This course treats community as its central theme, and focuses on the use of data science to understand how individuals interact with each other, and how organizations and firms may gain insights and design policies that better serve individuals across various communities. The class talks about community in general, but sometimes uses marketing examples to illustrate ideas. The concepts and tools are applicable to any community, or any context where individuals are networked and interact with each other. In particular, we will explore the nature of marketplace networks, investigate how they are formed and maintained, and study the kinds of economic behaviors that result from different network structures. Furthermore, to understand how networked individuals influence one another, we will study the conversations by which they communicate. We will focus on the tools used by data scientists to understand consumer sentiment, demand, etc., and make informed recommendations. In addition, the course will also include a foundational review of key modeling tools that are needed to support these goals. The course is designed as both an introduction to key concepts and tools that data scientists need to understand social activity in a networked context. This course is suitable for policy makers interested in data-driven strategies. After taking this course, student should be able to: investigate networked data through visualization, understand models of network formation, understand how information flows in network, measure network effects, extract useful information from textual communications, including sentiment analysis, and develop appropriate policy recommendations based on all of the above.