How does Dr. Vincent Granville define a data scientist's reliance on statistical models?

Dr. Vincent Granville defines a data scientist's reliance on statistical models as one where a data scientist combines various methods for analysis. This approach reflects the complexities of real-world data and acknowledges that no single method can capture all aspects of a dataset effectively. By integrating different techniques—be it statistical models, machine learning algorithms, or data mining methods—data scientists can leverage the strengths of each approach to derive more accurate insights and predictions.

This multifaceted reliance is essential because data analysis often involves working with different types of data, uncertainties, and various analytical goals. Therefore, employing a combination of methods allows for a more comprehensive understanding of the data and improves the robustness of findings. In practice, this means that a data scientist is not limited to one technique but is adaptive and resourceful in employing the best available methodologies to address specific challenges in their analyses.