mixed method research has come into its own since the publication of the first edition, every chapter now shows how to implement a mixed method design in your proposal or plan as well as showing how to do the other two (qualitative and quantitative) approaches
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