University of Minnesota
Simulation Models for Decision Making
University of Minnesota

Simulation Models for Decision Making

This course is part of Analytics for Decision Making Specialization

Taught in English

Some content may not be translated

Alok Gupta

Instructor: Alok Gupta

5,610 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.6

(43 reviews)

Beginner level

Recommended experience

17 hours (approximately)
Flexible schedule
Learn at your own pace

Details to know

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Assessments

12 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.6

(43 reviews)

Beginner level

Recommended experience

17 hours (approximately)
Flexible schedule
Learn at your own pace

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This course is part of the Analytics for Decision Making Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 4 modules in this course

Uncertainty leads to challenges in decision making. Mathematically, we represent uncertainty by defining probabilities when several of the outcomes are possible in the future. This modules provides an overview of probability concepts that are essential to lay a good foundation for simulation modeling. We will also get our first exposure to Excel based simulations.

What's included

15 videos4 quizzes

While being able to estimate probabilities using mathematical relationships is important, a lot of natural events follow or approximate some nicely defined probability distribution functions such as Uniform, Exponential and Normal Distributions. To effectively build simulation models, it is important to understand how to use these distributions. Further, we may need to find what distribution does our observed data follow. This module introduces the finer details of working with probability distribution functions and introduces the types of simulation models as well as some practice based tricks to work with real-world data that may not be complete or may not fit a given distribution exactly.

What's included

14 videos4 quizzes

We started by stating that simulation is one of the most flexible modeling approaches. This module demonstrates that flexibility. In this module, four Monte Carlo simulation models are built for a coffee shop. The models increase in technical complexity and sophistication to demonstrate various issues that modelers have to consider in building these models depending upon the type of questions that need to be answered. The lessons explain which models can answer certain type of questions and what questions may not be answered by a certain type of model. The results obtained from various models are then compared and discussed to understand the tradeoffs in choice of a particular model choice.

What's included

14 videos2 quizzes

In this module we wrap up the Monte Carlo Simulation modeling by looking at modeling special cases and doing counterfactual analysis (examining scenarios that may not have existed or initiatives that have not actually been implemented). We then examine the power of Discrete Event simulation. The goal of Discrete Event simulation modeling discussion is to introduce you to examine the dependencies in events and how these dependencies can be modeled in Excel with some innovative thinking, even though Excel does not natively support any functionality to support Discrete Event simulation. The material in this part is completely original and is designed for this course and will not be found in any books.

What's included

17 videos2 readings2 quizzes

Instructor

Instructor ratings
4.7 (15 ratings)
Alok Gupta
University of Minnesota
1 Course5,610 learners

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Recommended if you're interested in Probability and Statistics

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4.6

43 reviews

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CL
5

Reviewed on Jun 4, 2021

AC
5

Reviewed on Jan 7, 2022

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