1. Optimization Concepts for Data Science and Artificial Intelligence
This course focuses on linear, nonlinear, and mixed integer linear optimization concepts in SAS Viya. Students learn how to formulate optimization problems and how to make their formulations efficient by using index sets and arrays. The demonstrations in the course include examples of diet formulation and portfolio optimization. Learn the OPTMODEL procedure and open-source tools to formulate and solve optimization problems.
2. Optimization Concepts for Data Science
This course focuses on linear, nonlinear, and efficiency optimization concepts. You learn how to formulate optimization problems and how to make their formulations efficient by using index sets and arrays. The demonstrations in the course include examples of data envelopment analysis and portfolio optimization. The OPTMODEL procedure is used to solve optimization problems that reinforce concepts introduced in the course.
The e-learning format of this course includes Virtual Lab time to practice.
3. Network Analysis and Network Optimization in SAS Viya
This course provides a set of network analysis and network optimization solutions using the NETWORK and OPTNETWORK procedures in SAS Viya. Real-world applications are emphasized for each algorithm introduced in this course, including using network analysis as a stand-alone unsupervised learning technique, as well as incorporating network analysis and optimization to augment supervised learning techniques to improve machine learning model performance through input/feature creation.
4. Operations Research with SAS Optimization
This course focuses on formulating and solving mathematical optimization models using the OPTMODEL procedure, from inputting data to interpreting output and generating reports. The course covers smooth and non-smooth models with continuous and integer decision variables. Advanced capabilities, such as control flow, multiple models, and network optimization, leverage the power of SAS Viya to solve complicated business problems.
5. Programming with SAS/IML Software
This course teaches you how to use the IML procedure via the programming language. You benefit from this course if you plan to use SAS/IML for manipulating matrices, simulating data, writing custom statistical analyses, or working with R. The programs in this course require SAS/IML 12.3 or later to run.
The self-study e-learning includes:
- Annotatable course notes in PDF format.
- Virtual lab time to practice.
6. Discrete-Event Simulation with SAS Simulation Studio
This course is for analysts who need to use discrete-event simulation in order to model complex systems that are difficult or impossible to model using traditional analytical techniques. Discrete-event simulation models dynamic systems whose state changes only when distinct, discrete events occur. The simulation models can then be used to look at various changes to the processes to determine the impacts that those changes might have.
The self-study e-learning includes:
- Annotatable course notes in PDF format.
- Virtual Lab time to practice.
