Analytical Approaches to Solving Problems in Communications and Media
Business Knowledge Series course
Presented by Carlos Andre Reis Pinheiro, D.Sc., Principal Data Scientist, SAS
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.Learn how to
Who should attendBusiness analysts, statisticians, mathematicians, computer scientists, data analysts, data scientists, quantitative analysts, data miners, marketing analysts, risk and fraud analysts, analytical model developers, and marketing modelers
You will benefit most from this course if you are already superficially familiar with analytics.