Top 6 Languages for Data Science
Top 6 Languages for Data Science
We take a high-level look at six great languages for doing data science, and how big data professionals of all levels can benefit from them.
Join the DZone community and get the full member experience.Join For Free
The open source HPCC Systems platform is a proven, easy to use solution for managing data at scale. Visit our Easy Guide to learn more about this completely free platform, test drive some code in the online Playground, and get started today.
The 2012 Harvard business review rightly mentioned data science as "The sexiest job of the 21st-century.” Even after six years of the publication of the report, the business review stands vindicated. With the advent of artificial intelligence and machine learning, the term "data science" gained currency among the tech-savvy. In the simplest terms, data science is a way to dig out knowledge from data, either structured or unstructured, using scientific techniques and algorithms. Thus, to be a pioneer in data science programming one needs to have a good command of at least one of the supported languages.
Whether you are a newbie or a professional in the field of data science, some of the basic things you need to keep in mind include analyzing data, applying programming tools such as sequence and selection on data, and performing simple data visualizations.
6 Programming Languages Preferred by Data Scientists:
The R programming language is widely used by data miners and data scientists for analyzing data. It is also popular among statisticians to simplify their job. R offers strong object-oriented programming facilities which give it an upper hand over other computing languages. The static graphics make it easier to produce graphs and other mathematical symbols. Some of the things you can do with R are creating vectors, matrices, arrays and data frames. It serves as an alternative to SAS and Matlab. In the past few years, R has become the favorite choice for companies such as Google and Facebook.
Python is a simple, general purpose, multi-paradigm programming language. The greatest strength of Python is its huge number of libraries which can help you do a variety of tasks, such as graphical user interface, automation, multimedia, databases, text, and image processing. Moreover, it is an easy language to learn and work with. Therefore, it is the preferred language by both students and recruiters.
Java is one of the oldest choices of languages among data scientists. Although its existence has been challenged by many new languages, Java never fails to outshine them. The special feature of Java is "write once, run anywhere." Once the code is compiled, it can be run on any platform which supports Java. Thus, portability is one of the great facets of this language. The Java virtual machine (JVM) is a great tool for data science. If we look at the recent developments in Java, there have been two great improvements: Lambda support (which helps in reducing verbosity) and REPL support. Therefore, Java is a must-learn for budding data scientists.
Scala has a large user interface. Initially, it was designed to run on Java. All the platforms which support Java can also run Scala. It is user-friendly and engineered to be changed as per the demands of users. Hence, it is ideal for coding high-level algorithms.
Structured Query Language (SQL) is used to deal with large databases. In particular, it is helpful in managing structured data. Learning SQL can be a good addition to the language skills of data scientists. The drawback associated with this language is the lack of portability.
Julia has been designed to address all the numerical and computational needs, hence it is ideal for data scientists. The special feature of this language is a library that's good for floating point calculations and linear algebra.
Opinions expressed by DZone contributors are their own.