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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM66665
Volume:
10 |
8 |
Aug 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Science & Engineering
MERI College of Engineering & Technology
Data science has grown from a loosely defined intersection of statistics and computer science into one of the most consequential disciplines shaping contemporary research, industry, and public policy. This paper presents a structured review of the field, tracing its conceptual origins, examining the methodological frameworks that guide applied projects, surveying its principal application domains, and critically assessing the ethical and workforce-related challenges that accompany its rapid expansion. The review synthesises evidence from peer-reviewed literature, industry research, and professional bodies to argue that data science is best understood not as a single technique but as a socio-technical practice whose success depends as much on organisational process, governance, and human judgement as on algorithmic sophistication. Particular attention is paid to the persistent gap between technical capability and reliable real-world deployment, to algorithmic bias and privacy as central ethical concerns, and to the way generative artificial intelligence is reshaping the skills and roles the discipline demands. The paper concludes that the next phase of data science will be defined less by raw analytical power and more by the discipline's capacity to institutionalise trustworthy, transparent, and reproducible practice.
KEYWORDS
Data science has evolved from a loosely defined extension of statistics into a distinct, application-driven discipline that now shapes decisions across nearly every major economic sector. This review has traced that evolution from its statistical and computational origins through the methodological frameworks that structure applied projects, the domains in which the discipline delivers demonstrable value, and the ethical and workforce challenges that accompany its continued growth. The evidence surveyed here supports a central conclusion: the discipline's future development will be determined less by further gains in raw algorithmic performance and more by its capacity to institutionalise trustworthy process, rigorous governance, and a workforce equipped to exercise judgement over increasingly automated analytical systems. Organisations and researchers that treat data science as a purely technical pursuit risk repeating the well-documented pattern of technically sound projects that fail to reach production or to earn stakeholder trust; those that treat it as an integrated technical, organisational, and ethical practice are better positioned to realise its long-term value.
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Publication Date
8/11/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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