Simulation Models for Decision Making from Coursera

This course is primarily aimed at third- and fourth-year undergraduate students or graduate students interested in learning simulation techniques to solve business problems.

The course will introduce you to take everyday and complex business problems that have no one correct answer due to uncertainties that exist in business environments. Simulation modeling allows us to explore various outcomes and protect personal or business interests against unwanted outcomes. We can model uncertainties by using the concepts of probability and stepwise thinking. Stepwise thinking allows us to break down the problem in smaller components, explore dependencies between related events and allows us to focus on aspects of problem that are prone to changes due to future uncertainties.

The course will introduce you to advanced Excel techniques to model and execute simulation models. Many of the Excel techniques learned in the course will be useful beyond simulation modeling. We will learn both Monte Carlo simulation techniques where overall outcome is of primary interest and discrete event simulation where intermediate dependencies between related events might be of interest. The course will introduce you to several practical issues in simulation modeling that are normally not covered in textbooks. The course uses a few running examples throughout the course to demonstrate concepts and provide concrete modeling examples.

After taking the course a student will be able to develop fairly advanced simulation models to explore fairly broad range of business environments and outcomes.

What's inside

Syllabus

Week 1: Probability Concepts

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.

Week/Module 2: Probability Distributions and Introduction to Monte Carlo Simulations

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.

Week 3: Monte Carlo Simulations

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.

Week 4: Counterfactual Analysis and Discrete Event Simulations

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.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Suitable for third- and fourth-year undergraduate students, as well as graduate students with an interest in learning simulation techniques for business problem-solving

Introduces advanced Excel techniques for modeling and executing simulation models

Emphasizes practical issues in simulation modeling that are often overlooked in textbooks

Includes several practical examples to demonstrate concepts and provide concrete modeling demonstrations

Provides foundational understanding of probability concepts, probability distributions, and Monte Carlo simulations

Covers discrete event simulations, exploring dependencies between related events

Reviews summary

Engaging simulation course

Learners say that this simulation models course is engaging and well-paced. It is a practical course that thoroughly covers the fundamentals of simulation modeling with real-world examples. Students appreciate the clear explanations and guidance. The interactive simulations and hands-on projects are helpful in understanding the concepts. The instructors are knowledgeable and supportive, although some students criticize the visual materials. Overall, this course is a great option for those looking to learn about simulation models for decision making.

Instructors are knowledgeable and supportive.

"The instructors are knowledgeable and supportive."

"The instructors are very responsive to questions and are always willing to help."

"I found the instructors to be passionate about the subject matter."

Instructors provide clear explanations.

"I liked the clear explanations."

"The explanations in this course are clear and easy to understand."

"I found the instructors' lectures to be well-organized and informative."

Course includes engaging simulations and projects.

"The interactive simulations and hands-on projects are very helpful."

"I found the simulations and projects to be engaging and educational."

"The course provides a good mix of theoretical and practical content."

Course is practical and well-paced.

"This course is practical and well-paced."

"I found the course to be very practical and applicable to my work."

"The course is well-structured and provides a good balance of theory and practice."

Some students find the visual materials to be outdated.

"The visual material is 'old style' power point with very text heavy slides which at times had cheezy 'clip art' animations."

"The visual materials were so poor and distracting to me I decided not to finish this course."

"I suggest a total re-do of the visual material with a designer and modern graphics."

Career center

Learners who complete Simulation Models for Decision Making will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists use data to solve business problems. Simulation modeling is a valuable tool for Data Scientists, and you will have the opportunity to develop and strengthen this skill-set through taking this course. Simulation Models for Decision Making will teach you how to use statistical methods and mathematical models to build predictive models that can be used to make better decisions. This is a valuable skill for Data Scientists, and it will give you a competitive advantage in the job market.

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Statistician

Statisticians use statistical methods to collect, analyze, and interpret data. Simulation modeling is a valuable tool for Statisticians, and the knowledge and skills you will learn in the Simulation Models for Decision Making course will provide you with a strong foundation for a career in this field.

