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.
Virtual Training nebo e-Learning?
Máme dostatečnou flexibilitu, takže vybírat můžete jak prezenční termíny, tak online kurzy.
Zkuste živý kurz virtuálněTarget group
Those who want to develop the advanced knowledge and skills necessary to work as a data scientist, especially those with a strong background in applied mathematics
Course structure
Introduction to Mathematical Optimization
- Introduction.
- A simple example.
- The OPTMODEL procedure.
Linear Programming Problems: Basic Ideas
- Introduction to linear programming.
- Formulating and solving linear programming problems using the OPTMODEL procedure.
- Using index sets and arrays in the OPTMODEL procedure.
- Dual values and reduced costs in the simplex method (self-study).
- Applied data envelopment analysis.
- Reading SAS data sets (self-study).
Nonlinear Programming Problems
- Introduction to nonlinear programming.
- Solving nonlinear programming problems using the OPTMODEL procedure.
Prerequisites
Before enrolling in the data science certification program, you should have completed all coursework for the SAS Certified Big Data Professional program or passed the Big Data Certification exams. Before attending this course:
- You should complete an undergraduate course in operations research that includes linear programming, have recent experience using linear programming, or be comfortable with matrix algebra.
- You should be able to execute SAS programs and create SAS data sets.
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