What is the main purpose of using data visualization in data science?

The main purpose of using data visualization in data science is to simplify complex data relationships. Data visualization transforms intricate datasets into graphical representations, making it easier for analysts and stakeholders to see patterns, trends, and correlations that might not be readily apparent in raw data.

Visual representations can highlight relationships between variables, show the distribution of data, and depict changes over time, all of which facilitate a clearer understanding of the underlying information. By simplifying these complex relationships, data visualization enables more informed decision-making and insights to be drawn from data analysis, which is crucial for effective communication and comprehension of data-driven narratives.

The other options do not accurately reflect the primary role of data visualization. It does not intend to replace statistical analysis, but rather to complement it by providing visual insights. Additionally, while it is important to present data clearly, masking data complexity or merely aiming to impress stakeholders distracts from the core function of visualization, which is to enhance understanding and accessibility of data.