Business Knowledge Series course
Presented by Bart Baesens, Ph.D. Professor at KU Leuven (Belgium), and lecturer at the University of Southampton (UK)
This course discusses how to leverage social networks for analytical purposes. Obviously, when we say "social networks," many people think of Facebook, Twitter, Google+, LinkedIn, and so on. These are all examples of networks that connect people using either friendship or professional relationships. In this course, we zoom out and provide a much more general definition of a social network. In fact, we define a social network as a network of nodes that are connected using edges. Both nodes and edges can be defined in various ways, depending on the setting. This course starts by describing the basic concepts of social networks and their applications in marketing, risk, fraud, and HR. It then defines various social metrics and illustrates how they can be used for community mining. The course also discusses how social networks can be used for predictive analytics. The course provides a sound mix of both theoretical and technical insights, as well as practical implementation details, and is illustrated by several real-life cases. The instructor extensively reports on both his research and consulting experience in the field. References to background material such as selected papers, tutorials, and guidelines are also provided.
Learn how to
- describe the basic concepts of social networks and their applications
- identify the main social network metrics
- identify the main characteristics of community mining
- identify the main characteristics of social-network-based predictive analytics
- identify the main characteristics of bipartite graphs.
Who should attend
Data scientists, business analysts, senior data analysts, quantitative analysts, data miners, senior CRM analysts, marketing analysts, risk analysts, analytical model developers, online marketers, and marketing modelers in the following industries: banking and finance, insurance, Telco, online retailers, advertising, Pharma
Before attending this course, you should
- have a basic background in descriptive and predictive statistics
- know how to preprocess data and develop predictive models using techniques such as linear regression, logistic regression, and decision trees
- be familiar with hierarchical clustering and non-hierarchical clustering, such as k-means clustering.
This course addresses Base SAS, SAS Enterprise Miner software.
