DATA ANALYTICS VS DATA SCIENCE: CONCEPTS, DIFFERENCES, AND THEIR ROLE IN THE MODERN ECONOMY

Authors

  • Madina Eshboyeva Student of Jizzakh branch of National university of Uzbekistan Author

Keywords:

data analytics, data science, big data, business analytics, machine learning, digital economy, data-driven decision making

Abstract

The rapid development of digital technologies has led to the exponential growth of data in nearly every sector of the global economy. As organizations increasingly rely on data-driven decision-making, the disciplines of data analytics and data science have become fundamental tools for extracting insights from large and complex datasets. Although these terms are often used interchangeably, they differ significantly in their scope, methodologies, and practical applications. This article examines the conceptual foundations of data analytics and data science, identifies the key differences between the two disciplines, and analyzes their role in modern economic systems. The study highlights that data analytics focuses primarily on analyzing historical data to support business decision-making, whereas data science integrates statistics, programming, and machine learning techniques to develop predictive models and generate new knowledge from data. The findings indicate that both fields play complementary roles in enhancing organizational performance, improving economic efficiency, and supporting innovation in the digital economy.

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Published

2026-04-01

How to Cite

Eshboyeva, M. (2026). DATA ANALYTICS VS DATA SCIENCE: CONCEPTS, DIFFERENCES, AND THEIR ROLE IN THE MODERN ECONOMY. Scientific Practical Conference, 1(3), 885-888. https://d-pressa.com/index.php/spc/article/view/586