Items related to Data Science for Financial Econometrics (Studies in...
- PublisherSpringer
- Publication date2020
- ISBN 10 3030488527
- ISBN 13 9783030488529
- BindingHardcover
- LanguageEnglish
- Edition number1
- Number of pages643
- EditorNgoc Thach Nguyen, Kreinovich Vladik, Trung Nguyen Duc
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Data Science for Financial Econometrics (Studies in Computational Intelligence, 898)
Seller: SpringBooks, Berlin, Germany
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Data Science for Financial Econometrics (Studies in Computational Intelligence, 898)
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Data Science for Financial Econometrics
Published by
Springer International Publishing, 2020
ISBN 10: 3030488527
ISBN 13: 9783030488529
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Data Science for Financial Econometrics
Published by
Springer International Publishing Nov 2020, 2020
ISBN 10: 3030488527
ISBN 13: 9783030488529
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Hardcover
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Buch. Condition: 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. Seller Inventory # 9783030488529
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Data Science for Financial Econometrics
Published by
Springer International Publishing, 2020
ISBN 10: 3030488527
ISBN 13: 9783030488529
New
Hardcover
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Buch. Condition: 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. Seller Inventory # 9783030488529
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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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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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Data Science for Financial Econometrics
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Springer-Nature New York Inc, 2021
ISBN 10: 3030488527
ISBN 13: 9783030488529
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Seller: Revaluation Books, Exeter, United Kingdom
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Hardcover. Condition: Brand New. 643 pages. 9.25x6.10x1.38 inches. In Stock. Seller Inventory # x-3030488527
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