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SAS Predictive Modeling Certification
This course covers the content of both SAS Data Integration Studio: Essentials and SAS Data Integration Studio: Additional Topics. It introduces and expands the knowledge of SAS Data Integration Studio and includes topics for registering sources and targets; creating and working with jobs; and working with transformations. This course also covers information on working with slowly changing dimensions, working with the Loop transformations, and defining new transformations. Targeted towards Data integration developers and data integration architects.
Learn How To:
- 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
Prerequisites:
- 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.
Course Outline:
SAS Enterprise Guide: ANOVA, Regression, and Logistic Regression
- 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.
The fees is inclusive of:
- Training & Digital Badge.
- Course material.
- 2 attempts of Global certification.
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