6 Useful Programming Languages for Data Science You Should Learn (that are not R and Python)

Karthe 14 Jun, 2020
8 min read


  • Which programming language should you pick for data science? Here’s a list of 6 powerful ones that are not Python or R
  • These languages are vast in their scope and are commonly used in the data science field
  • We have also provided open-source libraries for each language to help you get started with various stages of a data science project, such as data cleaning, model building, etc.



“Which programming language should I pick up to start my data science journey?”

If I started handing out nickels for each time I saw this question – there would be a lot of millionaires! It’s easily the most popular question asked by data science enthusiasts. The answer, I’m sure you’ve seen, usually hovers between Python and R.

But here’s my question – why should we limit ourselves to these two languages? There is a whole world of programming languages we can pick up and apply in this field. And therein lies the beauty of data science – it transcends programming languages.

My aim here is to introduce a world beyond Python and R while keeping the core idea behind them. We will cover 6 powerful and useful programming languages for data science that I feel every data scientist should learn (or at least be aware of). All of these languages are open source.

data science languages

And let’s face it – we love comparisons. Whether it’s Apple v Samsung, iOS v Android, MacOS v Windows (or Linux), these comparisons lead to intense discussions. So if this article sparks a debate among our community – that’s even better!

So, what are these languages and how they are used in the field of data science? Let’s find out!

Note: I have also provided open source libraries and free tutorials wherever possible to help you get started with each programming language.



scala data science

Scala is a fairly common programming language. Chances are you’ve either worked on it or come across it at some point (especially if you’ve worked in IT).

Scala is an open source modern multi-paradigm programming language created by Martin Odersky in 2003. Scala stands for “Scalable Language”. It is designed to express common programming standards in a brief, elegant and type-safe way.

Let’s put it this way – if you are aware of Java’s syntax, you’ll pick up Scala in a jiffy. In fact, learning Scala will be pretty smooth if you know programming languages like C, C++ or Python. I can already see your enthusiasm starting to light up!

So, why Scala? Well, the code we write in Scala is compiled and executed much faster as compared to pure Python (and not specialized libraries like NumPy). I love Scala because of its stability, flexibility, high speed, and scalability. You can use Scala to develop useful products that work with Big Data.

Interested in learning Scala? We have the perfect article for you:


Top Scala Libraries for Data Science

  • Breeze: Breeze is a library for numerical processing, like probability and statistic functions, optimization, linear algebra, etc.
  • Vegas: Scala library for data visualization.
  • Smile: Statistical Machine Intelligence and Learning Engine (Smile) is a modern machine learning library.
  • DeepLearning.scala: It is a simple library for creating complex neural networks from object-oriented and functional programming constructs.



julia for data science

Julia is coming up big right now in the data science world. If you didn’t know this already, it’s time to get on board. A few experts are already claiming it as a rival to Python! It might be a little too soon for that but it gives us an idea of how useful Julia is.

Julia is a refreshingly modern, meaningful and high-performance programming language created by a group of computer scientists and mathematicians at MIT. It is open source and is commonly used for scientific calculations and data manipulations.

You’ll pick up Julia quickly if you’ve worked on R, Python or Matlab before. There even exists a scikit-learn library in Julia to help your transition. What else could a data scientist ask for?

Again the question comes up – why Julia for data science? There are multiple reasons but the primary one is that the execution speed of Julia is 10x-30x than that of Python and R.

You can refer to the below article to learn Julia for data science from scratch:


Top Julia Libraries for Data Science

  • DataFrames.jl: Data structure to find numerical patterns in data.
  • Plots.jl: This is used for plotting APIs and toolsets.
  • ScikitLearn.jl: ScikitLearn.jl is the Julia version of the popular Scikit-learn library. It is a very popular option for building ML solutions.
  • Mocha: Mocha is a Deep Learning framework for Julia, inspired by the C++ framework Caffe.



javascript for data science

Calling all developers! If you were looking for a way into data science without wanting to learn a new language – JavaScript is your pathway to the jackpot.

JavaScript is a powerful, lightweight, and easy-to-implement programming language. It was first launched in Netscape 2.0 in 1995 under the moniker LiveScript.

It’s good to have some basic knowledge of HTML and prior exposure to object-oriented programming concepts if you want to pick up JavaScript. This will give you a basic idea of creating online applications. This comes in especially handy when you’re deploying your machine learning models in mobile apps or in the browser.

Apart from this, JavaScript has some excellent libraries for data visualization and creating dashboards. Various machine learning techniques like gesture recognition, object recognition, music composition, etc. can be executed using TensorFlow.js, a powerful JavaScript library for data science.

You can get started with machine learning in the browser by following the steps mentioned in the below article:


Top JavaScript Libraries for Data Science



swift for data science

Are you an Apple fan? Do you love using their various devices and their tightly-knit iOS? Well, then you’ll love Swift.

Swift is an open source, easy, and flexible programming language developed by Apple for iOS and OS X apps. Swift builds on the best of C and Objective-C, without the constraints of C compatibility. It’s actually a friendly programming language for freshers because of its concise yet expressive syntax and lightning speed to run the apps.

