SAS - SAS® Certified Specialist: Forecasting and Optimization Using SAS® Viya

Curriculum

Module 1: Data Visualization (15% – 20%)

Create project and load data

• Create a Forecasting project (define variable roles)
• Load data from various sources
• Use Data tab functionality

Visualize data using attribute variables

• Load Attributes table
• Identify scenarios in which attribute variable are useful in visualizing data
• Create a Visualization using Attribute Variables

Module 2: Pipeline Modeling (25% – 30%)

Model using a pipeline

• Auto-forecast using a pipeline
• Build and run a custom pipeline
• Given a scenario select and use appropriate pipeline template
• Visualize the forecasts

Determine the champion models

• Compare models within a pipeline
• Recognize and interpret the model family of the champion model
• Define the role of accuracy statistics in pipeline comparison
• Select the champion model for the project
• Explore the champion model

Judge model accuracy using accuracy statistics

• Define and calculate MAPE, MAE, RMSE Adaptive learning
• Given a scenario determine when is best appropriate to use MAPE, MAE or RMSE
• Use a holdout sample to do honest assessment

Module 3: Hierarchical Forecasting (15% – 20%)

Generate a forecast using data with a hierarchical structure

• Generate a hierarchical forecast with default functionality
• Improve the fit of a forecast by adding combined models
• Share a model using The Exchange
• Visualize the forecast models for a given level of the hierarchy

Use Time Series data creation options

• Explain the differences between data accumulation and data aggregation
• Given a scenario select the appropriate accumulation or aggregation options

Implement a hierarchical model or combined model

• Given a scenario select the appropriate reconciliation method for a hierarchical model
• Generate a combined model

Module 4: Post-Forecasting Functionality (10% – 15%)

Implement an override on a forecast in SAS Model Studio

• Apply an override to a forecast
• Resolve an override conflict
• Use attribute variable to set an override
• Disseminate tables containing the results of a forecast (manually vs. automatically)

Export a forecast

• Prepare exported data set for use in SAS Visual Analytics

Module 5: Optimization (25% – 30%)

Optimize using Linear Programming

• Explain local properties of functions that are used to solve mathematical optimization problems
• Use the OPTMODEL procedure to enter and solve simple linear programming problems
• Formulate linear programming problems using index sets of arrays of decision variables, families of constraints, and values stored in parameter arrays
• Modify a linear programming problem (changing bounds or coefficients, fixing variables, adding variables or constraints) within the OPTMODEL procedure

Optimize using Nonlinear Programming

• Use the OPTMODEL procedure to enter and solve simple nonlinear programming problems
• Describe how, conceptually and geometrically, iterative improvement algorithms solve nonlinear programming problems
• Identify the optimality conditions for nonlinear programming problems
• Solve nonlinear programming problems using OPTMODEL procedure
• Interpret information written to the SAS log during the solution of a nonlinear programming problem
• Differentiate between the NLP algorithms and how solver options influence the NLP algorithms

Optimize using Mixed Integer Linear Programming

• Use the OPTMODEL procedure to enter and solve simple MILP problems
• Identify the optimality conditions for MILP problems
• Solve MILP programming problems using the OPTMODEL procedure