**Course Overview**

Welcome to Optimization Concepts for Data Science. My name is Jay Laramore, and I’m an Analytical Training Consultant in SAS Education, where I’ve taught a wide variety of analytical techniques, ranging from time series forecasting, to matrix programming, to optimization. Prior to joining SAS, I worked in the marketing, health insurance, and fuel industries, deploying these analytical techniques to leverage resources and drive profitability. During my career at SAS I’ve worked with customers from a wide range of fields. Including the pharmaceutical, biotech, insurance, and banking industries– as well as government and academic institutions– to help them learn how to analyze and leverage their data to make informed decisions.

The Optimization Concepts for Data Science course will teach you to effectively build and structure a variety of different optimization problems using SAS software. This class covers linear and nonlinear programming techniques. And will equip you with the tools needed to begin deploying optimization solutions in SAS. This class will teach you the necessary components of the OPTMODEL programming language, build intuition behind the algorithms running under the hood, and develop the framework for analyzing and solving optimization problems. To be successful in this course, you should be comfortable with programming in SAS, be detail oriented, and also have some prior knowledge of basic mathematical concepts. Let’s get started.

### Course Curriculum

0.1a Course Overview-Solving Optimization Problems | |||

0.1a Course Overview-Solving Optimization Problems | 00:00:00 | ||

0.1b Building Blocks Optimization Problem QUESTIONS | |||

0.1b Building Blocks Optimization Problem QUESTIONS | 00:00:00 | ||

0.1c Think About It | |||

0.1c Think About It | 00:00:00 | ||

0.1d DemonstrationDemo Building Blocks Optimization Solution | |||

0.1d DemonstrationDemo Building Blocks Optimization Solution | 00:00:00 | ||

1.1a introduction Introduction to Mathematical Optimization | |||

1.1a introduction Introduction to Mathematical Optimization | 00:00:00 | ||

1.1b objective Introduction to Mathematical Optimization | |||

1.1b objective Introduction to Mathematical Optimization | 00:00:00 | ||

1.2a What Is Optimization | |||

1.2a What Is Optimization | 00:00:00 | ||

1.2b Defining Mathematical Optimization Problems | |||

1.2b Defining Mathematical Optimization Problems | 00:00:00 | ||

1.2c OPTMODEL Procedure | |||

1.2c OPTMODEL Procedure | 00:00:00 | ||

1.2d Optimization Modeling Process | |||

1.2d Optimization Modeling Process | 00:00:00 | ||

1.2e Why Optimization | |||

1.2e Why Optimization | 00:00:00 | ||

1.2f Optimization Going Up or Down | |||

1.2f Optimization Going Up or Down | 00:00:00 | ||

1.2g Exercise Classifying Mathematical Optimization Problems EXERCISE | |||

1.2g Exercise Classifying Mathematical Optimization Problems EXERCISE | 00:00:00 | ||

1.2g Exercise Classifying Mathematical Optimization Problems SOLUTION | |||

1.2g Exercise Classifying Mathematical Optimization Problems SOLUTION | 00:00:00 | ||

1.3a Two-Dimensional Example | |||

1.3a Two-Dimensional Example | 00:00:00 | ||

1.4a Two-Dimensional Example | |||

1.4a Two-Dimensional Example | 00:00:00 | ||

1.4b Demo Solving the Simple Polynomial Example Using PROC OPTMODEL | |||

1.4b Demo Solving the Simple Polynomial Example Using PROC OPTMODEL | 00:00:00 | ||

