SAS Predictive Modeling Certification

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

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

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..

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.

Training & Digital Badge.

Course material.

2 attempts of Global certification.

Provide labor market insights that connect your new skills to jobs.

Enable recruiters to view and verify candidates with certain SAS skills.

It will answer, What you did? Who says you did it? and Why it matters? these questions for you.

• SAS Certified Trainers with Live Web Training Centers (Demonstrate your true SAS skills and understanding.)

• Hands on learning (Learn SAS Predictive Modeling Course in Pune with a SAS Certified expert instructor in a traditional classroom setting for an in-depth, hands-on learning experience.)

• Receive ongoing support and be a part of a community.

• Software Access (Access the latest software 24*7 on Azure servers)

• Earn a Globally Recognized Digital Badge (A secure, electronic verification of your achievement. Allow employers to easily discover you).

• SAS Global Certification Credentials (Earn your Base SAS Programmer credentials and be recognized across all industries and geographic locations).

▪ discussing descriptive statistics

▪ discussing inferential statistics

▪ listing steps for conducting a hypothesis test

▪ discussing basics of using your SAS software

▪ introducing to the SAS Enterprise Guide 7.1 environment

▪ discussing fundamental statistical concepts

▪ examining distributions

▪ describing categorical data

▪ constructing confidence intervals

▪ performing simple tests of hypothesis

▪ 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

▪ exploring stepwise selection techniques

▪ examining residuals

▪ investigating influential observations and collinearity

▪ 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 regressions

▪ introduction to SAS Enterprise Miner

▪ Creating a SAS Enterprise Miner project, library, and diagram

▪ defining a data source

▪ exploring a data source

▪ cultivating decision trees

▪ optimizing the complexity of decision trees

▪ understanding additional diagnostic tools (self-study)

▪ autonomous tree growth options (self-study)

▪ selecting regression inputs

▪ optimizing regression complexity

▪ interpreting regression models

▪ transforming inputs

▪ categorical inputs

▪ polynomial regressions (self-study)

▪ introduction to neural network models

▪ input selection

▪ stopped training

▪ other modeling tools (self-study)

▪ model fit statistics

▪ statistical graphics

▪ adjusting for separate sampling

▪ profit matrices

▪ internally scored data sets

▪ score code modules

▪ cluster analysis

▪ market basket analysis (self-study)

▪ ensemble models

▪ variable selection

▪ categorical input consolidation

▪ surrogate models

▪ SAS Rapid Predictive Modeler

▪ banking segmentation case study

▪ website usage associations case study

▪ credit risk case study

▪ enrollment management case study