Buch, Englisch, 608 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1156 g
Reihe: Tourism on the Verge
Interdisciplinary Approaches, Methodologies, and Applications
Buch, Englisch, 608 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1156 g
Reihe: Tourism on the Verge
ISBN: 978-3-030-88388-1
Verlag: Springer International Publishing
The book is a very well-structured introduction to data science – not only in tourism – and its methodological foundations, accompanied by well-chosen practical cases. It underlines an important insight: data are only representations of reality, you need methodological skills and domain background to derive knowledge from them - Hannes Werthner, Vienna University of Technology
Roman Egger has accomplished a difficult but necessary task: make clear how data science can practically support and foster travel and tourism research and applications. The book offers a well-taught collection of chapters giving a comprehensive and deep account of AI and data science for tourism - Francesco Ricci, Free University of Bozen-Bolzano This well-structured and easy-to-read book provides a comprehensive overview of data science in tourism. It contributes largely to the methodological repository beyond traditional methods.
- Rob Law, University of Macau
Zielgruppe
Lower undergraduate
Autoren/Hrsg.
Fachgebiete
- Wirtschaftswissenschaften Wirtschaftssektoren & Branchen Dienstleistungssektor & Branchen Tourismuswirtschaft, Gastgewerbe
- Sozialwissenschaften Sport | Tourismus | Freizeit Tourismus & Reise Tourismus & Reise: Ökonomie, Ökologie
- Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
Weitere Infos & Material
Part I: Theoretical Fundaments.- AI and Big Data in Tourism.- Epistemological Challenges.- Data Science and Interdisciplinarity.- Data Science and Ethical Issues.- Web Scraping.- Part II: Machine Learning.- Machine Learning in Tourism: A Brief Overview.- Feature Engineering.- Clustering.- Dimensionality Reduction.- Classification.- Regression.- Hyperparameter Tuning.- Model Evaluation.- Interpretability of Machine Learning Models.- Part III: Natural Language Processing.- Natural Language Processing (NLP): An Introduction.- Text Representations and Word Embeddings.- Sentiment Analysis.- Topic Modelling.- Entity Matching: Matching Entities Between Multiple Data Sources.- Knowledge Graphs.- Part IV: Additional Methods.- Network Analysis.- Time Series Analysis.- Agent-Based Modelling.- Geographic Information System (GIS).- Visual Data Analysis.- Software and Tools.