Facilitating transmuters' acquisition of data scientist knowledge based on their educational backgrounds: state-of-the-practice and challenges
ISSN: 0737-8831
Article publication date: 19 February 2021
Issue publication date: 25 August 2023
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.
Keywords
Acknowledgements
This work is related to the master research by Muhammad Javed Ramzan, supported by CUI, Islamabad, Pakistan. The authors would like to acknowledge the members of Software Reliability Engineering Group (SREG) at CUI, Islamabad, Pakistan who have provided suggestions and critical analysis of current work. Finally, we appreciate the participants (Data Scientists), who have provided their valuable response and feedback by timely responding to the formulated questionnaire.Research funding: This work has been sponsored partially by the NWO/TTW project Multi-scale integrated Traffic Observatory for Large Road Networks (MiRRORS) under grant number 16270.Data Statement: The data that support the findings of this study are openly available on Mendeley Data (Ramzan et al., 2020).Disclosure statement: All authors declare that they have no competing interests.
Citation
Ramzan, M.J., Khan, S.U.R., ur-Rehman, I., Rehman, M.H.U. and Al-khannaq, E.N. (2023), "Facilitating transmuters' acquisition of data scientist knowledge based on their educational backgrounds: state-of-the-practice and challenges", Library Hi Tech, Vol. 41 No. 4, pp. 1119-1144. https://doi.org/10.1108/LHT-08-2020-0203
Publisher
:Emerald Publishing Limited
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