According to Michael Friendly’s book “A Brief History of Data Visualization,” data visualization is a graphic representation of quantitative information. Data visualization can transform seemingly arbitrary data into an easy-to-understand format. For example, this data visualization on the complete history of the NFL makes use of a graph and turns it into a timeline of the entire sport. 

Candidates seeking a degree in data science, whether by taking the master’s in data science online or in person, may want to also consider how the art of visualization pairs with the harder skills needed to analyze and organize data.

Graphs are just one of the many ways to visualize data. The following guide will help to explain the foundational concepts and processes that allow such visualizations, as well as the skills needed to make them a reality. 

Friendly also writes that data visualization dates to the pre-17th century where “visualization arose in geometric diagrams, in tables of the positions of stars and other celestial bodies, and in the making of maps to aid in navigation and exploration.” Since then, data visualization has developed exponentially. 

With the growth of technology, data scientists are able to display data in ways that may have seemed unrealistic decades ago. For instance, they can now use interactive statistical computing systems, large-scale statistical and graphics software, and linear statistical modeling. 

Big data, simply put, is “larger, more complex data sets, especially from new data sources … [that] are so voluminous that traditional processing software just can’t manage them.” Data visualization can help data scientists interpret and convey their findings from immense amounts of data by creating large-scale and 3D visualizations.

Some cool big data visualization examples include: 

A few other examples visualize big data using ways such as:

There are many types of data visualization techniques. However, some may help portray different data statistics better than others. It is important to research which strategy will best showcase your findings before making a final decision. 

2D planar graphs are graphs that can be drawn onto a plane and are done without any outer edges crossing. These graphs are particularly useful for visualizing geospatial data. The graphs that fall under the planar 2D category are:

3D volumetric visualization is a method that allows one to observe and manipulate 3D volumetric data. This includes:

These data visualization techniques would work well for conveying data on a large-scale, show possible realistic outcomes, and get a closer look without having to travel far. 

Temporal structures help convey data collected on time, change and motion. Structures that fall under the temporal category include:

Multidimensional graphs work best for conveying numbers and can be used as a visual representation for proportions and comparisons. These include:

Tree graphs may be used to display a hierarchical climb or other information that contains no cycles—most commonly to display family history. Graphs that fall under this category include:

When referring to a network in data science, researchers are referring to a set of objects that are connected together. This can include:

Data visualization can be accomplished by anyone, but it can be done accurately and more effectively if it is created by someone with the following skills: 

Data engineers may often rely on different data visualization methods to help convey their findings. However, to become a data engineer, they must be knowledgeable about the different programming and coding languages. Programming languages allow data engineers to mine and query data, and in some cases use big data SQL engines. Some of the popular languages are: 

There are multiple data visualization software solutions available, including: 

Although certain skills may not be required, having a broad understanding of data science can help to create effective and accurate data visualization. However, this is not to say users shouldn’t have any skills. Some useful skills for data scientists who want to create data visualizations include:

Having a skill set in public speaking and presenting, even if it’s minor, can help researchers feel confident when presenting their data. Seven powerful public speaking tips include:

With the development of technology also came the development of machinery. Having the ability to correctly use machines can help data scientists make more impactful data visualizations. For example, machine learning can improve data visualization by:

Data visualizations help convey a large amount of data in a way that can’t be done otherwise. Data scientists should consider acquiring the skill sets that can be used to help them successfully and accurately use data visualization.