Bridging Data Science and Big Data Analytics: Mathematical Foundations for Innovation and Scalable Efficiency

  • Ayman Mousa Mubarak General Department, College of Technology for Applied Sciences - Al-Awata, Tripoli, Libya.
DOI https://doi.org/10.58916/jhas.v10i2.719
Published: 2025-04-06
تحميل pdf (Arabic)
307
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Abstract

Big data analytics and data science have revolutionized our ability to extract actionable insights from massive datasets through advanced mathematical methodologies. This article focuses on key aspects such as statistical inference, optimization, and linear algebra, addressing the challenges of ensuring data security and seamless integration despite the complexity of large-scale datasets. By combining empirical evidence from diverse global contexts, the discussion highlights strategies to enhance data-driven decision-making while exploring the ethical dilemmas and privacy concerns associated with big data. Through practical examples and grounded analysis, this work aims to bridge the gap between theoretical understanding and real-world application in data science and analytics

Keywords: Big data Analysis, Data Analysis, Optimization, Integration

How to Cite

Ayman Mousa Mubarak. (2025). Bridging Data Science and Big Data Analytics: Mathematical Foundations for Innovation and Scalable Efficiency. Bani Waleed University Journal of Humanities and Applied Sciences, 10(2), 27-38. https://doi.org/10.58916/jhas.v10i2.719
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This work is licensed under a Creative Commons Attribution 4.0 International License.

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