Predictive Analytics & Data Mining Strategic Implementation

    

Summary:
Data mining is essentially a discovery process a process riddled with common yet elusive strategic pitfalls. Project failure is rarely due to poor model development. Rather, data mining projects often fall short of their potential due to flawed or overlooked assessment, business understanding, project definition and strategic planning specifically for information discovery.If you are looking for an intensive vendor-neutral and non-promotional introduction to data mining best practices and an approach to predictive analytics which is critical to modeling success, then this course is designed for you. There are no prerequisites for this course. However, participants will benefit by reviewing the CRISP-DM guide ahead of the training."Predictive Analytics & Data Mining: Strategic Implementation" offers a concentrated presentation of capabilities, limitations, risks, rewards, use cases, best practices, strategy and lifecycle management. 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 is no longer needed to succeed in predictive analytics. Live working sessions reveal real-world obstacles and breakthroughs from which to interpret, learn and apply.

Duration:
3 Days/Lecture & Lab

Audience:
-IT/IS Executives and Managers -Line-Of-Business Executives and Functional Managers-Technology Planners-Consultants

Topics:

• Introduction
• Use Case Workshop #1
• Use Case Workshop #2
• Extended Modeling Topics
• Wrap-Up and Next Steps

Last Update: May 23, 2012