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Data science at scale has emerged as a foundational driver of innovation across scientific, industrial, and societal domains. With the exponential expansion of big data from sensors, enterprise systems, cloud platforms, mobile devices, and scientific instrumentation, organizations require high-performance computational architectures and advanced machine learning strategies to transform raw data into actionable insights. This paper explores scalable data engineering pipelines, distributed data architectures, cloud–edge ecosystems, and large-scale learning models that enable the analysis of massive and heterogeneous datasets. It highlights the role of deep learning, graph analytics, automated machine learning (AutoML), and multimodal modeling in generating real-world impact. The study further examines multidisciplinary applications including healthcare, smart mobility, environmental science, industrial systems, finance, and social computing. Challenges involving data governance, bias, reproducibility, energy efficiency, and model interpretability are discussed, alongside future research pathways. Overall, data science at scale provides the backbone for developing intelligent systems capable of addressing complex real-world problems in an era defined by overwhelming data complexity and cross-domain integration.