株式会社極東書店トップ ＞ 商品一覧 ＞ Handbook of Computational Social Science. Volume 2. : Data Science, Statistical Modelling, and Machine Learning Methods.
商品詳細
Handbook of Computational Social Science. Volume 2. : Data Science, Statistical Modelling, and Machine Learning Methods. 計算社会科学ハンドブック 第2巻：データ科学、 統計モデリング、機械学習方法
・ISBN 978-0-367-45780-8 hard GB£ 210.00
￥66,528.- (税込) ※（※）価格はご注文時の参考価格となります。
納品価格につきましては書籍の入荷時点で確定となります。
版元の原価改定、外国為替の変動等により異なる場合がございますので、予めご了承下さい。
・ISBN 978-1-032-07770-3 paper GB£ 62.99
￥19,955.- (税込) ※（※）価格はご注文時の参考価格となります。
納品価格につきましては書籍の入荷時点で確定となります。
版元の原価改定、外国為替の変動等により異なる場合がございますので、予めご了承下さい。
電子版あり 大学・学術機関向け電子ブック（eBook）ISBN 978-1-003-02524-5
| 著者・編者 | Engel, Uwe / Quan-Haase, Anabel et al. (eds.), |
|---|---|
| シリーズ | European Association of Methodology Series |
| 出版社 | (Routledge, UK) |
| 出版年月 | 2021.09 |
| ページ数 | 412 pp. |
| 言語 | ENG |
| ニュース番号 | ＜663-1557 663-19＞ |
解説
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.