Web Analytics

Organizations, both commercial and community, can benefit from deep analysis of their website interactions and mobile data. Social networks have also become a source of information for companies; search engines are an important referral mechanism. Popular social networks and other online communities provide rich sources of user information and (inter-) actions through their application programming interfaces. This data can help to identify a number of individual user preferences and behaviors, as well as fundamental relationships within the community. Search engines use algorithms to rank sites. Students will learn how to analyze social network data for types of networks, the fundamental calculations used in social networks (e.g., centrality, cohesion, affiliations, and clustering coefficient) as well as network structures and roles. Beyond social network data, students will learn about important concepts of analyzing website traffic such as click streams, referrals, keywords, page views, and drop rates. The course will touch on the fundamentals of search algorithms and search engine optimization. To provide a basic context for understanding these online user and community behaviors, students will learn about relevant social science theories such as homophily, social capital, trust, and motivations as well as business and social use contexts. In addition, this course will address ethical and privacy issues as they relate to information on the internet and social responsibility.

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