5 Programming Languages Every Big Data Enthusiast Should Embrace

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There are hundreds of programming languages, each one with their own set of features and uses., but for a data professional to be a success they will probably need to become familiar with more than one of these languages.

However, some programming languages are more useful than others depending on the domain. So the question must be asked…

Is it better to be a jack of all trades or master of one?

The short answer is that you will probably specialise. In a field which requires such a diverse skill set, you should always embrace the opportunity to learn. For example, as a front-end specialist, an understanding of how back-end works will make you a better developer and might give you an edge over another candidate in the hiring process.

It’s also helpful to remember that all coding languages have a core set of concepts they are built upon. When you master the fundamentals of one language, it makes it easier to learn others. Some words of comfort I’m sure for anyone learning their first programming language.

That being said here are six programming languages that every big data enthusiast should embrace.


Widely used within the data science community, Python is an extremely popular general-purpose programming language. It is widely touted as the easiest to learn.

The demand for Python experts is rising rapidly due to the advancement of technologies such as artificial intelligence, machine learning, and predictive analytics.


R is an open source language and software environment for statistical computing and graphics. It is one of the most in-demand programming languages with recruiters of machine learning and data science. The leader in open statistical analysis, R provides many statistical models and numerous analysts have composed their applications in R.


Another skill-set with rising demand, Java is an extremely popular general-purpose programming language. Many numbers of organizations, particularly MNC organizations use this language to create backend systems. It is an Oracle-supported unique computing system that empowers portability between platforms.

Since it was developed to run on the JVM, it allows interoperability with the Java itself, making Scala a very great general purpose language while also being a perfect option for data science.


TensorFlow is a machine learning framework suitable for large-scale data. It is an excellent open source software library for numerical computation.

TensorFlow’s biggest advantage is that it allows data professionals to train huge neural networks on immense training sets in a short time. The graph can be broken into many chunks that can keep running in parallel over various GPUs or CPUs.


Scala (scalable language) is one of the best-known languages with one of the largest user bases. It is a general-purpose, open source programming language which runs on the JVM.

Since it was developed to run on the JVM, it allows interoperability with the Java itself. This makes Scala a very great general purpose language, while also being a perfect option for data science.


In-Depth knowledge of programming languages is one of the most in-demand skill-sets for data professionals. Therefore, learning any one of the above-mentioned programming languages will be a massive step in the right direction for your data science career.

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