The Implications of Data Science in Contemporary Tamilnadu Archaeological Interpretation

Abstract

A transformational shift is taking place in how Tamilnadu archaeology documents, analyzes, and interprets archaeological evidence by integrating data science. A growing number of data-driven approaches are providing archaeologists with tools to improve beyond traditional descriptive analysis with the availability of larger datasets, from excavation records, to inscriptions, to artefacts, to geo-spatial surveys, to remote sensing. The analysis of settlement patterns, trade networks, cultural transformations, and site discoveries can be aided by techniques such as supervised and unsupervised learning, statistical modeling, big data analytics, and digital preservation strategies. Specifically, machine learning and digital visualization have been applied to Tamilnadu's rich archaeological context, including Keeladi and other Sangam-age settlements, in order to classify inscriptions, date artefacts, and reconstruct past landscapes. Archaeology, heritage management, and public history are linked through data science, thereby fostering interdisciplinary collaboration. Using these methods, the author explores the implications for reshaping interpretations, ensuring cultural preservation, and opening up new avenues for future archaeological research in Tamilnadu. By combining data science with traditional archaeological expertise, Tamilnadu's past can be understood holistically, evidence-based, and transparently in the digital age.