90折
Handbook of Computational Social Science, Volume 2：Data Science, Statistical Modelling, and Machine Learning Methods
- 系列名：European Association of Methodology Series
- ISBN13：9780367457808
- 出版社：PBKTYFRL
- 作者：Engel; Uwe (University of Bremen; Germany); Quan-Haase; Anabel; Liu; Sunny Xun; E Lyberg; Lars
- 裝訂／頁數：精裝／432頁
- 規格：17.4cm*24.6cm (高/寬)
- 出版日：2021/09/06
定 價：NT$ 10500 元
優惠價：90 折 9450 元
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商品簡介
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.
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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