Swift has recently started gaining traction among the data science community. It is highly endorsed by Jeremy Howard (fast.ai’s co-founder). There are various libraries for performing tasks like numerical computation, high-performance functions for matrix math, digital signal processing, applying deep learning methods, building machine learning models, etc.

Refer to the below article to learn more about Swift for TensorFlow:


Top Swift Libraries for Data Science


Go (Golang)

go for data science

How could Google ever stay out of any data science related discussion?

Go, as the name suggests, is a programming language created by Google. Simple, reliable, and efficient software – that’s Go in a nutshell. What I like about Go is its singular focus. It keeps conflicts at bay by focusing on one method at a time (as opposed to other languages where there are multiple ways to solve a problem).

There are a great number of open source tools, packages, and resources for performing data science tasks using Go. This includes data gathering, data organization, data parsing, arithmetic and statistical computations, EDA and building machine learning models, etc.

Check out the below discussion to learn more about the important libraries in Go:


Top Go Libraries for Data Science

  • Math: This package provides basic constants and mathematical functions.
  • Dataviz: Build and Visualize data structures in Golang.
  • GoLearn: General Machine Learning library for Go.
  • Gorgonia: It smoothes machine learning tasks and provides a platform for the exploration of non-standard deep-learning and neural network related things.



spark for data science

Spark is more of a framework than a language but you’ll soon see why it’s on my list. It is very popular among data engineers and data scientists.

Spark provides:

  • High-level Application Programming Interfaces (APIs) in Java, Scala, Python and R, and
  • An optimized engine that supports general execution graphs

It is an open source, fast cluster computing framework which is used for processing, querying and analyzing Big Data. The advantage of Spark over other big data frameworks is that it is based on in-memory computation. This enables computations to run up to a hundred times faster.

Basic knowledge of Python is good enough for you to pick up Spark quickly.

Spark can perform various data science and data engineering tasks, such as:

  • Exploratory data analysis
  • Feature extraction
  • Supervised learning
  • Model evaluation
  • Building and debugging Spark applications, etc.

Here’s the perfect article to learn Apache Spark:


Top Spark Libraries for Data Science

  • Spark SQL: It is Apache Spark’s module for working with structured data.
  • GraphX: GraphX is Apache Spark’s API for graphs and graph-parallel computation.
  • MLib: MLlib is Apache Spark’s scalable machine learning library.
  • Spark NLP: John Snow Labs Spark NLP is a natural language processing library built on top of Apache Spark ML.


End Notes

Don’t you love how vast the field is for data science languages? Python and R are wonderful in their own right. But my aim here was to bring out other languages that we can use to perform data science tasks.

Some of these languages you might even know right now (I’m sure all you developers are aware of JavaScript!) – you just didn’t realize you could use it for building awesome visualizations and designing models. Well, now you do!

Any language(s) you feel I should have included in the article? Connect with me in the comments section below. I look forward to hearing your thoughts, suggestions, and feedback!

Karthe 14 Jun, 2020

Frequently Asked Questions

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Responses From Readers


Ayan 24 Jun, 2019

I'm a B.Pharmacy graduate. Is it a right choice for me to select Data science to advance my career?

Prudhvi Cuttamanchi
Prudhvi Cuttamanchi 24 Jun, 2019

Java has it's label everywhere and it's a very good competitor to python in all the ways but except in data science....could you please explain where java is not strong enough to support data science.... Thank you....

Priya 24 Jun, 2019

A very precise, well put article. Thank you, much appreciated!

Madras Satta
Madras Satta 24 Jun, 2019

In among all i know only JavaScript, And with the help of html css i created my website. Your blog is always useful to be updated with the market. I follow it.

Vinnie 24 Jun, 2019

Thanks for the information!!!! But, leaving python means; you're pushing yourself away from data science.

Joel 24 Jun, 2019

I'd add VBA. One of the the least known secrets of Data Science is that Microsoft Excel is an excellent tool for doing Data Science. Sometimes a spreadsheet with a short macro is easier than a long Python script.

Wilham Monger
Wilham Monger 24 Jun, 2019

Nice info

Siddharth 24 Jun, 2019

Missing Java? Tensorflow API has already gone to version 2.0.

Ravi Yadav
Ravi Yadav 24 Jun, 2019

Thank you so much

Vignesh 24 Jun, 2019

How to get in beginners to complete this all session how many times take 2-3 years ? Where I can learn help me please

Subramaniam 24 Jun, 2019

Thanks a lot for sharing...nice information.it helps a lot for freshers like me who are passionate about data analytics.

Shachee 24 Jun, 2019

IMHO, I don't think anyone can and should learn so many languages! Could it become a case of Jack of all trades and master of none?

Sagar 25 Jun, 2019

Nice knowledge for programs

Surya 25 Jun, 2019

Very much useful. Appreciate your work. Thank you

Harish Sridhar
Harish Sridhar 25 Jun, 2019

I just came to know there are many many languages! Keep up the good work. Thank you!

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