Data Science Journey: Past, Present, and the Path Ahead

Main Article Content

Disha Grover

Abstract

Data science has emerged as a critical field that drives decision-making and innovation across various industries, such as healthcare, finance, and retail. Initially rooted in traditional statistical analysis, data science has rapidly evolved through advancements in machine learning (ML), artificial intelligence (AI), and big data technologies. This paper explores the historical development of data science, from its statistical origins to its present role as a multifaceted discipline combining computer science, statistics, and domain expertise. The paper also addresses current challenges, such as data quality, privacy, scalability, and the talent gap, while proposing solutions, including automation, cloud computing, and improved data governance. Additionally, it compares the evolution of data science tools and techniques quantitatively, highlighting significant improvements in efficiency, cost-effectiveness, and scalability. Finally, the paper emphasizes the importance of ethical considerations for the future of data science. The analysis provides a comprehensive overview of the field’s growth and offers insights into its future trajectory.

Article Details

How to Cite
Disha Grover. (2025). Data Science Journey: Past, Present, and the Path Ahead. European Economic Letters (EEL), 15(1), 429–434. https://doi.org/10.52783/eel.v15i1.2414
Section
Articles