TY - JOUR
T1 - Facilitating transmuters' acquisition of data scientist knowledge based on their educational backgrounds
T2 - state-of-the-practice and challenges
AU - Ramzan, Muhammad Javed
AU - Khan, Saif Ur Rehman
AU - ur-Rehman, Inayat
AU - Rehman, Muhammad Habib Ur
AU - Al-khanak, Ehab Nabiel
N1 - Accepted Author Manuscript
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Challenges
KW - Data science
KW - Data scientist
KW - Guidance
KW - Motivational factors
KW - Skillset
KW - Tools
UR - http://www.scopus.com/inward/record.url?scp=85100976023&partnerID=8YFLogxK
U2 - 10.1108/LHT-08-2020-0203
DO - 10.1108/LHT-08-2020-0203
M3 - Article
AN - SCOPUS:85100976023
SN - 0737-8831
VL - 41
SP - 1119
EP - 1144
JO - Library Hi Tech
JF - Library Hi Tech
IS - 4
ER -