Optimizing Big Data Analytics Using Advanced Data Science Algorithms

Abstract

Big data analytics has become a cornerstone of decision-making in industries ranging from healthcare to supply chain management. However, as the volume, velocity, and complexity of data grow, traditional optimization models struggle to meet the demands of real-time processing and scalability. This paper explores advanced data science algorithms that optimize big data analytics, focusing on innovative approaches that integrate machine learning, artificial intelligence (AI), and emerging technologies like quantum computing and edge computing. It highlights hybrid optimization models, providing case studies from sectors such as healthcare, supply chain management, and the Internet of Things (IoT), to demonstrate the real-world impact of these algorithms. This work discusses the technical challenges of applying these models at scale and presents future research directions aimed at overcoming these barriers. The findings offer valuable insights for academia, industry, and policy makers by showing how advanced data science can enhance big data optimization, drive innovation, and optimize decision-making in dynamic environments.