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Titlebook: Data Science and Social Research; Epistemology, Method N. Carlo Lauro,Enrica Amaturo,Marina Marino Conference proceedings 2017 Springer Int

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書目名稱Data Science and Social Research
副標(biāo)題Epistemology, Method
編輯N. Carlo Lauro,Enrica Amaturo,Marina Marino
視頻videohttp://file.papertrans.cn/264/263111/263111.mp4
概述Applies methods and techniques of data science to the social sciences.Provides extensive examples of new (big) data use in the social sciences.Discusses epistemological consequences of new data on soc
叢書名稱Studies in Classification, Data Analysis, and Knowledge Organization
圖書封面Titlebook: Data Science and Social Research; Epistemology, Method N. Carlo Lauro,Enrica Amaturo,Marina Marino Conference proceedings 2017 Springer Int
描述.This edited volume lays the groundwork for Social Data Science, addressing epistemological issues, methods, technologies, software and applications of data science in the social sciences. It presents data science techniques for the collection, analysis and use of both online and offline new (big) data in social research and related applications. Among others, the individual contributions cover topics like social media, learning analytics, clustering, statistical literacy, recurrence analysis and network analysis. .Data science is a multidisciplinary approach based mainly on the methods of statistics and computer science, and its aim is to develop appropriate methodologies for forecasting and decision-making in response to an increasingly complex reality often characterized by large amounts of data (big data) of various types (numeric, ordinal and nominal variables, symbolic data, texts, images, data streams, multi-way data, social networks etc.) and from diverse sources..This book presents selected papers from the international conference on Data Science & Social Research, held in Naples, Italy in February 2016, and will appeal to researchers in the social sciences working in acad
出版日期Conference proceedings 2017
關(guān)鍵詞data analysis in social sciences; data science and social sciences; data science; new data; textual anal
版次1
doihttps://doi.org/10.1007/978-3-319-55477-8
isbn_softcover978-3-319-55476-1
isbn_ebook978-3-319-55477-8Series ISSN 1431-8814 Series E-ISSN 2198-3321
issn_series 1431-8814
copyrightSpringer International Publishing AG 2017
The information of publication is updating

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Springer Tracts in Mechanical Engineeringth the aim of identifying the best partition of the objects, described by the best orthogonal linear combinations of the factors, according to the least-squares criterion. This new methodology named multiple correspondence .-means is a useful alternative to the Tandem Analysis in the case of categor
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