The increasing complexity of modern nuclear research has generated unprecedented volumes of scientific data originating from reactor instrumentation, radiation detection systems, particle experiments, computational simulations and materials characterization. These developments have transformed nuclear science into a computationally intensive discipline in which data management, reproducible analysis and intelligent decision support have become fundamental components of scientific investigation. Although high-performance computing has traditionally occupied a central role in nuclear engineering, comparatively less conceptual attention has been devoted to the growing influence of data science environments built around Python programming and Jupyter notebooks. These technologies have evolved beyond educational tools to become integrated scientific platforms supporting computational experimentation, collaborative research and reproducible analytical workflows. This conceptual preprint introduces the concept of Computational Nuclear Analytics (CNA), defined as the integrated scientific capability through which data science methodologies, Python-based computational ecosystems and interactive research environments continuously acquire, process, analyse, visualize and operationalize nuclear data throughout the research lifecycle. Unlike conventional perspectives that emphasize isolated analytical software or simulation codes, the proposed framework conceptualizes Python and Jupyter as collaborative scientific infrastructures that unify computational modelling, statistical analysis, machine learning, data visualization and research documentation within a single reproducible environment. The manuscript proposes a theoretical framework explaining how data acquisition, computational workflows, analytical intelligence, interactive scientific computing and collaborative knowledge management collectively support modern nuclear research operations. The discussion further explores the role of Python libraries, notebook-driven experimentation, artificial intelligence integration and cloud-based computational environments in advancing reactor analysis, radiation monitoring, nuclear materials research and scientific reproducibility.
Anshuman Sinha (Sat,) studied this question.