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

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Τίτλος: Data science education programmes in Middle Eastern institutions: A survey study.
Συγγραφείς: Zakaria, Mahmoud Sherif
Πηγή: IFLA Journal; Mar2023, Vol. 49 Issue 1, p157-179, 23p
Θεματικοί όροι: Data science, Science education, Big data, Data management, Information technology
Γεωγραφικοί όροι: Middle East
Περίληψη: 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. [ABSTRACT FROM AUTHOR]
Copyright of IFLA Journal is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: 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. [ABSTRACT FROM AUTHOR]
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  Label:
  Group: Ab
  Data: <i>Copyright of IFLA Journal is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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