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Analytical Approaches to Solving Problems in Communications and Media
The Communications and Media market is highly competitive and becomes more complex as the number of products and services increases. The dynamic market requires companies to be fast and precise in their business actions. A solid analytical approach plays an important role in such an environment. This course presents, with a practical and high-level perspective, a set of techniques to analyze communications and media data. Using comprehensive and real-world examples, this course examines the customer life cycle and the analytical models that can be deployed in each phase of it. From customer acquisition to churn, including fraud detection and risk assessment, you learn a variety of analytical techniques that address business problems in real market scenarios.
Supervised models such as linear and logistic regressions, decision trees, and artificial neural networks are presented as examples of analytical approaches that can be used to predict and classify a wide range of business events, such as churn, bad debt, fraud, risk, insolvency, collecting, cross-sell, and up-sell. Model assessment, ensemble models, and two-stage models are also covered. Unsupervised models such as clustering (hierarchical, k-means, and self-organizing maps), association and sequence rules, link and path analysis, text mining, and social network analysis are covered as analytical methods to raise business knowledge and to recognize customer patterns. Optimization models are covered as analytical methods to improve operational performance. This course focuses on practical solutions that provide a problem-solving framework.
The examples in this course are related to the Communications and Media market. However, students from other industries are welcome and will experience an analytical approach for business problem solving.
This course familiarizes you with analytical problem-solving approaches. This is not a programming class and includes no hands-on use of software.
Exploratory Analysis for Large and Complex Problems Using SAS® Enterprise Miner™
This course is intended for analysts working with virtually any type of exploratory data analysis problem. Discovery in a complicated data set is one of the analyst's toughest problems. The course covers this discovery process using many real-world problems. There is a focus on fraud detection, with the recognition that the core principles of modeling to solve fraud detection are the basis of all exploratory data analysis. Analytical methods used in the course include decision trees, logistic regression, neural networks, link analysis, and social network analysis. In addition, analysts receive practical advice on presenting complex findings to their audience.
SAS® Fraud Framework: Administering Social Network Analysis Virtual Lab
This course provides you with the knowledge and skills needed to implement the investigator interface of SAS Social Network Analysis 6.2M4, a component of SAS Fraud Framework. The course addresses the use of eight stored processes for populating the interface, and it provides an understanding of the tasks that must be completed before implementation. This course does not address installing SAS Social Network Analysis Server.
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SAS® Fraud Framework: Investigating Alerts Using Social Network Analysis
This course provides investigators with the knowledge and skills needed to use the Social Network Analysis interface, a component of SAS Fraud Framework. The course takes investigators through the process of accessing alerts associated with fraud and investigating alerts by analyzing the data related to the alerted entity. In addition, investigators learn how to explore relationships in the social network diagram and take action on alerts.
Social Network Analysis for Business Applications
Go beyond the traditional clustering and predictive models to identify patterns in your business data. Social network analysis describes customers' behavior, but not in terms of their individual attributes. Rather than basing models on static individual profiles, social network analysis depicts behavior in terms of how individuals relate to each other. In practical terms this approach highlights connections between individuals and organizations and how important they might be in viral effect throughout communities and particular groups. For business purposes, social network analysis can be employed to avoid churn, diffuse products and services, and detect fraud and abuse, among many other applications. This course shows you how to build networks from raw data and presents different approaches for analyzing your customers, focusing on their relationships and connections within the network.
Based on the recognition of customers', or organizations', roles within communities or special groups, you can improve business performance and better understand how your customers are using products and services. In addition to the network analysis approach to linking distinct entities, playing different roles on particular connections, this course also shows you a set of network optimization algorithms that you can use to solve a variety of complex business problems. Methods such as minimum-cost network flow, shortest path, linear assignment, minimum spanning tree, eigenvector, and transitive closure are presented in a business perspective for problem solving.
This course contains practical examples based on SAS Social Network Analysis Server and PROC OPTGRAPH.