Photo by Georgia Howard

Especially in this digital age, information is everywhere. A quick Google search can easily yield massive databases of information, all readily available to the public. However, without the proper education on how to analyze and use data, the existence and availability of this information are wasted.

The introduction of a data visualization class to each student’s curriculum is therefore necessary, not only to teach the fundamentals of data analysis, but also to show students how to effectively present data.

Strong visualizations will enable users to determine unlikely connections and draw unforeseen conclusions, leading to innovative solutions to problems as well as new discoveries.

By playing on the universal nature of visualizations, students can foster collaboration between people from different corners of the world. These visualizations can reveal patterns and relationships in the data that lead to a more holistic representation of a topic.

For example, Windmap, created by Fernanda Viégas and Martin Wattenberg, depicts wind patterns across the U.S. Windmap was initially created for “artistic exploration.” However, the maps were later used by bird watchers who wanted to track avian migration patterns, by bicyclists who wanted to plan bike trips in optimal wind conditions and by conspiracy theorists who wanted to track mysterious chemicals in the air.

Furthermore, visualizations can also help make sense of large collections of scientific data. The Broad Institute of MIT and Harvard has enacted a data visualization initiative in which they seek to “help scientists find patterns in quantitative data to address biological questions” through a combination of words, numbers and images.

DataStream, a product of the Broad Institute’s initiative, uses the data collected by scientists at the institute to visualize and pinpoint where, amongst the billions of data points, differences occur between comparable genome and DNA data.

Other analyses of large, complex data sets can help analysts identify effective solutions to problems. For instance, Cycle Atlanta collects information about local cyclists, for example age, ethnicity and routes taken, among other information, in order to find out where there is a need for additional allocation of attention and resources from the City of Atlanta to create a safer, more efficient environment for cyclists.

Successful visualizations enable the exploration of data. Requiring students from diverse backgrounds to take a data visualization course will pave the way for an assortment of new discoveries and valuable insights to be found within various fields of study. Since improper analyses of data can lead to inaccurate or biased conclusions, correct methods of analyses and visualization must be taught in order to insure that the resulting conclusions are credible.