Network Analytics

3 Credits · Level: Graduate

The design and analysis of networks to represent interactions between and within data is a quickly emerging discipline of significant importance. Data Analytics combines graph theory, optimization, data science, data visualization, community and cluster analysis, and more. Topics in this course will help answer intriguing questions such as, “How can we make sense of large, highly-associated data sets, ranging from social networks to the smart power grid?” or “Which models are more accurate for predicting popularity on Twitter?” or “How can we estimate the spread of a contagion or of information?” The course will begin with a discussion of applications, specifically to data science and analytics. From there, a formal framework for analysis of graphs and trees will be introduced. This will include graph theory and representation, optimization, and graph-based algorithms. Next packages in Python and/or R will be investigated for the purposes of exploring and visualizing data that contain relationships. These packages will then be used to model and analyze complex data sets for the purposes of community detection, path analysis, influencer assessment, logistics analytics, contagion or information spread (such as rumor spreading), web page ranking, and more. Examples of data science applications are provided with real-world data sets including social network data, web-based data, attributed data, flow data, biological data, and more.