TDWI World Conference Fall November 7-12, 2010

CBIP

Prerequisite: None

Tony Rathburn

Typically, organizations approach analytics from a technology perspective. Analytical tools receive a great deal of attention for their features and capabilities. This course illustrates the importance of an appropriate conceptual approach to predictive analytics, and the critical role of data handling on performance. Unlike OLAP, predictive analytics focuses on group behavior, probabilistic expectations, and low-incidence/high-impact occurrences.

You Will Learn

• Principles and terminology for predictive analytics
• How to define business objectives for a predictive analytics model
• Common project performance and maintenance issues
• Strengths and capabilities of various types of data
• Data representation and transformation techniques to enhance performance
• Experimental design for predictive analytics
• Conceptual foundation to common predictive analytics technologies

Geared To

• Line-of-business executives and functional managers: risk managers, customer relationship managers, business forecasters, inventory flow analysts, financial forecasters, direct marketing analysts, medical diagnostic analysts, e-commerce company executives; technology planners who survey emerging technologies to prioritize corporate investment; consultants whose competitive environment is intensifying and whose success requires competency with data mining and related emerging information technologies