This volume presents techniques and theories drawn from mathematics, statistics, computer science, and information science to analyze problems in business, economics, finance, insurance, and related fields.
The authors present proposals for solutions to common problems in related fields. To this end, they are showing the use of mathematical, statistical, and actuarial modeling, and concepts from data science to construct and apply appropriate models with real-life data, and employ the design and implementation of computer algorithms to evaluate decision-making processes.
This book is unique as it associates data science - data-scientists coming from different backgrounds - with some basic and advanced concepts and tools used in econometrics, operational research, and actuarial sciences. It, therefore, is a must-read for scholars, students, and practitioners interested in a better understanding of the techniques and theories of these fields.
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M. Kenan Terzioğlu received his MSc. degree in Actuarial Sciences from Hacettepe University, Ankara (Turkey). He studied at Tilburg University (Netherlands) in the Department of Econometrics and Operations between 2008 and 2009 while working in the Department of Actuarial Sciences at Hacettepe University between 2006 and 2009 as a Research Assistant. He worked as a Risk Analyst Assistant (Assistant Specialist) in Ziraat Bank Risk Management Department and received his Ph.D. degree in Econometrics from the Department of Econometrics at Gazi University, Ankara (Turkey). Since 2018, he has been working as an Associate Professor in the Econometrics Department at Trakya University, Edirne (Turkey). He is part of the management team at the Risk Management and Corporate Sustainability Application and Research Center at Trakya University. His research focuses on linear/non-linear time series analysis, financial econometrics, risk management, valuation method, and data sciences.
This volume presents techniques and theories drawn from mathematics, statistics, computer science, and information science to analyze problems in business, economics, finance, insurance, and related fields.
The authors present proposals for solutions to common problems in related fields. To this end, they are showing the use of mathematical, statistical, and actuarial modeling, and concepts from data science to construct and apply appropriate models with real-life data, and employ the design and implementation of computer algorithms to evaluate decision-making processes.
This book is unique as it associates data science - data-scientists coming from different backgrounds - with some basic and advanced concepts and tools used in econometrics, operational research, and actuarial sciences. It, therefore, is a must-read for scholars, students, and practitioners interested in a better understanding of the techniques and theories of these fields.
"About this title" may belong to another edition of this title.
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