Monte Carlo Simulations in Python

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

This practical course introduces Monte Carlo simulations, which are used to estimate a range of outcomes for uncertain events, and Python libraries such as SciPy and NumPy make simulating fast and easy! As you advance your simulation skills, you’ll apply these skills on a dataset of diabetes patient outcomes and use the results of your simulation to understand how different variables impact diabetes progression. You’ll review probability distributions and learn how to choose the best distribution for your simulation, and you’ll discover the importance of input correlation and model sensitivity. Finally, you’ll learn to communicate your findings using the popular Seaborn visualization library.

• 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.
• 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.
• 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.
• 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!