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Sinopsis
<p>This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models – based on researchers’ insights – can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques. </p><p></p>
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De la contraportada
This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models - based on researchers' insights - can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques.
"Sobre este título" puede pertenecer a otra edición de este libro.
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Data Science for Financial Econometrics (eng)
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Data Science for Financial Econometrics
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Springer, Berlin|Springer International Publishing|Springer, 2021
ISBN 10: 3030488551
ISBN 13: 9783030488550
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Librería: moluna, Greven, Alemania
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics.This book offers an overview of state-of-the-art econometric. Nº de ref. del artículo: 519855370
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Data Science for Financial Econometrics
Publicado por
Springer International Publishing Nov 2021, 2021
ISBN 10: 3030488551
ISBN 13: 9783030488550
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Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models - based on researchers' insights - can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques. 644 pp. Englisch. Nº de ref. del artículo: 9783030488550
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Data Science for Financial Econometrics
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Taschenbuch. Condición: Neu. Data Science for Financial Econometrics | Nguyen Ngoc Thach (u. a.) | Taschenbuch | x | Englisch | 2021 | Springer | EAN 9783030488550 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Nº de ref. del artículo: 120720173
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Data Science for Financial Econometrics
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Calificación del vendedor: 5 de 5 estrellas
Condición: Hervorragend. Zustand: Hervorragend | Seiten: 644 | Sprache: Englisch | Produktart: Bücher | This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models ¿ based on researchers¿ insights ¿ can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques. Nº de ref. del artículo: 38226623/1
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Data Science for Financial Econometrics
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Springer, Palgrave Macmillan Nov 2021, 2021
ISBN 10: 3030488551
ISBN 13: 9783030488550
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models - based on researchers' insights - can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 644 pp. Englisch. Nº de ref. del artículo: 9783030488550
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Data Science for Financial Econometrics
Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models - based on researchers' insights - can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques. Nº de ref. del artículo: 9783030488550
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Data Science for Financial Econometrics (Studies in Computational Intelligence, 898)
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Condición: New. 1st ed. 2021 edition NO-PA16APR2015-KAP. Nº de ref. del artículo: 26390222109
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Data Science for Financial Econometrics (Studies in Computational Intelligence, 898)
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Data Science for Financial Econometrics (Studies in Computational Intelligence, 898)
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