Top 5 programming languages data scientists should learn

Data scientists working with large data sets or in high-performance computational environments may find these programming languages essential to extracting data quickly and effortlessly.

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 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.

Lauded by software developers and data scientists alike, Python has shown itself to be the go-to programming language for both its ease of use and its dynamic nature. It’s mature and stable, not to mention compatible with high-performance algorithms, allowing it to interface with advanced technologies such as machine learning, predictive analysis, and artificial intelligence (AI) through rich, supported libraries in its extensive ecosystem. In addition to its strengths as a deep learning language, Python also enjoys almost unparalleled support across a variety of operating systems to aid in data processing from almost any source natively.

R is often compared to Python in that its inherent strengths are similar due to its open-source nature and system-agnostic design to support most operating systems. And while both languages excel in data science and machine learning circles, R was designed by and leans heavily into statistical models and computing.