Data scientists who use massive sets of data or work in high-powered computing environments may see the following coding languages as necessary for making data extraction effortless and fast.
The field of data science is focused on pulling insights from data. Take for example the COVID-19 pandemic: Officials in the government were tasked with analyzing sets of data from various sources, such as contact tracing, infection rates, rates of mortality, and data based on location to analyze which places are impacted more and the best way lifestyles in those places in order to curb infections.
Big data, as it’s usually referred to, is the collection of large data sets pulled from multiple places. This data is usually massive in size, made of a large variety of types, and run at a high velocity (the rate of collection). This results from the huge growth in the movement to digitize the data available globally as well as the push to increase storage capacity, manipulate data of this size, and analyze amounts of data of this magnitude.
The science of data, as envisioned by Jim Gray, a recipient of the Turing Award and scholar of computer science, argued that the “4th paradigm” in science should be “driven by data” after the paradigms of empirical, theoretical, and computational. Keeping this in mind, the software programming languages listed below are in the perfect position to make them efficient at how they handle massive sets of data and robust in bringing together of disparate sources of data to extract the necessary info to yield insight about what is contained within streams of data as well as for data mining and machine learning, among others.
People in both software development and data science utilize Python, which has proven itself to be a go-to coding language because of its dynamic nature and how easy it can be to utilization Python. It’s stable and mature, as well as useful for writing high-performance algorithms. Python can interface with predictive analysis, machine learning, and artificial intelligence (AI) via rich, supported libraries. In addition to being a programming language for utilization with deep learning, Python also enjoys close to unparalleled support across a various operating system softwares to assist in the processing of data natively from nearly any source of data.
People in the data science community regularly compared R with Python because of inherent strengths that are similar between the two as a result of both programming languages being system-agnostic in design (supporting most operating stems) and open-source. And although both R and Python are wonderful in the data science and machine learning communities, R was created for data science and leans towards statistical models and computing. For exploratory analysis of data, R provides users with an array of operations that may be done to generate data and sort it, merge the data, modify the data, and appropriately distribute sets of data in preparation for its final representative formatting. Lastly, R specializes in the realm of data visualization, with an array of packages to assist in graphical representation of results with charts and plots, which includes complex plotting of things from numerical analysis.
People have used Java for approximately a quarter of a century. During the time that Java has existed, the object-oriented, class-based language has followed the creed of “write once, run anywhere (WORA)”. This principle establishes Java as needing as few dependencies as possible. This principles also extends to Java apps run on a Java virtual machine (JVM), which can be used no matter the underlying OS. As a result, Java remains largely a system-agnostic software language. The platform of choice is Java for some of the most utilized tools in the realm of big data analytics. Java is used with such data science tools as Scala and Apache Hadoop. Java is a programming language with mature libraries for machine learning, big data frameworks, and native scalability which allows for utilizing almost unlimited storage capacities while managing many tasks related to data processing on clustered systems.
When compared with other languages for programming in data science on this list, Julia is the youngest of the languages with fewer than ten years since being initially released. You would be incorrect to confuse this newness of Julia with a lack of maturity. Despite being a newer programming language, Julia is steadily gaining popularity with data scientists who must use a dynamic language that is capable of doing numerical analysis within an environment for high-performance computations. Thanks to having quicker execution times, Julia not only offers faster development, but Julia also yields data science apps that run like those built with low-level languages, such as C. One small downside to using Julia is that its community of users is not as robust as the communities of users of other programming languages. The smaller community of users limits the support options for Julia, but that’s one of the growing pains with any newer technology. This growing pain will work itself out as more and more people make use of the Julia.