Facilitating transmuters' acquisition of data scientist knowledge based on their educational backgrounds
state-of-the-practice and challenges
Muhammad Javed Ramzan (International Islamic University Islamabad)
Saif Ur Rehman Khan (International Islamic University Islamabad)
Inayat ur-Rehman (International Islamic University Islamabad)
Muhammad Habib Ur Rehman (Khalifa University of Science and Technology)
Ehab Nabiel Al-khanak (Transport and Planning)
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Abstract
Purpose: In recent years, data science has become a high-demand profession, thereby attracting transmuters (individuals who want to change their profession due to industry trends) to this field. The primary purpose of this paper is to guide transmuters in becoming data scientists. Design/methodology/approach: An exploratory study was conducted to uncover the challenges faced by data scientists according to their educational backgrounds. An extensive set of responses from 31 countries was received. Findings: The results reveal that skill requirements and tool usage vary significantly with educational background. However, regardless of differences in academic background, the data scientists surveyed spend more time analyzing data than operationalizing insight. Research limitations/implications: The collected data are available to support replication in various scenarios, for example, for use as a roadmap for those with an educational background in art-related disciplines. Additional empirical studies can also be conducted specific to geographical location. Practical implications: The current work has categorized data scientists by their fields of study making it easier for universities and online academies to suggest required knowledge (courses) according to prospective students' educational background. Originality/value: The conducted study suggests the required knowledge and skills for transmuters to acquire, based on their educational background, and reports a set of motivational factors attracting them to adopt the data science field.