Edge Computing represents a paradigm shift in data processing, bringing computational power closer to the source of data generation. This paper explores the transformative role of edge computing in data science, particularly its impact on real-time analytics. In an era dominated by the Internet of Things (IoT) and massive data generation, edge computing offers an efficient alternative to traditional, centralized cloud computing by processing data locally, thereby reducing latency and bandwidth usage. This is particularly crucial in applications where real-time data processing and decision-making are essential, such as in autonomous vehicles, healthcare monitoring systems, and smart cities. Edge computing enhances privacy and security, as sensitive data can be processed locally without being transmitted over the network. This paper delves into the architectural aspects of edge computing, its integration with data science, and the challenges and opportunities it presents. The potential of edge computing to revolutionize data processing paradigms in various industries, paving the way for more efficient, secure, and fast data analytics, is thoroughly examined.