Data Analytics im Risikomanagement
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Home > Business and Economics > Business and Management > Management and management techniques > Data Analytics im Risikomanagement: Descriptive Analytics - Diagnostic Analytics - Predictive Analytics
Data Analytics im Risikomanagement: Descriptive Analytics - Diagnostic Analytics - Predictive Analytics

Data Analytics im Risikomanagement: Descriptive Analytics - Diagnostic Analytics - Predictive Analytics


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About the Book

Das Buch bietet einen umfassenden Überblick über Data Analytics im Risikomanagement in Unternehmen. Die fortschreitende Digitalisierung bringt eine wachsende Menge von Daten hervor. Data Analytics beschäftigt sich mit den Methoden zur Analyse dieser Daten und umfasst neben statistischen Modellen auch das Maschinelle Lernen. Unternehmen, die die methodischen Grundlagen von Data Analytics verstehen und anwenden, können nicht nur im Risikomanagement Daten wertschöpfend nutzen, sondern auf Basis einer verbesserten Analyse und Prognose fundierte unternehmerische Entscheidungen treffen. In einem durch den Einsatz von Artificial Intelligence (AI) geprägten Unternehmensumfeld stellt dies einen erheblichen Wettbewerbsvorteil dar. Neben einer allgemeinen Einführung in Data Analytics erläutern die Autoren fundiert die Methoden der deskriptiven, diagnostischen sowie prädiktiven Datenanalyse. Basierend auf einem fiktiven Unternehmen werden Anwendungsbeispiele aus der Praxis des Risikomanagements beschrieben. Die Beispiele werden als Sourcecode in der Programmiersprache R für eine praktische Umsetzung zum Download bereitgestellt.

Table of Contents:
1. Einführung.- 1.1 Einführungsbeispiel: Auf der Suche nach der Weltformel.- 1.2 Data Analytics als Teil des "Knowledge Discovery Process Model".- 1.3 Abgrenzung von Begriffen (Data Analytics, Business Analytics, Data Mining, Big Data, Artificial Intelligence, Descriptive Analytics, Diagnostic Analytics, Predictive Analytics und Prescriptive Analytics, Supervised und Unsupervised Learning, Algorithmus etc.).- 1.4 Artificial Intelligence versus Künstliche Intelligenz.- 1.5 Menschliche Intelligenz versus Algorithmen.- 1.6 Von Descriptive Analytics bis Prescriptive Analytics.- 2. Daten als Ausgangspunkt.- 2.1 Datenkompetent (data literacy), Methodenkompetenz (statistical literacy) und Informationskompetenz (information literacy).- 2.2 Datenquellen, Datenqualität, Datentypen.- 2.3 Datenaufbereitung, fehlende Daten, Ausreißer, Datentransformation.- 2.4 Bedeutung der Stichprobengröße und mögliche Lösungsansätze.- 2.5 Data Governance als Grundlage für Datenqualität und Zuverlässigkeit.- 3. Descriptive Analytics/ Datenvisualisierung.- 3.1 Einführung in die deskriptive Statistik.- 3.2 Datenvisualisierung für eine Variable.- 3.3 Statistische Maßzahlen zur eindimensionalen Datenbeschreibung.- 3.4 Exploration des Zusammenhangs von mehreren Variablen.- 3.5 Anwendungsbeispiele in R.- 4. Fortgeschrittene deskriptive und erklärende Analyse.- (Diagnostic Analytics).- 4.1 Regressionsanalyse.- 4.2 Klassifikationsverfahren.- 4.3 Clusteranalyse.- 4.4 Assoziationsanalyse: Exploration struktureller Zusammenhänge.- 4.5 Zeitreihenanalyse.- 4.6 Anwendungsbeispiele in R.- 5. Predictive Analytics: Ein Blick in die Zukunft.- 5.1 Kurze Geschichte der Zukunft.- 5.2 Prädiktive Modellierung als Grundlage.- 5.3 Einführung in die Bayessche Statistik.- 5.4 Bayessche Netze.- 5.5 Künstliche Intelligenz und Big Data.- 5.6 Maschinelles Lernen.- 5.7 Evaluierung von prädiktiven Modellen.- 5.8 Anwendungsbeispiele in R.- 6. Data Analytics im Risikomanagement.- 6.1 Bedeutung und Potenziale von Data Analytics im Risikomanagement.- 6.2 Prescriptive Analytics im Risikomanagement.- 6.3 Artificial Intelligence im Risikomanagement.- 6.4 Modellrisiken.- 6.5 Anwendungsbeispiele in R.

About the Author :
Frank Romeike ist Geschäftsführer und Eigentümer des Kompetenzportals RiskNET und zählt international zu den führenden Experten für Risiko- und Chancenmanagement. Die von ihm gegründete Risk Academy hat weltweit rund 30.000 Risikomanager ausgebildet bzw. gecoacht. Er engagiert sich in der akademischen Lehre an Hochschulen im In- und Ausland. Prof. Dr. Gabriele Wieczorek ist Mathematikerin und Professorin mit dem Lehrgebiet Industrielle Statistik und Wahrscheinlichkeitstheorie an der Hochschule Hamm-Lippstadt. Ihre Forschung zum Risikomanagement umfasst u. a. die nicht-frequentistischen Methoden zur Risikomodellierung sowie die Anwendung von Künstlicher Intelligenz im Risikomanagement.


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Product Details
  • ISBN-13: 9783658488420
  • Publisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
  • Binding: Hardback
  • Language: German
  • Returnable: N
  • Width: 168 mm
  • ISBN-10: 3658488425
  • Publisher Date: 10 Sep 2025
  • Height: 240 mm
  • No of Pages: 400
  • Sub Title: Descriptive Analytics - Diagnostic Analytics - Predictive Analytics


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