Business Intelligence in the Tourism Sector

Intelligent Tourism Data Management. Intelligent decision-making. Machine learning in decision-making. Prediction, classification, regression, and clustering algorithms. Knowledge extraction from tourism data. Big data analytics in tourism. Data visualization technologies.

• Understanding the transformation of tourism and the emerging concept of Tourism 4.0
• Understanding the opportunities and challenges arising from the digital era in the tourism sector
• Understanding the field of Artificial Intelligence and its main applications in tourism
• Understanding the field of Machine Learning and Data Mining
• Understanding the Data Transformation process
• Understanding fundamental Machine Learning algorithms
• Development of digital skills and familiarization with the Weka Machine Learning workbench
• Application of traditional and advanced supervised learning methods to business planning problems
• Application of traditional and advanced unsupervised learning methods to business planning problems

Week 1: The digital transformation of Greek tourism – challenges and prospects

Week 2: Introduction to Artificial Intelligence, Machine Learning, and Data Mining, Data Transformation – Supervised Learning – Classification – Training – Validation – Testing

Week 3: Familiarization with the Weka workbench

Week 4: k-Nearest Neighbors algorithm, Decision Tree induction algorithm

Week 5: Experimental application of algorithms in Weka using tourism data examples

Week 6: Data Science and Business Data Analytics I

Week 7: Visualization algorithms in real-world tourism business problems

Week 8: Clustering algorithms in real-world tourism business problems

Week 9: Supervised learning methods applied to tourism data

Week 10: Decision Support Systems for tourism data II

Week 11: Recommender systems applied to tourism data

Week 12: Case studies

Week 13: Review and preparation for examinations

1. Witten, I., Frank, E., & Hall, M. (2016). Data Mining: Practical Machine Learning Tools and Techniques, 4th Edition, Morgan Kaufmann.
2. Mitchell, T. (1997). Machine Learning, McGraw Hill.
3. Dunham, M. (2002). Data Mining, New Technologies Publications.
4. Machine Learning, Springer.
5. Data Mining and Knowledge Discovery, Springer.
6. Applied Artificial Intelligence, Taylor & Francis.
7. Delen, D., & Ram, S. (2018). Research challenges and opportunities in business analytics. Journal of Business Analytics, 1(1), 2–12.
8. Liebowitz, J. (Ed.). (2013). Big Data and Business Analytics. CRC Press.
9. Sauter, V. L. (2014). Decision Support Systems for Business Intelligence. John Wiley & Sons.

• Search, analysis, and synthesis of data and information using appropriate technologies
• Teamwork
• Work in an interdisciplinary environment
• Generation of new research ideas
• Critical thinking and self-reflection
• Promotion of free, creative, and inductive thinking

Support of the learning process through the Open Courses electronic platform and the Zoom synchronous distance learning platform.

I. Written final examination (50%), including critical-thinking questions and problem-solving exercises.

II. Group Assignment (50%), including:

• Literature review on a Machine Learning application in tourism data
• Development of a complete Machine Learning experiment on a tourism dataset
• Presentation, evaluation, and analysis of the experimental results
• Derivation of conclusions from the experiment and identification of future directions

Assessment criteria: validity, relevance, and coverage of the literature review; methodological rigor in conducting the experiment; depth of analysis of results and conclusions.