| | | | | | | | 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)
|
| |
|
|
|
| | | |
|
|
|
©
2006-2010
Six Sigma Products Group, Inc.
|
|
|
|
|
|
|
|