Data-driven methods is an academic and methodological approach that emphasizes the systematic analysis of empirical data to understand phenomena, build models, and make predictions. This concept investigates complex systems, patterns, and relationships by applying computational and statistical techniques (such as machine learning, data mining, and statistical modeling) to identify structure and insights directly from datasets, often with reduced dependence on strong *a priori* theoretical frameworks. Its key characteristics include reliance on large volumes of data, algorithmic processing, and the iterative refinement of models based on data evidence. The significance of data-driven methods lies in their capacity to enable the discovery of non-obvious correlations, automate complex tasks, generate evidence-based hypotheses, and inform decision-making across a wide spectrum of scientific, engineering, social, and commercial disciplines.