Data Mining: Level III

PT3962
Summary
The tactical and methodological for Level II is put into action through team-driven, live data mining exercises. Review sessions then reveal real-world obstacles, breakthroughs and results from which to interpret, learn and apply. Through hands-on workshop data mining methods and techniques presented in Level II are applied to real-world data.
Prerequisites
Students should have equivalent experience to Data Mining I & II.
Duration
1 Day/Lecture & Lab
Audience
Data Mining Practitioners who wish to expand their skills and analytical toolbox as well as hone proficiencies in maneuvering elusive obstacles that impede superior model accuracy, Business Analysts, who must develop and interpret models, communicate the results and make actionable recommendations and Functional Analysts: Customer Relationship Managers, Risk Analysts, Statistical Analysts, Business Forecasters, Inventory Flow Analysts, Direct Marketing Analysts, Medical Diagnostic Analysts, Market Timers, e-commerce System Architects and Web Data Analysts.
Topics
Business Understanding & Responsibilities::Prioritized Business Questions and related Data::Mining Approaches::Summary Statistics::Visualization::Outlier Analysis::Missing Data Analysis::Create Mini-Report::Data Assessment Summary::Assign Data Preprocessing Responsibilities::Result Summaries of Available Modeling Data, and Recommendations::Correct Data Problems::Create Features::Data Preprocessing Summary::Join Data Modified During Breakout Sessions::Determine Sampling Strategy::Result Single Modeling Dataset::Build Decision Trees, Regression, and Neural Networks::Assess Results::Rebuild Models, Changing Modeling Parameters::Score Models on Testing Data::Rank Variable Importance to Models::Create Mini-Report::Summarize Modeling Results::Which Modeling Techniques Worked::Determine Needs for More Data Pre-Processing and Modeling::Assign Responsibilities::Create Final Models::Score Models::Select Final Model to Use::Explain Reason for Selection

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