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. Exploring data offers a number of operations that may be performed to sort and generate data, modify, merge, and accurately distribute data sets to get it ready for its final representative formatting. Lastly, data visualization is another point in which R specializes, with a number of packages that aid in graphically representing results with charts and plots, including complex plotting of numerical analysis.
Java has been around for about a quarter of a century and during this time, the class-based, object-oriented language has adhered to the “write once, run anywhere (WORA)” creed, establishing it as requiring as few dependencies as possible—regardless of where its code will run. This extends to applications run within the Java virtual machine (JVM), which can be run regardless of the underlying OS, remaining largely system-agnostic. It is the platform of choice for some of the most widely used tools in big data analytics, such as Apache Hadoop and Scala (more about Scala below). Its mature machine learning libraries, big data frameworks, and native scalability allow for accessing nearly unlimited amounts of storage while managing many data processing tasks in clustered systems.
Compared to the other programming languages on this list, Julia is the newest language with less than 10 years since its initial release. But you’d be mistaken if you confuse that with a lack of maturity because despite being among newer languages, Julia is steadily growing in popularity among data scientists that require a dynamic language capable of performing numerical analysis in a high-performance computing environment. Thanks in part to its faster execution times, it not only provides speedier development but produces applications that run similarly to those created on low-level languages, like C for example. One relatively small downside to Julia is that the community is not quite as robust as that of other languages, limiting support options–however, that’s part of the growing pains of any newer technology that will work itself out as the technology grows.
A high-level programming language that is based on the JVM platform, Scala was designed to take advantage of many of the same benefits as Java address some of its shortcomings. Scala is intended to be highly scalable and as such, perfectly suited to handling the complexities of big data. This includes compatibility with high-performance data science frameworks based on Java, such as Hadoop, for example. It also makes for a flexible, highly scalable, open-source clustered computing framework when paired with Apache Spark and able to make use of large hardware resource pools efficiently.