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May 25, 20260 citationsOpen Access

The Effectiveness of Data Science Technologies in Analyzing Large-Scale Data and Their Importance in Optimizing Business Processes

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VQVosid Vaxob ugli Qarshiboyev

Key Points

  • The research investigates how data science technologies aid in analyzing large-scale data and optimizing business operations.
  • Analyzed various data science technologies, including machine learning and predictive analytics.
  • Utilized practical examples from global companies.
  • Evaluated the impact of these technologies on operational automation and decision-making.
  • Data Science technologies significantly enhance organizational productivity.
  • They reduce operational costs by streamlining processes.
  • These technologies improve strategic planning outcomes in modern enterprises.

Abstract

The rapid growth of digital technologies has led to an unprecedented increase in the volume of data generated by organizations, governments, and individuals. In this context, Data Science technologies have become one of the most effective tools for analyzing large-scale data and optimizing business processes. This article examines the effectiveness of Data Science technologies in big data analytics and their role in improving operational efficiency, decision-making accuracy, and competitive advantage in business environments. The study is based on factual scientific sources and practical examples from global companies that implement machine learning, predictive analytics, artificial intelligence, and data mining technologies. The research analyzes the methodological approaches used in Data Science and evaluates their impact on operational automation, customer behavior analysis, supply chain management, and financial forecasting. The article concludes that Data Science technologies significantly enhance organizational productivity, reduce operational costs, and improve strategic planning in modern enterprises.

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Vosid Vaxob ugli Qarshiboyev (2026) studied this question.

synapsesocial.com/papers/6a13e7cf0e02ee3982d32710https://doi.org/10.5281/zenodo.20353247
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