This is because visualizations of complex algorithms are generally easier to interpret than numerical outputs. When a data scientist is writing advanced predictive analytics or machine learning ( ML) algorithms, it becomes important to visualize the outputs to monitor results and ensure that models are performing as intended. Visualization is central to advanced analytics for similar reasons. As businesses accumulated massive collections of data during the early years of the big data trend, they needed a way to quickly and easily get an overview of their data. It also plays an important role in big data projects. It can be used by teachers to display student test results, by computer scientists exploring advancements in artificial intelligence (AI) or by executives looking to share information with stakeholders. Data visualization is also an element of the broader data presentation architecture (DPA) discipline, which aims to identify, locate, manipulate, format and deliver data in the most efficient way possible.ĭata visualization is important for almost every career. The term is often used interchangeably with others, including information graphics, information visualization and statistical graphics.ĭata visualization is one of the steps of the data science process, which states that after data has been collected, processed and modeled, it must be visualized for conclusions to be made.
The main goal of data visualization is to make it easier to identify patterns, trends and outliers in large data sets.
Data visualization is the practice of translating information into a visual context, such as a map or graph, to make data easier for the human brain to understand and pull insights from.