In this chapter, several fundamental stochastic computational tools are introduced. The chapter starts by presenting the ideas of the Monte Carlo method, which is a widely utilized technique that exploits the repeated sampling of random variables to solve many deterministic and stochastic problems. This is followed by introducing importance sampling, a special Monte Carlo method that draws the sample from a proposal distribution and re-weights the integral using importance weights to obtain the target distribution. Then, the Euler-Maruyama and Milstein schemes are introduced, which are essential methods for numerically finding the path-wise solution of the SDEs. With these tools in hand, the Monte Carlo simulation is combined with the numerical schemes that allow using the ensemble method to approximate the statistics of the SDE numerically. Ergodicity is also discussed. It provides a more efficient way to compute the equilibrium statistics if the underlying SDE has such a desirable feature. Finally, the kernel density estimation is presented, which advances the recovery of a smoothed PDF using only a finite number of samples.