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Operations Research Analyst

Operations Research Analysts use mathematical and analytical techniques to improve the efficiency and effectiveness of organizations. Simulation modeling is a valuable tool for Operations Research Analysts, and the knowledge and skills you will learn in the Simulation Models for Decision Making course will provide you with a strong foundation for a career in this field.

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Six Sigma Specialist

Six Sigma Specialists use statistical and analytical techniques to improve the quality of products and processes. Simulation modeling can be used to help Six Sigma Specialists identify and eliminate defects. The knowledge and skills you will learn in the Simulation Models for Decision Making course will provide you with a competitive advantage in the Six Sigma field and help you to improve the quality of products and processes.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment recommendations. Simulation modeling is a valuable tool for Quantitative Analysts, and the knowledge and skills you will learn in the Simulation Models for Decision Making course will provide you with a strong foundation for a career in this field. The course will teach you how to use Excel to build and analyze financial models, a critical skill for Quantitative Analysts.

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Data Analyst

Data Analysts use statistical and quantitative techniques to collect, clean, and analyze data to identify trends and patterns and solve business problems. The skills you will learn in the Simulation Models for Decision Making course, including probability, statistics, and modeling, will provide you with a strong foundation for a career as a Data Analyst. The course will also teach you how to use Excel to build and analyze simulation models, which is a valuable skill for Data Analysts.

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Risk Analyst

Risk Analysts identify and assess risks that could affect an organization. Simulation modeling can be used to help Risk Analysts quantify risks and develop mitigation strategies. The knowledge and skills you will learn in the Simulation Models for Decision Making course will provide you with a strong foundation for a career in this field. The course will teach you how to use Excel to build and analyze risk models, a valuable skill for Risk Analysts.

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Management Consultant

Management Consultants help organizations improve their performance by identifying and solving business problems. Many consulting firms use simulation modeling to help their clients make better decisions. The knowledge and skills you will learn in the Simulation Models for Decision Making course will provide you with a competitive advantage in the consulting industry and increase your ability to raise the caliber of your counsel.

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Systems Analyst

Systems Analysts design and implement computer systems. Simulation modeling can be used to help Systems Analysts evaluate the performance of different system designs. The knowledge and skills you will learn in the Simulation Models for Decision Making course will provide you with a competitive advantage in the Systems Analysis field and help you to design and implement more efficient and effective systems.

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Product Manager

Product Managers are responsible for managing the development and launch of new products. Simulation modeling can be used to help Product Managers make better decisions about product design, pricing, and marketing. The knowledge and skills you will learn in the Simulation Models for Decision Making course will provide you with a competitive advantage in the product management field and help you to launch more successful products.

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Software Engineer

Software Engineers design, develop, and maintain software applications. Simulation modeling can be used to help Software Engineers evaluate the performance of different software designs. The knowledge and skills you will learn in the Simulation Models for Decision Making course will provide you with a competitive advantage in the Software Engineering field and help you to design and develop more efficient and effective software applications.

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Business Analyst

Business Analysts combine advanced problem-solving and analytical skills to help organizations make better decisions. The knowledge gained from Simulation Models for Decision Making will provide you with a better understanding of how to use data to identify inefficiencies, improve processes, and develop better strategies. The course will also teach you how to use simulation modeling to create models that can help predict future outcomes and make better decisions.

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Market Researcher

Market Researchers conduct research to understand consumer behavior and trends. Simulation modeling can be used to help Market Researchers develop and test new marketing strategies. The knowledge and skills you will learn in the Simulation Models for Decision Making course will provide you with a competitive advantage in the Market Research field and help you to develop and test more effective marketing strategies.

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Financial Analyst

Financial Analysts use financial data to make investment recommendations and help businesses make financial decisions. The Simulation Models for Decision Making course will provide you with the skills and knowledge you need to understand financial data and develop simulation models to help you make better investment decisions. The course will also teach you how to use Excel to build and analyze financial models, which is a valuable skill for Financial Analysts.

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Teacher

Teachers educate students in a variety of subjects. Simulation modeling can be used to help Teachers create more engaging and effective lesson plans. Incorporating the course materials into your set of instructional tools will provide additional opportunities to actively engage your students and liven up your syllabi.

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