Applied Data Science in Tourism -

. .

Résumé :
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....

Biographie:
Dr. Roman Egger is a full Professor at the Salzburg University of Applied Sciences at the Department of Innovation and Management in Tourism, where he is the head of eTourism, and head of key competencies and research. His research focus lies on new technologies in tourism and their adoption from a user-centric perspective, as well as on methodological issues in tourism research. Roman has published 19 books so far, a large number of articles and chapters in international journals and edited books, is co-editor of the Journal of Tourism Science (De Gruyter), series editor of Tourism on the Verge (Springer), and board member of a number of journals. He is a member of IFITT, AIEST, DGT, and a fellow of The ICE. Roman has received more than a dozen awards in his career.

Sommaire:
Access to large data sets has led to a paradigm shift in the tourism research landscape. Big data is enabling a new form of knowledge gain, while at the same time shaking the epistemological foundations and requiring new methods and analysis approaches. It allows for interdisciplinary cooperation between computer sciences and social and economic sciences, and complements the traditional research approaches. This book provides a broad basis for the practical application of data science approaches such as machine learning, text mining, social network analysis, and many more, which are essential for interdisciplinary tourism research. Each method is presented in principle, viewed analytically, and its advantages and disadvantages are weighed up and typical fields of application are presented. The correct methodical application is presented with a how-to approach, together with code examples, allowing a wider reader base including researchers, practitioners, and students entering the field.