1.4c Exercise Using the OPTMODEL Procedure EXERCISE | |||

1.4c Exercise Using the OPTMODEL Procedure EXERCISE | 00:00:00 | ||

1.4c Exercise Using the OPTMODEL Procedure SOLUTION | |||

1.4c Exercise Using the OPTMODEL Procedure SOLUTION | 00:00:00 | ||

2.1a Introduction-Linear Programming Problems Basic Ideas | |||

2.1a Introduction-Linear Programming Problems Basic Ideas | 00:00:00 | ||

2.1b objectives Linear Programming Problems Basic Ideas | |||

2.1b objectives Linear Programming Problems Basic Ideas | 00:00:00 | ||

2.2a A Linear Programming Problem | |||

2.2a A Linear Programming Problem | 00:00:00 | ||

2.2b Lagrangian Mulitplier - Building Blocks Example | |||

2.2b Lagrangian Mulitplier – Building Blocks Example | 00:00:00 | ||

2.2c Two-Dimensional Example | |||

2.2c Two-Dimensional Example | 00:00:00 | ||

2.2d Feasible Region - Building Blocks Example | |||

2.2d Feasible Region – Building Blocks Example | 00:00:00 | ||

2.2e Demo Solving a Linear Programming Problem Using PROC OPTMODEL | |||

2.2e Demo Solving a Linear Programming Problem Using PROC OPTMODEL | 00:00:00 | ||

2.2f Exercise Changing the Limit of a Tight Constraint Exercise | |||

2.2f Exercise Changing the Limit of a Tight Constraint Exercise | 00:00:00 | ||

2.2f Exercise Changing the Limit of a Tight Constraint solution | |||

2.2f Exercise Changing the Limit of a Tight Constraint solution | 00:00:00 | ||

2.2g Exercise Detecting Infeasible Linear Programming Problems Exercise | |||

2.2g Exercise Detecting Infeasible Linear Programming Problems Exercise | 00:00:00 | ||

2.2g Exercise Detecting Infeasible Linear Programming Problems Solution | |||

2.2g Exercise Detecting Infeasible Linear Programming Problems Solution | 00:00:00 | ||

2.2h Three-Dimensional Example | |||

2.2h Three-Dimensional Example | 00:00:00 | ||

2.2i Solving the Dual Linear Programming Problem Using PROC OPTMODEL (Self-Study) | |||

2.2i Solving the Dual Linear Programming Problem Using PROC OPTMODEL (Self-Study) | 00:00:00 | ||

2.3a Defining the Problem | |||

2.3a Defining the Problem | 00:00:00 | ||

2.3b Furniture-Making Problem | |||

2.3b Furniture-Making Problem | 00:00:00 | ||

2.3c Furniture-Making Problem Data | |||

2.3c Furniture-Making Problem Data | 00:00:00 | ||

2.3d Solving Mathematical Optimization Problems | |||

2.3d Solving Mathematical Optimization Problems | 00:00:00 | ||

2.3e Building Structure with Index Sets | |||

2.3e Building Structure with Index Sets | 00:00:00 | ||

2.3f Demo Using PROC OPTMODEL to Solve the Furniture-Making Problem | |||

2.3f Demo Using PROC OPTMODEL to Solve the Furniture-Making Problem | 00:00:00 | ||

2.3g Exercise Adding a Budget Constraint to the Furniture-Making Problem Exercise | |||

2.3g Exercise Adding a Budget Constraint to the Furniture-Making Problem Exercise | 00:00:00 | ||

2.3g Exercise Adding a Budget Constraint to the Furniture-Making Problem solution | |||

2.3g Exercise Adding a Budget Constraint to the Furniture-Making Problem solution | 00:00:00 | ||

2.4a Using Index Sets | |||

2.4a Using Index Sets | 00:00:00 | ||

2.4b Typographical Conventions | |||

2.4b Typographical Conventions | 00:00:00 | ||

2.4c Using Index Sets in the Furniture-Making Problem | |||

2.4c Using Index Sets in the Furniture-Making Problem | 00:00:00 | ||

2.4d Declaring Parameters in PROC OPTMODEL | |||

2.4d Declaring Parameters in PROC OPTMODEL | 00:00:00 | ||

2.4e Example Transportation Problem | |||

2.4e Example Transportation Problem | 00:00:00 | ||

2.4f Declaring Decision Variables | |||

2.4f Declaring Decision Variables | 00:00:00 | ||

2.4g SUM Aggregation Operator | |||

2.4g SUM Aggregation Operator | 00:00:00 | ||

2.4h Declaring Variables, Objectives, and Constraints | |||

2.4h Declaring Variables, Objectives, and Constraints | 00:00:00 | ||

2.4i Demo Using Arrays and Index Sets in PROC OPTMODEL to Solve the Furniture-Making Problem | |||

