Applied Data Science in Tourism : Interdisciplinary Approaches, Methodologies, and Applications

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.