Model yourself as a modeler. In these classes, you will equip yourself with skills in SAS statistics, modeling, and programming, including ANOVA, regression, logistic regression, business applications of modeling, and modeling challenges.
Suggested by: Coursera (What is Coursera?)
No prior knowledge required
No unnecessary risks
This program is designed for individuals who want to upgrade their predictive and statistical modeling skills to drive data-driven business outcomes. If data modeling for business outcomes is part of your role or industry, this certificate is a valuable demonstration of your proficiency.
Hands-on Learning Project
There are plenty of hands-on exercises throughout the three courses in the program. The data examples are general enough to be applicable to a wide range of fields. Specific examples you will see in the courses relate to agriculture, manufacturing, healthcare, banking, retail, and non-profit organizations.
Course 1 • 10 hours • 4.6 (115 ratings)
This course is intended for SAS users who perform statistical analyses using SAS/STAT software. The course focuses on t-tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression.
Course 2 • 11 hours • 4.7 (48 ratings)
This course is intended for SAS users who perform statistical analyses using SAS/STAT software. The course focuses on t-tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression.
Course 3 • 16 hours • 4.6 (53 ratings)
This course deals with predictive modeling using SAS/STAT software, with an emphasis on the logistic procedure. The course also includes a discussion of variable selection and interactions, categorical variable imputation based on partial evidence weights, model estimation, handling missing values, and using efficient techniques for large data sets. You will learn to use logistic regression to measure individual behavior as a function of known inputs, create effect and likelihood plots, manage missing values, and deal with problems of multicollinearity between predictors. In addition, you will learn to evaluate model performance and compare models.
