Predictive Analytics: Low-Risk/High-Impact Implementation in Doha Qatar

This intensive three-day hands-on methodical workshop event offers a comprehensive project-level orientation to predictive analytics solutions — from project assessment and preparation to industry standard process, case demonstrations, pragmatic exercises, and model lifecycle management.

If you are looking for an intensive vendor-neutral and non-promotional introduction to data mining best practices and an approach to predictive analytics that is critical to modeling success, then this course is designed for you. Those in attendance will actively step through the industry standard process for data mining and realize why an advanced degree in statistics, mathematics, or computer science not required to establish a productive internal predictive analytics practice. Live working sessions reveal real-world obstacles and breakthroughs from which to interpret, learn, and apply.

*This course can also be offered as 2 days with instructor demonstrations only. Two day option includes instructor lecture and demonstration only (without hands-on).

You Will Learn

Process, principles and terminology for predictive analytics
Who is utilizing predictive analytics, and why
Common project pitfalls and how to avoid them
Project performance and maintenance issues
How to define business objectives for a decision-support system
Hands-on exposure to the natural messiness of data mining
How to get started

Geared To

IT/IS executives and managers: CIOs, CKOs, CTOs, functional officers, technical directors, and project managers; 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 in order to prioritize corporate investment; consultants whose competitive environment is intensifying and whose success requires competency with data mining and related emerging information technologies