Data Visualization

- Overview

Data visualization is the practice of transforming information into a visual environment, such as a map or graph, to make it easier for the human mind to understand data and draw insights from it. The main goal of data visualization is to more easily identify patterns, trends, and outliers in large datasets. The term is often used interchangeably with other terms, including infographics, information visualization, and statistical graphics.

Data visualization is one of the steps of the data science process, which states that after data is collected, processed, and modeled, it must be visualized to draw conclusions. Data visualization is also an element of the broader discipline of Data Presentation Architecture (DPA), which aims to identify, locate, manipulate, format and deliver data in the most efficient way possible. 

Data visualization is important to almost any career. Teachers can use it to display test results for students, computer scientists can use it to explore advances in artificial intelligence (AI), or executives who want to share information with stakeholders. It also plays an important role in big data projects. As businesses accumulated large amounts of data in the early days of the big data trend, they needed a way to quickly and easily get an overview of the data. Visualizers are a natural fit. 

For similar reasons, visualization is at the heart of advanced analytics. When data scientists write advanced predictive analytics or machine learning (ML) algorithms, it becomes important to visualize the output to monitor results and ensure the model is performing as expected. This is because visualizations of complex algorithms are often easier to interpret than numerical outputs.