Exploratory Data Mining with Application to Life and Social Sciences

Business Knowledge Series course

Duration: 3.0 days Course fee: $2,175 EPTO units: 4.2 CEUs: 1.8

Presented by Patricia Cerrito, Ph.D., professor of mathematics at University of Louisville

This focus of this course is on analyzing data in a variety of ways, including data visualization, association rules, clustering and predictive modeling. Text mining is used in the context of compressing categorical data. Many examples are taken from the social sciences, and from medical studies. It is particularly relevant for data in the social sciences, including open-ended survey questions, healthcare data, and data with difficult and vaguely stated outcomes.

Learn how to

investigate and explore large, complex data sets
compress categorical fields with hundreds or thousands of possible levels
make important decisions based upon complex data
group and cluster data in meaningful ways
investigate associations between outcomes.

Who should attend

Anyone with large amounts of data who is not certain how to examine it in order to maximize the extracted knowledge

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Prerequisites

Before attending this course, you should be familiar with basic statistics and have a basic knowledge of SAS programming, including the formation of a SAS DATA step.

Course Contents

Introduction to Data Mining

definition of data mining
examples from the health sciences
the data mining process

Introduction to SAS Enterprise Miner

basic nodes in SAS Enterprise Miner
starting a SAS Enterprise Miner project
additional data sets

Introduction to Data Visualization

introduction to data visualization
kernel density estimation
visualization of categorical data
example of student retention

Association Rules

introduction
the Association node in SAS Enterprise Miner
example of student enrollments
Path Analysis node
examples from health care

Compression of Categorical Data

introduction
numerical conversions to compress levels
basics of SAS Text Miner
multiple occurrences with same identifier
Text Miner with numeric codes

Introduction to Predictive Modeling

introduction
prediction of binary outcome
predictive modeling for interval outcomes
use of nodes to select variables
scoring data

Introduction to Clustering

introduction
the Clustering node in SAS Enterprise Miner
use of SOM/Kohonen
MEPS example
partitioning and clustering

Software

This course addresses SAS Enterprise Miner.

Course Materials

Students receive a hardcopy of the course notes and, in some courses, can choose to take home a copy of the course data.

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Are there additional topics you'd like for this course to address? Would you like for this course to be offered at another training facility? Let us know by adding to our Interest List.

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