2.4i Demo Using Arrays and Index Sets in PROC OPTMODEL to Solve the Furniture-Making Problem | 00:00:00 | ||

2.4j Exercise Formulating a Transportation Problem Exercise | |||

2.4j Exercise Formulating a Transportation Problem Exercise | 00:00:00 | ||

2.4j Exercise Formulating a Transportation Problem Solution | |||

2.4j Exercise Formulating a Transportation Problem Solution | 00:00:00 | ||

2.5a self-study Dual Values and Reduced Costs in the Simplex Method | |||

2.5a self-study Dual Values and Reduced Costs in the Simplex Method | 00:00:00 | ||

2.6a What Is Data Envelopment Analysis | |||

2.6a What Is Data Envelopment Analysis | 00:00:00 | ||

2.6b Defining Efficiency | |||

2.6b Defining Efficiency | 00:00:00 | ||

2.6c Measuring Efficiency | |||

2.6c Measuring Efficiency | 00:00:00 | ||

2.6d Data Envelopment Analysis | |||

2.6d Data Envelopment Analysis | 00:00:00 | ||

2.6e Data Envelopment Analysis: Efficieny | |||

2.6e Data Envelopment Analysis: Efficieny | 00:00:00 | ||

2.6f Data Envelopment Analysis Frontiers, Returns to Scale, and Other Considerations | |||

2.6f Data Envelopment Analysis Frontiers, Returns to Scale, and Other Considerations | 00:00:00 | ||

2.6g Exercise Building Data Envelopment Analysis Models to Solve an Operations Problem Exercise | |||

2.6g Exercise Building Data Envelopment Analysis Models to Solve an Operations Problem Exercise | 00:00:00 | ||

2.6g Exercise Building Data Envelopment Analysis Models to Solve an Operations Problem Solution | |||

2.6g Exercise Building Data Envelopment Analysis Models to Solve an Operations Problem Solution | 00:00:00 | ||

2.7a Self-Study | |||

2.7a Self-Study | 00:00:00 | ||

3.1a Introduction Nonlinear Programming Problems | |||

3.1a Introduction Nonlinear Programming Problems | 00:00:00 | ||

3.1b objective Nonlinear Programming Problems | |||

3.1b objective Nonlinear Programming Problems | 00:00:00 | ||

3.2a Nonlinear Programming Problems | |||

3.2a Nonlinear Programming Problems | 00:00:00 | ||

3.2b Two-Dimensional Example | |||

3.2b Two-Dimensional Example | 00:00:00 | ||

3.2c Demo Minimizing the Banana Function Using PROC OPTMODEL | |||

3.2c Demo Minimizing the Banana Function Using PROC OPTMODEL | 00:00:00 | ||

3.2d Nonlinear Function Challenges | |||

3.2d Nonlinear Function Challenges | 00:00:00 | ||

3.2e Demo Solving the Sinc Problem using PROC OPTMODEL | |||

3.2e Demo Solving the Sinc Problem using PROC OPTMODEL | 00:00:00 | ||

3.2f Global Optima | |||

3.2f Global Optima | 00:00:00 | ||

3.2g Demo Solving the Sinc Problem using PROC OPTMODEL MULITISTART Options | |||

3.2g Demo Solving the Sinc Problem using PROC OPTMODEL MULITISTART Options | 00:00:00 | ||

3.3a NLP Options in the OPTMODEL Procedure | |||

3.3a NLP Options in the OPTMODEL Procedure | 00:00:00 | ||

3.3b Demo Solving a Portfolio Optimization Problem Using PROC OPTMODEL | |||

3.3b Demo Solving a Portfolio Optimization Problem Using PROC OPTMODEL | 00:00:00 | ||

3.3cThink about it. | |||

3.3cThink about it. | 00:00:00 | ||

3.3d NLP Algoritms | |||

3.3d NLP Algoritms | 00:00:00 | ||

3.3e Conclusion | |||

3.3e Conclusion | 00:00:00 | ||

Resources | |||

Resources | 00:00:00 |

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