Handbook of computational social science : data science, statistical modelling, and machine learning methods
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 … celý popis
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- Další autoři:
- , , ,
- Typ dokumentu:
- Knihy
- Rozsah:
- xxii, 411 stran :
- Vydáno:
-
Abingdon, Oxon ; New York, NY :
Routledge,
2022.
- Vydání:
- 1st published 2022
- Edice:
- European association of methodology
- Témata:
- Fyzický popis:
- xxii, 411 stran : ilustrace, grafy ; 25 cm.
- Bibliografie:
- Obsahuje bibliografie a rejstřík
- ISBN:
- 978-0-367-45780-8
978-1-032-07770-3
Instituce:
Souborný katalog ČR
MARC
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| 245 | 0 | 0 | |a Handbook of computational social science : |b data science, statistical modelling, and machine learning methods / |c edited by Uwe Engel, Anabel Quan-Haase, Sunny Xun Liu and Lars Lyberg. |
| 246 | 2 | 3 | |a Handbook of computational social science : |b data science, statistical modelling, and machine learning methods. Volume 2. |
| 250 | |a 1st published 2022 | ||
| 264 | 1 | |a Abingdon, Oxon ; |a New York, NY : |b Routledge, |c 2022. | |
| 300 | |a xxii, 411 stran : |b ilustrace, grafy ; |c 25 cm. | ||
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| 490 | 0 | |a European association of methodology | |
| 504 | |a Obsahuje bibliografie a rejstřík | ||
| 520 | |a 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. -- Provided by publisher. | ||
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