Redefining Data Science. A Dynamic, Context-Aware, and Self-Learning Framework

Traditional data science methods, which rely mostly on static models and fixed analytical approaches, struggle with the complexities of today's fast-changing digital world. This research presents Dynamic Data Science (DDS), a new framework that brings together temporal data flows, contextual understanding, adaptive feedback, and error handling in a unified system. Through empirical analysis, mathematical modeling, and simulation experiments, we show that DDS delivers better predictive performance and real-time decision capabilities than existing methods. Our results demonstrate improvements of 23-47% across various application areas, offering a new perspective on data science practice.