International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025
p-ISSN: 2395-0072
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A Review of Data Science Applications across Major Sectors: Opportunities, Challenges and Future Trends Karimulla Polisetti1, Hariprasad Chitte2 1 Assistant Professor, EEE Department, Bapatla Engineering College, Bapatla, Andhra Pradesh, India 2 Assistant Professor, EEE Department, Bapatla Engineering College, Bapatla, Andhra Pradesh, India
---------------------------------------------------------------------***--------------------------------------------------------------------trends, methodologies, and challenges in data science, and to Abstract - Data science has become a transformative
force across numerous sectors, enabling advancements in predictive modeling, personalized services, and data-driven decision-making. This paper reviews recent applications of data science in healthcare, finance, retail, manufacturing, social media, and the public sector. Key methods such as machine learning, natural language processing, and predictive analytics are discussed in each sector, along with challenges such as data privacy, scalability, and model interpretability. Emerging trends and future directions are highlighted, emphasizing the need for ethical, transparent, and interdisciplinary approaches in deploying data science solutions across industries.
identify areas where future research and development may bring further advancements. This overview seeks to guide practitioners and researchers in harnessing data science effectively and ethically across domains.
Keywords—Machine learning, Natural language processing, Predictive analytics.
2. Applications of data science
In this paper optimal power flow is formulated minimizing the operation cost and compared these methods with respect to system loss to that of conventional power flow. Section II describes the Applications of Data Science, section III gives the challenges in Data Science Applications, section IV presents future directions and section V concludes the paper.
Here’s a detailed exploration of core applications of data science across various industries. Each section describes key applications, data science methods used, challenges, and emerging trends. Data cycle as shown in fig.2.
1.INTRODUCTION In recent years, data science has rapidly evolved into a powerful field, transforming industries by enabling datadriven decision-making and insights. As the volume of digital data grows exponentially, organizations across sectors are leveraging data science to extract valuable patterns, make predictions, and drive innovation. This interdisciplinary field combines techniques from statistics, machine learning, and computer science, allowing businesses and researchers to handle complex datasets and solve critical problems. Data scince life cycle is as shown in fig.1. This review paper explores the impact of data science across various industries, highlighting its applications in fields such as healthcare, finance, retail, manufacturing, social media, and the public sector. By examining these diverse applications, we aim to provide a comprehensive understanding of the current
Fig.2.Data cycle Finally, complete content and organizational editing before formatting. Please take note of the following items when proofreading spelling and grammar: 2.1 Healthcare and Medicine Applications:
Fig.1.Data scince life cycle
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Predictive Modeling for Patient Outcomes: Data science helps predict patient outcomes, such as the likelihood of developing chronic diseases, enabling preventive interventions [2].
Medical Image Analysis: Deep learning algorithms are applied to analyze medical images (e.g., MRIs, X-
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