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The "Model Development" 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.This course is designed to be taken independently, yet is part of a larger course series that covers a 6-Phase Model Development Methodology for low-risk, high-impact projects. The scope of this course extends to the second and third phases: Prepare and Build. You need not be an experienced statistician or mathematician to track well in this course - though traditional quantitative experts will benefit from the strategic referencing to the Plan phase and the pragmatic mind-shift required in this event. The machine learning algorithms are covered from a functional perspective. Modern software does a great job of handling the mathematical complexity. Those seeking a deep drill-down into the mathematical or theoretical underpinnings of predictive analytics algorithms should refer to other available academic offerings. This vendor-neutral course utilizes popular commercial and open-source analytic tools in its demonstrations. The tools are used to illustrate the methods conveyed, but not to showcase the products. If you desire an intensive tactical orientation to predictive modeling methods, techniques and practice, then this event is designed for you.
While this course is designed to be taken independently, it is important to understand its place and function within the overall Predictive Analytics & Data Mining Course Series. Registrants will be required to view a four-hour asynchronous "Core Concepts" orientation prior to attending this event. Access details for the Core Concepts orientation will be shared with participants prior to the start of the course. Prior education or experience in data analytics or statistics is helpful, but not required. Participants need only supply a laptop computer with Microsoft Excel. Instructions on how to download lab data and any analytic tools will be provided in the preparatory email. The instructor can assist participants with any preparation during breaks, and before or after class.
2 Days/Lecture & Lab
Data Scientists: who desire to extend their analytical toolbox and underscore the scientist aspect of the role with formal process and hands-on methodological practiceFunctional 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 TimersBig 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 practitionersBusiness Analysts: who must develop and interpret the models, communicate the results and make actionable recommendationsIT 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 supportAnyone Overwhelmed with Data and Starved for Actionable Insights
Introduction::Crisp-DM Methodology Parts 3, 4, 5::Modeling (CRISP 4)::Evaluation (CRISP 5)::Wrap Up and Parting Thoughts