Autonomous data science agents currently depend on costly trial-and-error workflows, motivating the need for models that predict operation outcomes prior to execution. The paper introduces the Data Science World Model, which models the data science environment by predicting state transitions based on current workflow state. This approach aims to improve agent efficiency by anticipating effects before real computation.