Data Mining Application Workshop

    Prerequisite:   Predictive Analytics (recommended) Data Mining Techniques, Tools, and Tactics (required)

     

    One-Day Workshop


    Course Outline

    You Will Learn

    • Hands-on experience through the data mining process via a staged progression of exercises using application data
    • First-hand vendor-neutral exposure to various data mining tools
    • Real-world perspective of data preparation for data mining, model optimization and results interpretation
    • Cross-learning through team exercise comparisons to reveal what worked, what didn't, and why
    • Development processes

    Geared To

    • DATA MINING TECHNIQUES, TOOLS, AND TACTICS PARTICIPANTS with an interest in applying first-hand the methods and techniques presented and illustrated in the course
    • DATA MINING PRACTITIONERS who wish to expand their skills and analytical toolbox as well as hone proficiencies in maneuvering elusive data mining obstacles that stand in the way of superior model accuracy
    • BUSINESS ANALYSTS who must develop and interpret models, communicate the results and make actionable recommendations
    • FUNCTIONAL ANALYSTS: Customer Relationship Managers, Risk Analysts, Business Forecasters, Statistical Analysts, Inventory Flow Analysts, Direct Marketing Analysts, Medical Diagnostic Analysts, Market Timers, e-commerce System Architects and Web Data Analysts

    Throughout the workshop, the CRISP-DM model will be used to guide participants through the steps of the data mining process, and the attendees themselves will complete the entire data mining process during the workshop by solving simple data mining problems through a staged progression.

    In the morning, participants will begin with a database containing multiple tables of information. Participants will each have a networked computer and may choose to work on exercises in pairs or individually. Attendees will determine which business questions will be considered, how they will be addressed using data mining, and how the data will be prepared for data mining. A divide-and-conquer approach will be used to carry out the “data understanding” and “data preprocessing” steps as participants work alone or in pairs to prepare the data for data mining.

    In the afternoon, regression, decision tree, and neural network models will be created, and performance assessed. Participants may optimize these models by using advanced algorithm options, and report model performance on held-out data and summaries of key variables used in the models. Data preprocessing will be re-applied if models do not meet performance requirements. The team will determine which model best addresses the business question, and score the model on validation data.

    Throughout the day, emphasis will be placed not only on the data mining process from a technical perspective, but also how to interpret, explain and apply results that have been discovered during the process.

    Among the benefits of attending this course:

    • Deeper understanding of Data Mining Techniques, Tools, and Tactics through team exercises
    • Hands-on experience through the data mining process via a staged progression of exercises using application data
    • First-hand vendor-neutral exposure to various data mining tools
    • Real-world perspective of data preparation for data mining, model optimization and results interpretation
    • Cross-learning through team exercise comparisons to reveal what worked, what didn't, and why

    Unlike any other application-oriented offering on the market, this course offers a structured approach to team-oriented data mining exercises in a lab environment. Because trainer is a practitioner and not a tools vendor, participants enjoy a balanced, broad, and non-promotional perspective of data mining.