In order to make inferences based on genomic data, it is useful to understand the evolutionary forces that underlie observed genetic variation. The field of population genetics offers theoretical tools to test predictions, gain intuition, and even generate training data for statistical and deep learning models. However, population genetics simulations have historically been slow and complex, making effective usage of such tools difficult. Recently, the evolutionary simulation framework SLiM (Selection on Linked Mutations) has revolutionized the field, making the capability to model complex evolutionary scenarios more accessible. In this workshop, participants will be introduced to the population genetics theory underlying SLiM, learn how to model complex scenarios, and practice interpreting the results of their simulations to make predictions about real-world data. Participants will also be given the opportunity to bring in their own data to analyze and use as inspiration to generate models. By learning about the fundamental forces that shape genomic data and how to model them, participants will gain a combination of both theoretical insights into evolutionary genetics and practical insights into relevant computational tools.