SAS Statistical Business Analyst Professional Certificate

Distinguish Yourself as a Modeler. You will acquire SAS statistics, modeling, and programming skills including ANOVA, regression, logistic regression, business applications of modeling, and challenges of modeling.

Instructors: Marc Huber +1 more

What you'll learn

  •   Perform ANOVA, Regression, and logistic regression analysis with one or many inputs
  •   Prepare inputs for predictive models
  •   Train, validate, and evaluate statistical models

    • Skills you'll gain

  •   Regression Analysis
  •   Statistical Methods
  •   Performance Analysis
  •   SAS (Software)
  •   Data Analysis
  •   Statistical Hypothesis Testing
  •   Predictive Modeling
  •   Plot (Graphics)
  •   Feature Engineering
  •   Exploratory Data Analysis
  •   Statistical Analysis
  •   Statistical Modeling

    • Professional Certificate - 3 course series

    There are numerous hands-on practices integrated throughout the three courses of the program. Data examples are general enough to be applicable to a broad range of subject areas. Specific examples you will see in the courses address agriculture, manufacturing, health care, banking, retail, and nonprofit. ­­

    This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression.

    This introductory course is for SAS software users who perform statistical analyses using SAS/STAT software. The focus is on t tests, ANOVA, and linear regression, and includes a brief introduction to logistic regression.

    This course covers predictive modeling using SAS/STAT software with emphasis on the LOGISTIC procedure. This course also discusses selecting variables and interactions, recoding categorical variables based on the smooth weight of evidence, assessing models, treating missing values, and using efficiency techniques for massive data sets. You learn to use logistic regression to model an individual's behavior as a function of known inputs, create effect plots and odds ratio plots, handle missing data values, and tackle multicollinearity in your predictors. You also learn to assess model performance and compare models.

    Regression Modeling Fundamentals

    Predictive Modeling with Logistic Regression using SAS

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