Welcome to my GitHub page for a practical course on Risk Assessment using Monte Carlo Simulation!

This course is designed to provide a comprehensive understanding of the principles and applications of Monte Carlo simulation in risk assessment. By the end of this course, you will be able to design and implement Monte Carlo simulations to assess risk in a variety of contexts.

On this GitHub page, you will find all the materials for the course, including:

1. Lecture slides
2. Sample code and datasets
3. Hands-on exercises and projects
4. Additional resources for further reading

We will cover the basic concepts of risk assessment, introduce the concept of Monte Carlo simulation and its role in risk assessment.

We will delve into the details of Monte Carlo simulation, including the different types of probability distributions and how to generate random numbers. We will also discuss the importance of statistical measures such as mean and standard deviation in risk assessment.

We will apply Monte Carlo simulation to real-world examples, such as portfolio optimization and risk management in the financial industry. We will also explore the use of Monte Carlo simulation in other fields such as engineering and environmental science.

We will cover advanced topics in Monte Carlo simulation, such as sensitivity analysis, scenario analysis, and decision trees. We will also discuss the limitations of Monte Carlo simulation and the potential for bias in the results.

If you have any questions or feedback, please feel free to contact me or open an issue on this GitHub page.

Happy learning!