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oapen-20.500.12657-577092022-08-03T03:08:14Z Chapter 19 Unsupervised Methods Bacher, Johann Pöge, Andreas Wenzig, Knut survey data, data analysis, data science, information technology, AI, socio-robotics, quantitative, survey methodology, ethics, ethical standards, privacy, replication, politics, survey design, social media, big data, social, human-robot interaction, machine learning, open data, data archives, data ownership, digital trace, unstructured data bic Book Industry Communication::U Computing & information technology::UY Computer science bic Book Industry Communication::J Society & social sciences::JM Psychology bic Book Industry Communication::J Society & social sciences::JM Psychology::JMB Psychological methodology The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital trace and textual data, as well as probability, non-probability, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors. 2022-08-02T09:57:12Z 2022-08-02T09:57:12Z 2022 chapter 9780367457808 9781032077703 https://library.oapen.org/handle/20.500.12657/57709 eng application/pdf Attribution-NonCommercial-NoDerivatives 4.0 International 9781003025245_10.4324_9781003025245-23.pdf Taylor & Francis Handbook of Computational Social Science, Volume 2 Routledge 10.4324/9781003025245-23 10.4324/9781003025245-23 7b3c7b10-5b1e-40b3-860e-c6dd5197f0bb 866251e4-af21-49fd-a795-9950f3c15530 9780367457808 9781032077703 Routledge 19 open access
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The Handbook of Computational Social Science is a comprehensive reference source for scholars across multiple disciplines. It outlines key debates in the field, showcasing novel statistical modeling and machine learning methods, and draws from specific case studies to demonstrate the opportunities and challenges in CSS approaches. The Handbook is divided into two volumes written by outstanding, internationally renowned scholars in the field. This second volume focuses on foundations and advances in data science, statistical modeling, and machine learning. It covers a range of key issues, including the management of big data in terms of record linkage, streaming, and missing data. Machine learning, agent-based and statistical modeling, as well as data quality in relation to digital trace and textual data, as well as probability, non-probability, and crowdsourced samples represent further foci. The volume not only makes major contributions to the consolidation of this growing research field, but also encourages growth into new directions. With its broad coverage of perspectives (theoretical, methodological, computational), international scope, and interdisciplinary approach, this important resource is integral reading for advanced undergraduates, postgraduates, and researchers engaging with computational methods across the social sciences, as well as those within the scientific and engineering sectors.
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