From Statistical Analysis to Deep Learning

Abstract

This chapter explores the shift from traditional statistical analysis to advanced deep learning in quantitative decision-making. It argues for integrating classical paradigms—such as descriptive and inferential statistics conducted in tools like SPSS—with modern computational techniques to address complex, large-scale challenges. The discussion highlights the pivotal role of big data platforms, including Hadoop and Spark, in enabling real-time analytics and distinguishes core machine learning algorithms from deep neural architectures designed for unstructured data. Interdisciplinary case studies in healthcare, finance, and engineering demonstrate the practical synergy of these approaches. By embracing multi-paradigm strategies, the chapter offers insights into building transparent, scalable, and effective decision-support systems across diverse domains. It concludes by addressing challenges of ethics and governance and by pointing to future directions such as explainable AI and federated learning, emphasising that robust decision-making depends on a hybridised analytical framework.

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