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

Part I. Theoretical Fundaments.- Introduction: Data Science in Tourism.- Industry Insights from Data Scientists: A Q&A Session.- AI and Big Data in Tourism.- Epistemological Challenges.- Interdisciplinarity in Data Science.- Data Science & Ethics.- Web Mining & Data Crawling.- Part II. Machine Learning.- Machine Learning: a Primer.- Feature Engineering.- Clustering.- Dimensionality Reduction.- Classification.- Regression.- Hyperparameter Tuning.- Model Evaluation.- Data Interpretability of ML-Models.- Part III. Natural Language Processing.- Introduction: Natural Language Processing.- Text Representation and Word Embeddings.- Sentiment Analysis.- Topic Modeling.- Entity Matching.- Knowledge-Graphs.- Part IV. Additional Methods.- Social Network Analysis.- Time Series Analysis.- Agent-based Modeling.- GIS Analysis.- Data Visualization.- Software and Tools.