Predictive Analytics: An Intensive Overview of Strategy, Application, and Best Practices for Data Mining

    Prerequisite:   None

     

    Two-Day Course

    Course Preview | Course Outline

    You Will Learn

    • Basic principles and terminology for predictive analytics
    • Who is utilizing predictive analytics, and why
    • Common project pitfalls and how to avoid them
    • Project deployment, performance, and maintenance issues
    • How to define business objectives for a decision support system
    • 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, ecommerce 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

    This two-day course offers a broad-brushed introduction to data mining terminology, methods, resources and business issues. Those in attendance will learn about various methods of predictive analytics, competitive advantages, and common pitfalls that often cause data mining projects to fall short of their potential.

    Traditionally, organizations use data tactically - to manage operations. For competitive edge, leading organizations use data strategically - to expand the business, to improve profitability, to reduce costs, and to market more effectively. The mining of data for predictive indicators creates information assets that an organization can leverage to achieve these strategic objectives.

    Predictive analytics is a new component in an enterprise's decision support system (DSS) architecture. It complements and interlocks with other DSS capabilities such as query and reporting, on-line analytical processing (OLAP), data visualization, and traditional statistical analysis. These other DSS technologies are generally retrospective.

    The predictive aspect of data mining may be defined as "the data-driven discovery and modeling of hidden patterns in large volumes of data." Predictive analytics differs from the retrospective technologies above because it produces models -- models that capture and represent hidden patterns and interactions in the data. Via data mining, a user can discover patterns and build models automatically, without knowing exactly what s/he's looking for.

    The resulting models are both descriptive and prospective. They address why things happened and what is likely to happen next. A user can pose "what-if" questions to a data-mining model that cannot be queried directly from the database or warehouse. Examples include: "What is the expected lifetime value of every customer account," "Which customers are likely to open a money market account," or "Will this customer cancel our service if we introduce fees?"

    If you desire to make predictive analytics an integral part of your organization's decision support system, this course is for you. Among the benefits of attending this course, you’ll be able to:

    • Make anticipatory business decisions based on information hidden in your data
    • Develop a strong vocabulary and understanding of data mining terminology
    • Communicate with confidence among your developers and consultants
    • Plan and manage your data mining projects effectively from the start
    • Leave with resources, contacts and actionable plans to substantially reduce your project preparation time, costs and risks

    This course is a vendor-neutral presentation of data mining topics and its role in enterprise decision support. For over fifteen years, the instructor has been deeply involved with the development and deployment of real-world data mining solutions.

    Leading products will be used to illustrate and compare methods. Results will be drawn from actual data mining applications and interpreted in the context of business impact. Attendees will depart with a binder full of slides, supporting notes and a valuable index of data mining resources.

    Those seeking to drill down into the methodology of predictive analytics may also wish to participate in Data Mining Techniques, Tools, and Tactics as an additional two days following this course.