The book provides a comprehensive overview of current practices and future directions in airline revenue management. It explains state-of-the-art revenue management approaches and outlines how these will be augmented and enhanced through modern data science and machine learning methods in the future. Several practical examples and applications will make the reader familiar with the relevance of the corresponding ideas and concepts for an airline commercial organization. The book is ideal for both students in the field of airline and tourism management as well as for practitioners and industry experts seeking to refresh their knowledge about current and future revenue management approaches, as well as to get an introductory understanding of data science and machine learning methods. Each chapter closes with a checkpoint, allowing the reader to deepen the understanding of the contents covered.
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Curt Cramer is an Advanced Analytics and Data Science executive in the retail industry. He has gathered extensive applied analytics experience in 15 years of consulting and line management responsibilities across industries, including leisure travel. He holds a Ph. D. in Engineering/Computer Science from the University of Karlsruhe/KIT.
Andreas Thams is an Honorary Professor for Airline Management at University of Applied Sciences Worms. He held various commercial management positions in the airline, travel, and logistics industry, particularly in the field of revenue management. He has a Ph. D. in Econometrics from Freie Universität Berlin.
This book provides a comprehensive overview of current practices and future directions in airline revenue management. It explains state-of-the-art revenue management approaches and outlines how these will be augmented and enhanced through modern data science and machine learning methods in the future. Several practical examples and applications help to familiarize the reader with the relevance of the corresponding ideas and concepts for a commercial airline organization. The book is ideal for both students in the field of airline and tourism management as well as for practitioners and industry experts seeking to refresh their knowledge about current and future revenue management approaches and to gain an introductory understanding of data science and machine learning methods. Each chapter closes with a checkpoint, allowing the reader to deepen their understanding of the contents covered.
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Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The book provides a comprehensive overview of current practices and future directions in airline revenue management. It explains state-of-the-art revenue management approaches and outlines how these will be augmented and enhanced through modern data science and machine learning methods in the future. Several practical examples and applications will make the reader familiar with the relevance of the corresponding ideas and concepts for an airline commercial organization. The book is ideal for both students in the field of airline and tourism management as well as for practitioners and industry experts seeking to refresh their knowledge about current and future revenue management approaches, as well as to get an introductory understanding of data science and machine learning methods. Each chapter closes with a checkpoint, allowing the reader to deepen the understanding of the contents covered.This textbook has been recommended and developed for university courses in Germany, Austria and Switzerland. 132 pp. Englisch. Seller Inventory # 9783658337209
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - The book provides a comprehensive overview of current practices and future directions in airline revenue management. It explains state-of-the-art revenue management approaches and outlines how these will be augmented and enhanced through modern data science and machine learning methods in the future. Several practical examples and applications will make the reader familiar with the relevance of the corresponding ideas and concepts for an airline commercial organization. The book is ideal for both students in the field of airline and tourism management as well as for practitioners and industry experts seeking to refresh their knowledge about current and future revenue management approaches, as well as to get an introductory understanding of data science and machine learning methods. Each chapter closes with a checkpoint, allowing the reader to deepen the understanding of the contents covered.This textbook has been recommended and developed for university courses in Germany, Austriaand Switzerland. Seller Inventory # 9783658337209
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Aa comprehensive overview of current practices and future directions in airline revenue managementIdeal for both students in the field of airline and tourism management as well as for practitioners and industry experts. Seller Inventory # 458735028
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Buch. Condition: Neu. Neuware -The book provides a comprehensive overview of current practices and future directions in airline revenue management. It explains state-of-the-art revenue management approaches and outlines how these will be augmented and enhanced through modern data science and machine learning methods in the future. Several practical examples and applications will make the reader familiar with the relevance of the corresponding ideas and concepts for an airline commercial organization. The book is ideal for both students in the field of airline and tourism management as well as for practitioners and industry experts seeking to refresh their knowledge about current and future revenue management approaches, as well as to get an introductory understanding of data science and machine learning methods. Each chapter closes with a checkpoint, allowing the reader to deepen the understanding of the contents covered.Springer Gabler in Springer Science + Business Media, Tiergartenstr. 15-17, 69121 Heidelberg 132 pp. Englisch. Seller Inventory # 9783658337209
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