Top Programming Languages for Data Science

Manas Singh 30th Dec 2018

The field of data science is continuously evolving at an unstoppable pace. It has been identified that multiple organizations are suffering lack of data scientist experts due to they have trouble in making quick and accurate decisions for them.

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The main role of a data scientist is to crunch and analyze huge datasets to fetch valuable insights from them. Data collected from various sources such as historical business operations, financial data, customer data, search and past experience etc. With advancement in technology complex machine learning algorithms based models are leveraged by organizations to accomplish their work in effective ways.

Tasks like visualization, classification, statistical representation, mathematical or scientific computing etc., are now becomes easier with the modules offered by several languages used in data science.

Here below, I’m discussing the few programming languages currently being used in data science:


Python is an easy and multi-paradigm programming language used for web, app, standalone, enterprise, scientific and research application development. For data scientists, Python is not less than any readymade recipe. It offers more than 1,37,000 packages for simplifying our work. This is the main reason more and more developers are willing to learn this programming language. Currently, python is the top programming languages followed by Java at second among developers.

Top libraries like numpy, scipy, pandas, matplotlib, TesorFlow, Theano etc., are the top libraries offered by Python assisting us in automation, database, text, and image processing like services. Python is currently being used in Google, Facebook, Instagram, Dropbox, Quora, Netflix, and Reddit to accomplish their work.

R Programming:

R programming language is getting famous with its increased use in data science and data mining operations to process information. It is also very popular among researchers and scientists for complex computational and statistical work.

The object-oriented programming feature of R make it more valuable than many other languages. R offers packages like Caret, randomForest, party, rpart package, MICE etc., to perform data-oriented operations. It simplifies tasks like shortest path calculation, fuzzy, bagged clustering, naive bays classification, regression.

Top level organizations like Mckinsey, Google, Facebook, Hewlett Packard, Ford, Roche, New York Times etc., are using R programming in their work culture. With R, statistical graph designing becomes a few click tasks leveraging metrics, data frames, and arrays. R is the top choice against SAS and Matlab.


Java was introduced in the 90s and is still used by top most organizations in all kind of modern development tasks. Whether it is about smart application development or very challenging scientific computation- Java will provide a solution to your problem.

Big giants like Pinterest, Google, Microsoft, Linkedin are using Java in their development environment. When it comes to data science, it is one of the oldest choices among all. With varieties of modules and packages for visualizing, interpreting and analyzing data, Java is getting more attention of the user. In Java, a written code can be executed anywhere as it is platform independent. Once your code is compiled and converted into bytecode, it is good to be get used anywhere. Its modules like Weka, Mallet, ELKI, DL4j, Java machine learning library etc., are very useful to pace up the working operations.

The Java Virtual Machine (JVM) offered recent advancements such as Lambda support and REPL is also reducing developers and data scientist’s efforts while dealing with data.


Julia is very fast and it is integrated with the Jupyter environment. It is developed to identify the data scientists need in large mathematical and computational problems. For floating point computation and linear algebra, it offers special libraries. It also has libraries like MLBase, ScikitLearn, Mocha, MachineLearning, TextAnalysis etc.


In the beginning, Scala with its cool interface was designed to execute on Java. But, with increased demand in the data science field watched its real potential in finishing such works. It’s user-friendly interface is designed to fulfill variable users' demand. You can write high-level programs over it. It offers machine learning libraries like Breeze, Saddle, Scalalab, Epic, Breeze-vis etc., for data scientists. The organization dealing with large data such as the New York Times, Courseera, Sound Cloud etc., are currently using Scala for their work.

Now, we have this much of languages to eliminate hurdles in achieving good data science results. So, what are you waiting for? Go and grab the thousands of opportunities evolving from this field.

Authored By Manas Singh

He is a continuous blogger and has blogged on different topic. He loves to surf Internet and always trying to get new Idea about new Technology and Innovations and sharing these great information to all the technology lovers.

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