Why is data visualization considered necessary in data science?

Data visualization is considered necessary in data science primarily because it allows data scientists and stakeholders to effectively visualize trends and patterns within the data. When dealing with large and complex datasets, raw numbers and text can be challenging to interpret. By transforming data into visual formats, such as charts, graphs, and infographics, it becomes significantly easier to comprehend relationships, detect anomalies, and identify trends over time.

This visual interpretation helps in communicating insights more effectively to both technical and non-technical audiences. It can also highlight correlations that may not be immediately obvious from numerical data. The ability to see data represented visually supports better decision-making processes and enhances the overall understanding of the underlying stories the data tells.

While other choices mention aspects like data storage and redundancy, they do not directly relate to the core benefits of visualization in analyzing and interpreting data, making the focus on trends and patterns fundamental to the art and science of data analytics.