DATA MINING TOOLS FOR THE SIX SIGMA PRACTITIONER
Training materials: XL Miner software (trial version); Data Mining Tools student participant guide. Recommended (but not required) texts: The Elements of Statistical Learning by T. Hastie, R. Tibshirani, J. H. Friedman; Principles of Data Mining (Adaptive Computation and Machine Learning) by David J. Hand, Heikki Mannila, Padhraic Smyth.
Course agenda:
I. OVERVIEW OF DATA MINING
Definitions and tasks
Steps in the data mining process
Facts versus myths
Data mining applications
II. PREPATORY STEPS
Data preparation (missing data, outlier analysis, data types)
Data visualization
Data dictionaries
III. BACKGROUND ON MODELING
The curse of dimensionality
Notation and terms
Bias-variance tradeoff
Control of the bias-variance tradeoff
Error functions
IV. TRADITIONAL MODELS
Linear regression procedures
Logistic regression
Discriminant analysis
Nearest neighbors
Clustering algorithms
V. MODERN MODELS
Classification and Regression Trees
Neural networks
Bump hunting
Association rules
Evaluating and combining models (Bagging, boosting, MART)
Survey of recent developments (as time permits)
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