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22대 대한민국 국회 국회정보길라잡이 국회의원검색
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Introduction: Data Science in Tourism
Industry Insights from Data Scientists: Q&A Session
Part I Theoretical Fundaments
AI and Big Data in Tourism
Luisa Mich
Epistemological Challenges
Roman Egger and Joanne Yu
Data Science and Interdisciplinarity
Roman Egger and Joanne Yu
Data Science and Ethical Issues
Roman Egger, Larissa Neuburger, and Michelle Mattuzzi
Web Scraping
Roman Egger, Markus Kroner, and Andreas Stöckl
Part II Machine Learning
Machine Learning in Tourism: A Brief Overview
Roman Egger
Feature Engineering
Pablo Duboue
Clustering
Matthias Fuchs and Wolfram Höpken
Dimensionality Reduction
Nikolay Oskolkov
Classification
Ulrich Bodenhofer and Andreas Stöckl
Regression
Andreas Stöckl and Ulrich Bodenhofer
Hyperparameter Tuning
Pier Paolo Ippolito
Model Evaluation
Ajda Pretnar Žagar and Janez Demšar
Interpretability of Machine Learning Models
Urszula Czerwinska
Part III Natural Language Processing
Natural Language Processing (NLP): An Introduction
Roman Egger and Enes Gokce
Text Representations and Word Embeddings
Roman Egger
Sentiment Analysis
Andrei P. Kirilenko, Luyu Wang, and Svetlana O. Stepchenkova
Topic Modelling
Roman Egger
Entity Matching: Matching Entities Between Multiple Data
Sources
Ivan Bilan
Knowledge Graphs
Mayank Kejriwal
Part IV Additional Methods
Network Analysis
Rodolfo Baggio
Time Series Analysis
Irem Onder and Wenqi Wei
Agent-Based Modelling
Jillian Student
Geographic Information System (GIS)
Andrei P. Kirilenko
Visual Data Analysis
Johanna Schmidt
Software and Tools
Roman Egger
Glossary
Index

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Applied data science in tourism : interdisciplinary approaches, methodologies, and applications 이용현황 표 - 등록번호, 청구기호, 권별정보, 자료실, 이용여부로 구성 되어있습니다.
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출판사 책소개

알라딘제공
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. 

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



New feature

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. 

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.



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