SAS Predictive Modeling

This course covers a range of introductory statistical topics and uses SAS software to carry out analysis. Emphasis will be placed on the interpretation of the results.

Duration 6 Days
Certificate SAS Global
Language English

Fees 66120 + taxes

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Course Description

Statisticians and business analysts who want to use a point-and-click interface to SAS; as well as data analysts, qualitative experts, and others who want an introduction to SAS Enterprise Miner

About Program

The schedule of events displayed on this page are for full-time program registration. If interested in part-time programs, please contact SAS India.

This course covers a range of introductory statistical topics and uses SAS software to carry out analysis. Emphasis will be placed on the interpretation of the results. It covers the skills required to assemble analysis flow diagrams using the rich tool set of SAS Enterprise Miner for both pattern discovery (segmentation, association, and sequence analyses) and predictive modeling (decision tree, regression, and neural network models). Ready-to-use procedures handle a wide range of statistical techniques including simple descriptive statistics, data visualization, analysis of variance, regression, categorical data analysis, multivariate analysis, cluster analysis, and non parametric analysis are part of this program

Format of Training

Live Web Classroom

Training is Led by an expert instructor live online using live web training platform, and this are not pre recorded lectures. Trainer can virtually look over your shoulder. Discuss, share, exchange ideas with students from different countries. 

Taste of Live Web - is a perfect platform for indivudals who fear online training. SAS® gives an opportunity to all the registrants to have a closer look at different courses given below with a blend of Online Learning. 

All the benefits of the classroom without the travel:

Classroom training options include courses offered in our regional training centers or via our Live Web classroom.

Taught by certified instructors at High-Tech facilities across the country:

  • A SAS expert at your side.
  • Focused learning away from the office
  • Networking opportunities
  • State-of-the-art facilities
  • Electronic course notes downloadable to your device and permission to print
  • Business Knowledge Series: in-depth courses on the latest business topics
  • We offer Connected Classes! Watch for courses in Cary, New York, Arlington, Dallas and San Francisco that connect remote students via our Live Web classroom.


Before attending this course, you should have knowledge in statistics covering p-values, hypothesis testing, analysis of variance, and regression. In addition, you should have at least an introductory-level familiarity with basic statistics and regression modeling.

Previous SAS software experience is helpful but not necessary.

Training Features

  • generate descriptive statistics and explore data with graphs
  • perform analysis of variance
  • perform linear regression and assess the assumptions
  • use diagnostic statistics to identify potential outliers in multiple regression
  • use chi-square statistics to detect associations among categorical variables
  • fit a multiple logistic regression model
  • define a SAS Enterprise Miner project and explore data graphically
  • modify data for better analysis results
  • build and understand predictive models such as decision trees and regression models
  • compare and explain complex models
  • generate and use score code
  • apply association and sequence discovery to transaction data
  • use other modeling tools such as rule induction, gradient boosting, and support vector machines.

Course Curriculum

Prerequisite Basic Concepts

  • discussing descriptive statistics
  • discussing inferential statistics
  • listing steps for conducting a hypothesis test
  • discussing basics of using your SAS software

Getting Started in Enterprise Guide 7.1

  • introducing to the SAS Enterprise Guide 7.1 environment

Introduction to Statistics

  • discussing fundamental statistical concepts
  • examining distributions
  • describing categorical data
  • constructing confidence intervals
  • performing simple tests of hypothesis

Analysis of Variance (ANOVA)

  • performing one-way ANOVA
  • performing multiple comparisons
  • performing two-way ANOVA with and without interactions


  • using exploratory data analysis
  • producing correlations
  • fitting a simple linear regression model
  • understanding the concepts of multiple regression
  • building and interpreting models
  • describing all regression techniques
  • exploring stepwise selection techniques

Regression Diagnostics

  • examining residuals
  • investigating influential observations and collinearity

Categorical Data Analysis

  • describing categorical data
  • examining tests for general and linear association
  • understanding the concepts of logistic regression and multiple logistic regression
  • performing backward elimination with logistic regression


  • introduction to SAS Enterprise Miner

Accessing and Assaying Prepared Data

  • creating a SAS Enterprise Miner project, library, and diagram
  • defining a data source
  • exploring a data source

Introduction to Predictive Modeling: Predictive Modeling Fundamentals and Decision Trees

  • introduction
  • cultivating decision trees
  • optimizing the complexity of decision trees
  • understanding additional diagnostic tools (self-study)
  • autonomous tree growth options (self-study)

Introduction to Predictive Modeling: Regressions

  • selecting regression inputs
  • optimizing regression complexity
  • interpreting regression models
  • transforming inputs
  • categorical inputs
  • polynomial regressions (self-study)

Introduction to Predictive Modeling: Neural Networks and Other Modeling Tools

  • input selection
  • stopped training
  • other modeling tools (self-study)

Model Assessment

  • model fit statistics
  • statistical graphics
  • adjusting for separate sampling
  • profit matrices

Model Implementation

  • internally scored data sets
  • score code modules

Introduction to Pattern Discovery

  • cluster analysis
  • market basket analysis (self-study)

Special Topics

  • ensemble models
  • variable selection
  • categorical input consolidation
  • surrogate models
  • SAS Rapid Predictive Modeler

Course Fees



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