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Monte Carlo Simulations in Python

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

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4 Hours15 Videos52 Exercises4350 XP

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Course 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.
  1. 1

    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.

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    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. 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.

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  3. 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!

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Diabetes Factors and Outcomes


Zhaojie Zhang
James Chapman
Maham Khan


Sampling in Python
Izzy Weber Headshot

Izzy Weber

Curriculum Manager, DataCamp

Izzy is a Curriculum Manager at DataCamp. 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 for students.
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