In an era where social connections drive everything from viral trends on TikTok to global collaborations on Wikipedia, understanding the structure and dynamics of social networks is essential for understanding complex social phenomena. This course aims to equip students with cutting-edge computational tools and theoretical frameworks to analyze how social networks shape human behavior, information flow, and societal outcomes. By combining social and network science theories, complex systems modeling techniques, and computational social science methodologies, students will learn to model and predict complex social dynamics on real-world social networks ranging from social networking sites (e.g., X, Facebook, Instagram, LinkedIn), media and content communities (e.g., YouTube, Reddit, RedNote), crowd collaboration platforms (e.g., Wikipedia, Github), sharing economy websites (e.g., Airbnb, Uber), co-authorship networks, to human mobility networks and online dating apps. Through hands-on assignments and final projects where students will apply advanced computational methods to analyze empirical network data and social phenomena, this course empowers students to conduct rigorous and impactful research that bridges academia and industry, preparing them for careers in data analytics, platform management, policy advising, tech innovation, and beyond.