This 2-day course dives into the data mining process at the tactical level. Participants will observe live demonstrations of machine learning methods and computer-aided pattern discovery techniques for extracting and interpreting complex patterns and relationships from large volumes of data. Attendees then participate in work-along labs that build upon an overall project.
The intent of this course is to present participants with a roadmap for data certification and preparation, model-building techniques, how various methods and tools apply to different kinds of data intensive problems, and how to overcome limitations that cause the majority of predictive models to under-perform. It does not restrict or skew the presentation of data mining methods through a single product. Rather, the Model Development course gives broad consideration of the capabilities and limitations of all resources from a vendor-neutral perspective.
Live modeling demonstrations projected from the presenter’s machine will precede the follow-along lab exercises. The exercises run from a design produced in the Planning phase. Participants will directly experience the natural messiness of data mining to discover what really works, as well as what doesn’t and why.
- Understand the purpose, function and impact of the 6-Phase Model Development Methodology
- Proceed through the general implementation of the two tactical phases: Prepare and Build
- Realize that model-building for actionable production need not be highly technical or complex
- Construct a valid data set and transform data for superior model performance
- Describe each of the five steps for preparing raw data for predictive analysis
- Select appropriate methods for each of the Four Core Analytic Project Types
- Assess the degree to which a model meets a predefined performance objective
- Leave with resources, contacts and actionable plans to substantially increase your analytic capabilities while minimizing dead ends
Who Should Attend
- Data Scientists: who desire to extend their analytical toolbox and underscore the scientist aspect of the role with formal process and hands-on methodological practice
- Functional Analytic Practitioners: Customer Relationship Managers, Risk Analysts, Business Forecasters, Statistical Analysts, Social Media and Web Data Analysts, Fraud Detection Analysts, Audit Selection Managers, Direct Marketing Analysts, Medical Diagnostic Analysts, Market Timers
- Big Data Analysts: who are under increasing pressure to transform their deluge of data from a liability to an asset
- Project Leaders: who desire to have a more detailed understanding of predictive modeling methods and techniques to better manage and interact with their practitioners
- Business Analysts: who must develop and interpret the models, communicate the results and make actionable recommendations
- IT Professionals: who wish to gain a better understanding of the data preparation, analytics and analytic sandbox development requirements to more fully support the growing demand for analytic IT support
Please contact us for a detailed course outline.