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Course Description

Statistics is about using data to estimate certain values and evaluate certain hypotheses; this makes perfect sense for passively studying how the world works (i.e., the scientific method). More often than not, however, we find ourselves wanting to use this statistical information to make decisions regarding the systems involved. Suppose we estimate that the demand for jet fuel next month will be greater than normal. How does this information affect the decision of an oil refinery to purchase crude oil from their various sources? How does an airline company decide how many flight crews to employ based on the current flight schedule? How does past sales information across the U.S. influence a company's decision over where to place its warehouses?

The quantification and mathematical solution of these types of decision-making problems are known broadly as optimization. The general features of an optimization problem are a set of quantifiable decisions that have a quantifiable effect that should be minimized or maximized (think cost or revenue) and a set of constraints on the possible values of those decisions. There are many different optimization branches, but the most prominent, due to its widespread applicability and computational efficiency, is linear programming, where the objective function and constraints are all linear.

In this course, you will explore the mathematics of linear programs, how to solve them, and how to evaluate your model. You will implement these techniques using packages in the free and open-source statistical programming language R to solve real-world logistical business problems. The focus will be on making these methods accessible for you in your own work.

You are required to have completed the following courses or have equivalent experience before taking this course:

  • Understanding Data Analytics
  • Finding Patterns in Data Using Association Rules, PCA, and Factor Analysis
  • Finding Patterns in Data Using Cluster and Hotspot Analysis
  • Regression Analysis and Discrete Choice Models
  • Supervised Learning Techniques
  • Neural Networks and Machine Learning

Faculty Author

Linda Nozick

Benefits to the Learner

  • Create linear programs by specifying decision variables, a linear objective function, and linear constraints
  • Compute the optimal solution to a linear program using R
  • Use linear programming to solve real-world logistical problems
  • Integrate uncertainty into optimization models

Target Audience

  • Current and aspiring data scientists
  • Analysts
  • Engineers
  • Researchers
  • Technical managers

Applies Towards the Following Certificates

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Language of Delivery
English
Type
2 week
Dates
Aug 03, 2022 to Aug 16, 2022
Total Number of Hours
20.0
Course Fee(s)
Standard Price $969.00
Language of Delivery
English
Type
2 week
Dates
Sep 28, 2022 to Oct 11, 2022
Total Number of Hours
20.0
Course Fee(s)
Standard Price $969.00
Language of Delivery
English
Type
2 week
Dates
Nov 23, 2022 to Dec 06, 2022
Total Number of Hours
20.0
Course Fee(s)
Standard Price $969.00
Language of Delivery
English
Type
2 week
Dates
Jan 18, 2023 to Jan 31, 2023
Total Number of Hours
20.0
Course Fee(s)
Standard Price $969.00
Language of Delivery
English
Type
2 week
Dates
Mar 15, 2023 to Mar 28, 2023
Total Number of Hours
20.0
Course Fee(s)
Standard Price $969.00
Language of Delivery
English
Type
2 week
Dates
May 10, 2023 to May 23, 2023
Total Number of Hours
20.0
Course Fee(s)
Standard Price $969.00
Language of Delivery
English
Type
2 week
Dates
Jul 05, 2023 to Jul 18, 2023
Total Number of Hours
20.0
Course Fee(s)
Standard Price $969.00
Language of Delivery
English
Type
2 week
Dates
Aug 30, 2023 to Sep 12, 2023
Total Number of Hours
20.0
Course Fee(s)
Standard Price $969.00
Language of Delivery
English
Type
2 week
Dates
Oct 25, 2023 to Nov 07, 2023
Total Number of Hours
20.0
Course Fee(s)
Standard Price $969.00
Language of Delivery
English
Type
2 week
Dates
Dec 20, 2023 to Jan 02, 2024
Total Number of Hours
20.0
Course Fee(s)
Standard Price $969.00
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