Continuous, linear optimization problems when all variables and constraints are deterministic has been a widely explored topic with many solution methods. These problems become much more complicated when uncertainty is introduced in the model. In this talk, an introduction to the linear, continuous case of stochastic optimization is introduced. Solution methods to the approximate case of decomposing these stochastic problems into scenarios are explored. One algorithm to solve, Progressive Hedging, is explained. A particular example in power grid expansion is used to illustrate the process. This talk only requires a basic understanding of optimization and probability theory and should be approachable to anyone who has taken Calculus I.