Quantify and model uncertainty with Monte Carlo Simulation, using random sampling in Python to support better decision-making.
- Generate statistical insights by using historical data to estimate future events
- Calculate value at risk to summarize risk exposure
- Visualize the results of your simulation to better communicate your recommendations
In this course, you’ll learn how to quantify and model uncertainty by using Monte Carlo simulation.
Traditional scenario analysis relies on 2 or 3 “best case” or “worst case” situations that are rarely scientific in nature. Businesses can benefit greatly from improved modeling of risk and uncertainty, by using even basic Monte Carlo simulation.
Using this technique, we can quantify and simulate scenarios that include multiple uncertainties at the same time.
This course will start from the basics, and work through five scenarios that will help you master the basics of Monte Carlo Simulation.
Using these scenarios, you’ll learn how to quantify uncertain scenarios in a more meaningful way to help make business decisions.
Business Intelligence derives value from descriptive, backward-looking metrics. To provide the next level of value we must start to consider future scenarios. Modeling uncertainty and scenarios is a key part of this forward-looking skillset, and this Monte Carlo course is a perfect introduction to that world.
What You’ll Learn In Modeling Risk with Monte Carlo Simulation?
- Explain the main concepts of Monte Carlo simulation
- Use historical observations to estimate the probability distributions of data
- Simulate many possible outcomes of uncertain variables using Python
- Summarize the distribution of scenarios using confidence intervals
- Interpret the output of Monte Carlo simulation results and use it to guide business decisions
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