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book: Actuarial Data Science
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Actuarial Data Science

Maschinelles Lernen in der Versicherung
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Language: German
Published/Copyright: 2021
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About this book

Actuaries, business mathematicians and IT specialists in corresponding companies are increasingly confronted with new requirements in the areas of IT automation, data management, machine learning / AI and data security, which are not covered by their previous basic mathematical and scientific training. The authors (specialists and certified DAV teachers) provide a fundamental introduction to these topics.
  • Ausführliche Darstellung der Grundlagen des Data Science
  • Autoren sind erfahrene Dozenten der Deutschen Aktuar Akademie
  • In Kursen erprobt
  • Zahlreiche Fallstudien

Author / Editor information

Martin Seehafer, München; Stefan Nörtemann, Essen

Topics

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    Frontmatter

    I
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    Vorwort

    V
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    Inhalt

    VII
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    1 Actuarial Data Science – Business Cases

    1
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    2 Crashkurs in Data Mining Anwendungen

    5
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    3 Neue Versicherungsprodukte

    12
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    4 Tools, Sprachen, Frameworks

    19
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    5 Informationstechnologie

    35
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    6 Mathematische Verfahren

    160
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    7 Korrelation und kausale Inferenz

    305
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    8 Data Mining

    320
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    9 Gesellschaftliches Umfeld

    332
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    A Appendix

    346
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    Nachwort & Danksagungen

    361
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    Literatur

    363
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    Stichwortverzeichnis

    369
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Publishing information
Pages and Images/Illustrations in book
eBook published on:
January 18, 2021
eBook ISBN:
9783110659344
Paperback published on:
January 18, 2021
Paperback ISBN:
9783110659283
Pages and Images/Illustrations in book
Front matter:
10
Main content:
370
Illustrations:
8
Coloured Illustrations:
86
https://doi.org/10.1515/9783110659344
eBook ISBN: 9783110659344
Paperback ISBN: 9783110659283
Keywords for this book
Actuary; Data Science; Financial Mathematics; Actuarial mathematics
Audience(s) for this book
Mathematiker, Informatiker, IT-Spezialisten, Risiko-Manager
Safety & product resources
Downloaded on 19.4.2026 from https://www.degruyterbrill.com/document/doi/10.1515/9783110659344/html
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