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The scope of this session encompasses theoretical foundations, system architectures, tools, and real-world applications that address the challenges of processing and analyzing large-scale data in distributed environments. It aims to explore interdisciplinary research at the intersection of data science, cloud computing, and distributed systems, including the development of scalable machine learning models, real-time data processing frameworks, and cloud-native analytics pipelines.

There exists a noticeable gap between theoretical advancements in data science and their practical deployment in large-scale distributed infrastructures. Bridging this gap requires interdisciplinary research that combines expertise in data science, distributed systems, cloud computing, and software engineering. There is also a pressing need for standardized frameworks, benchmarking methodologies, and best practices to evaluate and deploy scalable data science solutions effectively.