In today’s interconnected world, understanding complex systems and their underlying structures is key for extracting meaningful insights from data. Network science provides a powerful framework for analyzing and modeling a wide range of systems, from social networks to biological pathways to digital infrastructure. The teaching demonstration aims to introduce students to the fundamental concepts of network science along with some of their applications. I will specifically explain the basics of networks, network properties, and introduce Exponential Random Graph Models (ERGMs). The second portion of the talk focuses on my research work which involves applying network models to sociological data. In particular, my current research focuses on evaluating respondent-driven sampling (RDS) under dynamic network conditions. I give a brief overview of my current work along with some future research goals.