Syllabus

Short Course Description

Graph Analytics is a rapidly emerging subfield of data science that stands at the confluence of network theory, statistical analysis and business intelligence. In an increasingly networked society, social linkages affect all aspects of our daily life. Businesses, too, are embedded in complex economic networks which play an important role in influencing the profitability of organizations. The past decade has witnessed a surge in availability of data from various kinds of networks. However, traditional data analysis methods are often insufficient to uncover the underlying patterns of relationships in these networks. The objective of this course is to introduce students to the field of graph analytics through a combination of network fundamentals, hands-on experience with computational techniques and datasets, and exposure to social and business applications. Students will learn about the fundamentals of social networks and graph theory, and will learn how to leverage this knowledge to analyze real-world networks as well as business cases from a range of emerging contexts like digital marketing, fintech, or edtech. Through data-driven assignments and term projects, students will also be exposed to hands-on tools for network analysis and visualization, and have the opportunity to learn how to integrate network analysis tools and packages into existing analytics pipelines. While prior coding experience will be an advantage, it is not a must.