After doing these courses, I feel confident creating professional visualizations and dashboards
Shoebur RahmanProcurement Specialist, Giz Bangladesh
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.Prerequisites
Curriculum
Course outline
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.
- What is a Monte Carlo simulation?50 XP
- Deterministic simulation100 XP
- Stochastic nature of Monte Carlo simulation100 XP
- The Law of Large Numbers100 XP
- Resampling as a special type of Monte Carlo simulation50 XP
- Sampling with replacement100 XP
- Visualization of resampling results100 XP
- Permutation practice100 XP
- Leveraging Monte Carlo simulations50 XP
- Understanding Monte Carlo simulations100 XP
- Paired dice simulation100 XP
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
