From Ancient Records to Agentic Intelligence: A Comprehensive Historical Review of Data Science and Its Role in Intelligent Internet of Things Analytics

Abstract

Since the era of the Mesopotamia between 2900 BCE - 2350 BCE, the goals of “data science-like” approaches have always revolved around collecting fragments of information and trying to make insights out of such information. Data Science was not clearly termed as Data Science from the onset. Moreover, there exists several questions about Data Science just been a polished name for Statistics. Therefore, there is a need to look carefully at how Data Science has evolved over the years and where it is currently. The study defines Data Science as a multidisciplinary field that integrates statistics, mathematics, computer science, domain expertise, and ethics. This is important because, it will give Data Scientists and the Data Science enthusiasts a clear understanding of this discipline.  In addition, the paper demonstrates how Data Science transforms massive Internet of Things (IoT) data streams into actionable insights through predictive analytics, anomaly detection, explainable artificial intelligence, cybersecurity analytics, digital twins, and intelligent decision support. The findings reveal that the evolution of Data Science reflects a transition from descriptive statistical analysis to autonomous, explainable, and trustworthy intelligent systems capable of supporting real-time decision-making across diverse domains. This work provides a unified historical perspective, highlights emerging research directions, and offers a valuable reference for researchers, educators, and practitioners seeking to understand the past, present, and future direction of Data Science.