Data Mining Techniques: Theory and Practice

Duration: 3.0 days

Audience

This course is based on the newly revised and expanded book, Data Mining Techniques for Marketing, Sales and Customer Relationship Management. This course is for business analysts and their managers, statisticians, and anyone who has a professional interest in data mining.

Course Description

The course introduces a data mining methodology that is a superset of the SAS SEMMA methodology around which SAS Enterprise Miner is organized. This course also introduces a wide range of data mining algorithms and both theoretical knowledge and practical skills.

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 aren’t 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

As data exploration and classification tools
For modeling and scoring
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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