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

• recognize major trends in technology and consuming in the media and communications market
• recognize the customer life cycle and a variety of analytical approaches and business actions to be deployed in each phase of it
• identify the advanced analytics framework and the analytical applications to be used in order to improve business and operational performances
• determine whether supervised models can be used for classifying and estimating business events
• determine whether unsupervised models can be used for recognizing customer patterns and extracting useful business knowledge from structured and unstructured data
• solve problems by applying analytical approaches based on supervised models and unsupervised models, including regression, decision trees, neural networks, clustering, association and sequence association rules, path analysis, text mining, social network analysis, and optimization models.

Who should attend

Business 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

Formats available	Standard duration
Classroom:	2.0 days

You will benefit most from this course if you are already superficially familiar with analytics.

Introduction

• market overview and trends in communications
• customer life cycle
• advanced analytics framework

Supervised Models

• concepts, including linear and logistic regressions, decision trees, and artificial neural networks
• model assessment
• ensemble models
• two-stage models
• case studies

Unsupervised Models

• clustering, including hierarchical, k-means, and self-organizing maps
• association rules
• sequence association rules
• link analysis
• path analysis
• case studies

Text Mining

• case studies

Social Network Analysis

• case studies

Optimization Models

• case studies