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# Monte Carlo Simulations in Python

4.6+
11 reviews
Intermediate

Learn to design and run your own Monte Carlo simulations using Python!

4 hours15 videos52 exercises

or

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## Simulate Outcomes with SciPy and NumPy

This practical course introduces Monte Carlo simulations and their use cases. Monte Carlo simulations are used to estimate a range of outcomes for uncertain events, and Python libraries such as SciPy and NumPy make creating your own simulations fast and easy!

## Apply New Skills in a Principled Simulation

As you learn each step of creating a simulation, you’ll apply these skills by performing a principled Monte Carlo simulation on a dataset of diabetes patient outcomes and use the results of your simulation to understand how different variables impact diabetes progression.

## Learn How to Assess and Improve Your Simulations

You’ll review probability distributions and understand how to choose the proper distribution for use in your simulation, and you’ll discover the importance of input correlation and model sensitivity analysis. Finally, you’ll learn to communicate your simulation findings using the popular Seaborn visualization library.

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1. 1

### Introduction to Monte Carlo Simulations

Free

What are Monte Carlo simulations and when are they useful? After covering these foundational questions, you’ll learn how to perform simple simulations such as estimating the value of pi. You’ll also learn about resampling, a special type of Monte Carlo Simulation.

Play Chapter Now
What is a Monte Carlo simulation?
50 xp
Deterministic simulation
100 xp
Stochastic nature of Monte Carlo simulation
100 xp
The Law of Large Numbers
100 xp
Resampling as a special type of Monte Carlo simulation
50 xp
Sampling with replacement
100 xp
Visualization of resampling results
100 xp
Permutation practice
100 xp
Leveraging Monte Carlo simulations
50 xp
Understanding Monte Carlo simulations
100 xp
Paired dice simulation
100 xp
2. 2

### Foundations for Monte Carlo

Now that you can run your own simple simulations, you’re ready to explore real-world application of Monte Carlo simulations across various industries. Then, you’ll dive into the heart of what makes a good simulation work: sampling from the correct probability distribution. You’ll learn about probability distributions for discrete, continuous, and multivariate random variables.

3. 3

### Principled Monte Carlo Simulation

Once you’re comfortable with your choice of probability distribution, you’re ready to follow a principled Monte Carlo simulation workflow using a dataset of diabetes patient characteristics and outcomes. You will explore the data, perform a simulation, and generate summary statistics to communicate your simulation results.

4. 4

### Model Checking and Results Interpretation

Discover how to evaluate your Monte Carlo models and communicate the results with easy-to-read visualizations in Seaborn. Finally, use sensitivity analysis to understand how changes to model inputs will impact your results, and practice this concept by simulating how business profits are impacted by changes to sales and inflation!

### GroupTraining 2 or more people?

datasets

Diabetes Factors and Outcomes

collaborators

prerequisites

Sampling in Python
Izzy Weber

Data Coach at iO-Sphere

Izzy is a Data Coach at iO-Sphere. She discovered a love for data during her seven years as an accounting professor at the University of Washington. She holds a masters degree in Taxation and is a Certified Public Accountant. Her passion is making learning technical topics fun.
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## Don’t just take our word for it

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• Scott T.
28 days

The instruction was very clear and concise.

• Jason B.
5 months

Great Introduction with specific examples

• Francis B.
6 months

Excellent course! Very well put together!

• David N.
11 months

I liked the knowledge gained from the course

• Artur P.

.

"The instruction was very clear and concise."

Scott T.

"Great Introduction with specific examples"

Jason B.

"Excellent course! Very well put together!"

Francis B.