What does the data science methodology leverage for continuous improvement?

The data science methodology relies significantly on incorporating feedback and making adjustments as a means of continuous improvement because this iterative process allows for the refinement of models and results based on new insights and data that arise throughout the analysis. Continuous feedback leads to better understanding the problem and enhances the effectiveness of the solutions developed.

This adaptive approach is fundamental in data science, where understanding can evolve as additional information becomes available or as the environment changes. Engaging with stakeholders to gather their insights can also influence how the models are adjusted, thus ensuring that the outputs remain relevant and valuable. Ultimately, this practice leads to more accurate predictions and more successful outcomes.

The other choices reflect approaches that can hinder growth and learning in data science. Automating the entire process might streamline some tasks but does not inherently incorporate adaptability, while relying solely on expert opinions can lead to biases and may not consider changing dynamics or data. A linear and rigid methodology would restrict the flexibility needed to adapt to evolving datasets and insights.