Data Science and Interdisciplinarity

Egger, Roman; Yu, Jing

doi:10.1007/978-3-030-88389-8_3

Cited by 6 publications

(2 citation statements)

References 43 publications

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“…Over the past years, a number of data-driven approaches promising specific advantages over traditional segmentation techniques have been introduced. Yet, these days, with the rapid development of new algorithms-especially in the field of machine learning-even more possibilities have emerged, albeit only hesitantly applied to tourism research (Egger, 2022a). All the same, a few exceptions including self-organizing maps (SOMs) have a long history in customer segmentation.…”

Section: Machine Learning-based Segmentationmentioning

confidence: 99%

Vectorize Me! A Proposed Machine Learning Approach for Segmenting the Multi-optional Tourist

Egger

2023

Journal of Travel Research

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Contemporary consumer behavior is characterized by its multidimensionality and complexity, which, at the same time, pushes traditional segmentation approaches to their limits. In response, this methodological study proposes a multistage machine learning-based segmentation process using semiotic-semantic community detection. This innovative method was conducted exemplarily and evaluated on a representative sample of 1,101 German travelers. The main contribution of this study lies in the novel use of word vectors, which result from assigning a semiotic meaning to travel-type images. Thus, high-dimensional data could be used during the segmentation process, overcoming several classical segmentation problems. By using semantic similarities, tourists could be grouped and represented in their multidimensionality. From a theoretical perspective, this study was inspired by postmodern tourism practices in order to better understand the (oftentimes) hybrid and multilayered behaviors of tourists. To make this innovative approach reproducible, recommendations for implementation and all necessary data have been provided.

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Section: Machine Learning-based Segmentationmentioning

confidence: 99%

Vectorize Me! A Proposed Machine Learning Approach for Segmenting the Multi-optional Tourist

Egger

2023

Journal of Travel Research

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“…In other words, many disciplines – such as law, history and even nursing – have adopted data science because they deal with data-intensive and big data. Other examples of such disciplines include astronomy (Borne et al, 2009), media and entertainment (Gold et al, 2013), climate change (Faghmous and Kumar, 2014), neurobiology (Dierick and Gabbiani, 2015), physical medicine and rehabilitation (Ottenbacher et al, 2019), tourism (Egger, 2022) and other related disciplines as introduced by Cao (2018) in his book Data Science Thinking: The Next Scientific, Technological and Economic Revolution .…”

Section: Introductionmentioning

confidence: 99%

Data science education programmes in Middle Eastern institutions: A survey study

Zakaria

2022

IFLA Journal

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In response to the current trends in dealing with data in academia, various research institutions and commercial entities around the world are building new programmes to fill the gaps in workforce demand in specific disciplines, including data curation, big data, data management, data science and data analytics. Thus, the aim of the present study was to reveal the reality of data science education in the Middle East and to determine the opportunities and challenges for teaching data science in the region. Thirteen countries in the Middle East were offering 48 data science programmes at the time of the study. The results reveal that these data science programmes significantly use the words ‘data’ and ‘analytics’ in their names. With regard to the academic affiliations of the data science programmes, the study found that they are offered in a variety of schools, especially computer science, information technology and business. Moreover, the study found that computer science is the dominant trend in the programmes. Data science programmes have a significant overlap with other programmes, especially statistics and computer science, because of the interdisciplinary nature of this field. Data science schools in the Middle East differ in terms of their programme titles, programme descriptions, course catalogues, curriculum structures and course objectives. Broadly, this study may be useful for those who are seeking to establish a data science programme or to strengthen data science curricula at both the undergraduate and postgraduate levels.

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Feature Engineering

Duboue

2022

Applied Data Science in Tourism

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Data Science and Interdisciplinarity

Cited by 6 publications

References 43 publications

Vectorize Me! A Proposed Machine Learning Approach for Segmenting the Multi-optional Tourist

Vectorize Me! A Proposed Machine Learning Approach for Segmenting the Multi-optional Tourist

Data science education programmes in Middle Eastern institutions: A survey study

Feature Engineering

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