Course
Monte Carlo Simulations in Python
IntermediateSkill Level
Updated 04/2026PythonProbability & Statistics4 hr15 videos52 Exercises4,350 XP8,218Statement of Accomplishment
Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.Loved by learners at thousands of companies
Training 2 or more people?
Try DataCamp for BusinessCourse Description
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.Prerequisites
Sampling in Python1
Introduction to Monte Carlo Simulations
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.
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
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
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!
Monte Carlo Simulations in Python
Course Complete
Earn Statement of Accomplishment
Add this credential to your LinkedIn profile, resume, or CVShare it on social media and in your performance reviewEnroll Now
FAQs
What Python libraries are used for running Monte Carlo simulations in this course?
You will use SciPy and NumPy for running simulations and Seaborn for visualizing your results. The course also uses pandas for data manipulation.
What real-world dataset is used to apply simulation skills?
You will apply Monte Carlo simulations to a dataset of diabetes patient outcomes, using simulation results to understand how different variables impact diabetes progression.
Does this course cover how to choose the right probability distribution for a simulation?
Yes. You will review probability distributions and learn how to select the best distribution for your simulation, as well as understand input correlation and model sensitivity.
Is this course suitable for someone with only basic Python skills?
This is an intermediate course. You need prior knowledge of pandas, basic statistics, and sampling techniques in Python before enrolling.
What is the first simulation exercise in the course?
You start by learning to estimate the value of pi using a Monte Carlo simulation, which introduces the core concept before moving to more advanced applications like resampling.
Join over 19 million learners and start Monte Carlo Simulations in Python today!
Create Your Free Account
or
By continuing, you accept our Terms of Use, our Privacy Policy and that your data is stored in the USA.Grow your data skills with DataCamp for Mobile
Make progress on the go with our mobile courses and daily 5-minute coding challenges.