The final stages of the data science methodology involve an iterative cycle between Modeling, Evaluation, Deployment, and what else?

The final stages of the data science methodology emphasize that once a model has been deployed, it is important to gather feedback to determine its effectiveness and performance in the real world. This feedback loop is critical for refining the model, addressing any issues, and ensuring that it meets the desired objectives.

In practice, after deployment, the model may need adjustments based on the feedback received from users or systems. This iterative process allows data scientists to continuously improve the model, making necessary changes based on insights gained from its performance. Therefore, incorporating feedback into the cycle ensures that the model remains relevant and effective over time.

The other options, while important in the overall data science process, do not fit as directly into the iterative cycle that occurs after deployment. Data Understanding and Data Preparation are both foundational steps that precede modeling, and Analytics generally refers to the broader process of deriving insights but does not specifically capture the iterative nature required in the context of improving a deployed model.