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Computational Social Science: Discovery and Prediction (Analytical Methods for Social Research) - Tapa dura
Sinopsis
This book provides an overview of cutting-edge approaches to computational social science.
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R. Michael Alvarez is a Professor of Political Science at the California Institute of Technology. He is a Fellow of the Society for Political Methodology. He is the coeditor of Political Analysis and of the Cambridge University Press series, Analytical Methods for Social Science. He recently coauthored, with Lonna Rae Atkeson and Thad E. Hall, Evaluating Elections: A Handbook of Methods and Standards. He is also codirector of the Caltech/MIT Voting Technology Project.
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Computational Social Science: Discovery and Prediction (Analytical Methods for Social Research)
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Cambridge University Press, 2016
ISBN 10: 1107107881
ISBN 13: 9781107107885
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Computational Social Science: Discovery and Prediction
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Computational Social Science: Discovery and Prediction (Analytical Methods for Social Research)
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Cambridge University Press, 2016
ISBN 10: 1107107881
ISBN 13: 9781107107885
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Computational Social Science: Discovery and Prediction
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ISBN 10: 1107107881
ISBN 13: 9781107107885
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Computational Social Science
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Cambridge University Press, 2018
ISBN 10: 1107107881
ISBN 13: 9781107107885
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book serves as an introduction to the field of computational social science for academics, students, and practitioners. It will also appeal to data scientists who wish to learn about innovations in the area, in particular those interested in how data a. Nº de ref. del artículo: 447215915
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Computational Social Science (Hardcover)
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Cambridge University Press, Cambridge, 2016
ISBN 10: 1107107881
ISBN 13: 9781107107885
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Hardcover. Condición: new. Hardcover. Quantitative research in social science research is changing rapidly. Researchers have vast and complex arrays of data with which to work: we have incredible tools to sift through the data and recognize patterns in that data; there are now many sophisticated models that we can use to make sense of those patterns; and we have extremely powerful computational systems that help us accomplish these tasks quickly. This book focuses on some of the extraordinary work being conducted in computational social science - in academia, government, and the private sector - while highlighting current trends, challenges, and new directions. Thus, Computational Social Science showcases the innovative methodological tools being developed and applied by leading researchers in this new field. The book shows how academics and the private sector are using many of these tools to solve problems in social science and public policy. This book serves as an introduction to the field of computational social science for academics, students, and practitioners. It will also appeal to data scientists who wish to learn about innovations in the area, in particular those interested in how data analytics is applied to study social behavior. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Nº de ref. del artículo: 9781107107885
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Computational Social Science: Discovery and Prediction (Analytical Methods for Social Research)
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Cambridge University Press, 2016
ISBN 10: 1107107881
ISBN 13: 9781107107885
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Computational Social Science
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Cambridge University Press, 2016
ISBN 10: 1107107881
ISBN 13: 9781107107885
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Quantitative research in social science research is changing rapidly. Researchers have vast and complex arrays of data with which to work: we have incredible tools to sift through the data and recognize patterns in that data; there are now many sophisticated models that we can use to make sense of those patterns; and we have extremely powerful computational systems that help us accomplish these tasks quickly. This book focuses on some of the extraordinary work being conducted in computational social science - in academia, government, and the private sector - while highlighting current trends, challenges, and new directions. Thus, Computational Social Science showcases the innovative methodological tools being developed and applied by leading researchers in this new field. The book shows how academics and the private sector are using many of these tools to solve problems in social science and public policy. Nº de ref. del artículo: 9781107107885
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Computational Social Science: Discovery and Prediction
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Computational Social Science (Hardcover)
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Cambridge University Press, Cambridge, 2016
ISBN 10: 1107107881
ISBN 13: 9781107107885
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Hardcover. Condición: new. Hardcover. Quantitative research in social science research is changing rapidly. Researchers have vast and complex arrays of data with which to work: we have incredible tools to sift through the data and recognize patterns in that data; there are now many sophisticated models that we can use to make sense of those patterns; and we have extremely powerful computational systems that help us accomplish these tasks quickly. This book focuses on some of the extraordinary work being conducted in computational social science - in academia, government, and the private sector - while highlighting current trends, challenges, and new directions. Thus, Computational Social Science showcases the innovative methodological tools being developed and applied by leading researchers in this new field. The book shows how academics and the private sector are using many of these tools to solve problems in social science and public policy. This book serves as an introduction to the field of computational social science for academics, students, and practitioners. It will also appeal to data scientists who wish to learn about innovations in the area, in particular those interested in how data analytics is applied to study social behavior. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Nº de ref. del artículo: 9781107107885
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