Data Mining Techniques: Theory and Practice

Duration: 3.0 days

Explore the inner workings of data mining techniques and how to make them work for you. Students are taken through all the steps of a data mining project, beginning with problem definition and data selection, and continuing through data exploration, data transformation, sampling, portioning, modeling, and assessment.

Learn how to

• use a data mining methodology
• build and use decision trees and neural networks for modeling and scoring
• use survival analysis and create survival curves.

Who should attend: Business analysts, their managers, and statisticians

Prerequisites

No prior knowledge of statistical or data mining tools is required.

Course Contents

Introduction to Data Mining

• what is data mining?
• directed and undirected data mining
• models
• profiling and prediction

Data Mining Methodology

• why have a methodology?
• how data miners can inadvertently learn things that are not true
• translating business problems into data mining problems
• the importance of model stability
• finding the right input variables
• sampling to create balanced model sets
• partitioning to create training, validation, and test sets
• data preparation
• model assessment

Data Exploration

• developing intuition about data
• data structure
• data types
• data values
• exploring distributions
• summary statistics
• histograms
• using SAS Enterprise Miner for data exploration

Statistics and Regression

• the null hypothesis
• statistical significance
• confidence bounds
• variance and standard deviation
• standardized values
• correlation
• linear regression
• logistic regression
• using SAS Enterprise Miner to build regression models

Decision Trees

• decision trees as data exploration and classification tools
• decision trees for modeling and scoring
• decision trees for variable selection
• alternate representations of decision trees
• algorithms used to build decision trees
• splitting criteria
• recognizing instability and overfitting in decision tree models
• capturing interactions between variables
• using SAS Enterprise Miner to build decision trees

Neural Networks

• origins of neural networks
• neural networks compared with regression
• the algorithms used to train neural networks
• data preparation requirements for neural networks
• picking appropriate inputs for neural networks
• creating neural network models using SAS Enterprise Miner

Memory Based Reasoning

• similarity and distance
• distance metrics appropriate for different kinds of data
• the role of the training set in MBR
• combining the votes of several neighbors
• other K-nearest neighbor techniques
• collaborative filtering
• using the SAS Enterprise Miner MBR node

Clustering

• more on similarity and distance
• the K-means algorithm
• divisive clustering
• agglomerative clustering
• data preparation for clustering
• interpreting clusters
• finding clusters with SAS Enterprise Miner

Survival Analysis

• origins of survival analysis
• how business data is different from clinical data
• hazards and hazard charts
• retention curves and survival curves
• calculating survival from retention
• calculating hazards empirically
• parametric hazard models
• censoring
• competing risks
• survival based forecasting
• using SAS code in SAS Enterprise Miner to create survival curves

Miscellaneous Techniques

• link analysis
• genetic algorithms
• association rules
• using SAS Enterprise Miner to discover associations in retail data

Putting Data Mining Techniques to Work

• formulating the business problem as a data mining problem
• finding the tool that fits the problem

Software Addressed

This course addresses the following software product(s): SAS Enterprise Miner.

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