Data-Driven Intelligence: A Data Science Perspective

Abstract

The rise of Big Data has created unprecedented challenges and opportunities across industries, driving the need for adaptive, scalable models to process and analyze vast amounts of data. This paper  explores the integration of machine learning (ML) and data science techniques in the context of Big Data, focusing on federated learning, deep learning, and real-time data analytics. By investigating the latest advancements and challenges in ML, the paper  addresses how these technologies can be applied to enhance decision-making in sectors such as healthcare, finance, and IoT. It also identifies critical research gaps, providing a roadmap for future work in privacy-preserving, scalable, and sustainable machine learning models.