🎲 Monte Carlo Simulation

Overview

This project explores Monte Carlo methods implemented in Python to solve scientific and statistical problems through random sampling techniques.

The objective is to demonstrate how stochastic simulations can be used to approximate numerical solutions and analyse probabilistic systems.

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Features

• Monte Carlo simulations
• Random number generation
• Statistical analysis
• Numerical approximation
• Scientific visualization
• Python implementation

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Files

• Números_aleatorios.py → Main Python implementation of the Monte Carlo simulation.
• Simulación de Monte Carlo.pdf → Technical report describing the methodology, implementation and obtained results.

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Skills demonstrated

• Python programming
• Monte Carlo methods
• Probability and statistics
• Scientific computing
• Data analysis
• Numerical simulations

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Skills Developed

Through this project I strengthened my understanding of:

• Monte Carlo methods
• Random number generation
• Statistical analysis
• Scientific programming in Python
• Computational physics
• Scientific problem solving

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Author

Sofía Núñez de Andrés Developed during the Bachelor's Degree in Physics at the University of Oviedo.