Data Mining Cookbook: Introduction to Effective Predictive Modeling

Business Knowledge Series course

This course is not currently scheduled.

Duration: 2.0 days
Course fee:
EPTO units: .
CEUs: 1.2

Presented by C. Olivia Parr-Rud, Data Mining Strategist Consultant, OLIVIAGroup, and author of The Data Mining Cookbook

This course teaches statisticians and modelers how to assimilate business knowledge into predictive model development. Several methodologies are discussed and compared, with the emphasis is on how to meet a company's business objective through a thorough understanding of how to translate the objective into a case for predictive modeling.

Learn how to

define the modeling objective
gather and prepare the data for the model
select and transform variables to prepare for modeling
process and evaluate the model
validate, implement, and maintain the model.

Who should attend

Analysts, statisticians, marketers, and programmers

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Prerequisites

Before attending this course, it is recommended that you have some familiarity with database marketing. Proficiency with Base SAS is highly recommended. Basic understanding of SAS DATA steps is required. A general understanding of statistics is helpful.

Course Contents

Defining the Objective

discuss the model objective as it relates to the overall company or business goals
discuss a variety of objectives related to different industries
introduce descriptive and predictive statistical methods

Gathering the Data

gain a thorough understanding of the many data types and sources
learn rules for obtaining the best data for each specific modeling project
learn how to create a modeling data set
introduce the case study

Preparing the Data for Modeling

understand the importance of accurate data
learn to create a modeling data set
learn sampling techniques
clean and prepare the data for modeling

Selecting and Transforming the Variables

learn how to define the objective function in business terms that relate to the company objective
understand some important techniques in selecting and transforming variables
learn efficient methods for transforming large numbers of input variables
learn methods for selecting the final candidate variables

Processing and Evaluating the Model

understand the characteristics and benefits of logistic regression
learn some of the different options within logistic regression
practice running and evaluating the model

Validating the Model

learn how to build a gains table and lift chart
understand how to implement bootstrapping validation
learn to create tables to emphasize the model's effect on any variable

Implementing and Maintaining the Model

learn how to prepare data for scoring and implementation
learn how to audit data to insure valid results
understand the key components and benefits of a model log

Software

This course addresses SAS/STAT. This course also touches on SAS/GRAPH software.

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. You also receive a copy of C. Olivia Parr-Rud's book, The Data Mining Cookbook.

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Course fee and EPTO units will differ for on-site training.

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