This practical course is designed to introduce you to the concept of Monte Carlo simulations and their practical applications. Monte Carlo simulations are a valuable tool for estimating a range of outcomes for uncertain events. With the help of Python libraries such as SciPy and NumPy, you will learn how to quickly and easily create your own simulations. Throughout the course, you will learn the step-by-step process of creating a simulation and apply these skills to perform a principled Monte Carlo simulation on a dataset of diabetes patient outcomes. By analyzing the results of your simulation, you will gain a deeper understanding of how different variables impact the progression of diabetes. In addition to learning how to create simulations, you will also learn how to assess and improve them. You will explore probability distributions and gain an understanding of how to choose the appropriate distribution for your simulation. You will also discover the significance of input correlation and model sensitivity analysis in improving the accuracy of your simulations. Finally, you will learn how to effectively communicate your simulation findings using the popular Seaborn visualization library. This will enable you to present your results in a clear and visually appealing manner. By the end of this course, you will have the knowledge and skills to confidently create and analyze Monte Carlo simulations using SciPy and NumPy, and effectively communicate your findings using Seaborn.