Chapter 3: Interdisciplinarity in Data Science

by Roman Egger & Chung-En Yu

Salzburg University of Applied Sciences, Innovation and Management in Tourism

In parallel with the progression of technology, the tourism industry has been continuously confronted with a large amount of data that needs to be systematically analysed in order to gain significant insights for the science and business sectors. Data science has emerged as an interdisciplinary field where specific competencies from different sub-disciplines come together. This poses far-reaching challenges for both researchers and practitioners alike. To unlock the pillars of data science research and provide a guideline for relevant stakeholders in tourism, this chapter aims to conceptualise the core competencies needed in the data science process. More specifically, it will start with a discussion regarding the interplay between computer science, mathematics and statistics, and domain knowledge. Next, the procedure of data science will be classified into seven distinct phases: (1) topic formulation and relevance for academia and industry, (2) data access and data collection, (3) data pre-processing, (4) feature engineering, (5) analysis, (6) model evaluation and model tuning, and (7) interpretation of results. This chapter will review each stage in depth and evaluate the corresponding level of knowledge and competencies required for each phase. Finally, current implications and potential future directions of data science in the tourism industry will be discussed.