Data science is a field focused on extracting knowledge from data. Put into lay terms, obtaining detailed information applying scientific concepts to large sets of data used to inform high-level decision-making.Take the ongoing COVID-19 global pandemic for example: Government officials are analyzing data sets retrieved from a variety of sources, like contact tracing, infection, mortality rates, and location-based data to determine which areas are impacted and how to best adjust on-going support models to provide help where it is most needed while trying to curb infection rates.

Big data, as it is often called, is the collective aggregation of large sets of data culled from multiple digital sources. These swaths of data tend to be rather large in size, variety (types of data), and velocity (the rate at which data is collected). This is due to the explosive growth and digitization of information globally and the increase in capacity to store, handle, and analyze data pools of this magnitude.

Data science, as imagined by Jim Gray, a computer scientist and Turing Award recipient, believed it to be the “fourth paradigm” of science–adding data-driven after empirical, theoretical, and computational. With this in mind, the five programming languages below are poised to be efficient in their handling of large data sets and robust in their coalescence of multiple data sources to effectively extract the information necessary to provide insight and understanding of the phenomena that exist within data streams for data mining and machine learning, among others.

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