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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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