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Data Science and Multiple Criteria Decision Making Approaches in Finance by Hasan Dincer
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Data Science and Multiple Criteria Decision Making Approaches in Finance

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Description

This book considers and assesses essential financial issues by utilizing data science and fuzzy multiple criteria decision making (MCDM) methods. It introduces readers to a range of data science methods, and demonstrates their application in the fields of business, health, economics, finance and engineering. In addition, it provides suggestions based on the assessment results on each topic, which can help to enhance the efficiency of the financial system and the sustainability of economic development. Given its scope, the book will help readers broaden their perspective on the assessment and evaluation of financial issues using data science and MCDM approaches.

Author Biography:

Gökhan Silahtaroğlu is a Professor of Data Science andHead of the Department of Management Information Systems, Faculty of Economics and Administrative Sciences, Istanbul Medipol University (Turkey). Dr. Silahtaroğlu received his PhD in management sciences and quantitative methods from Istanbul University in 2005. Hasan Dinçer is a Professor of Finance at the Faculty of Economics and Administrative Sciences, Istanbul Medipol University (Turkey). He has more than 150 scientific articles to his credit, many of which are indexed in SSCI, SCI-Expanded and Scopus. He is also the editor of numerous books published by Springer and IGI Global. Serhat Yüksel is an Associate Professor of Finance at the Faculty of Economics and Administrative Sciences, Istanbul Medipol University (Turkey). He has more than 150 scientific articles, many of which are indexed in SSCI, SCI-Expanded and Scopus, to his credit. He is also the editor of various books published by Springer and IGI Global.
Release date NZ
May 31st, 2022
Audience
  • Professional & Vocational
Edition
1st ed. 2021
Illustrations
14 Illustrations, black and white; XVI, 173 p. 14 illus.
Pages
173
ISBN-13
9783030741785
Product ID
35840419

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