With the constant evolution of data science, you need to be skilled in cutting-edge technologies in the field. In this article, we will look at the top programming languages used in data science.

Data has become enormously valuable in the last decade.

Every big company out there has valuable data that, with the help of a good data scientist, can benefit the way they do their business. In other cases, pinpoint strategies that may not be working that well.

The industry is expanding, and the demand for data scientists is increasing.

If you want to become a data scientist, you should begin by learning the top programming languages in the field.

Let's look at the most used languages in Data Science and why you should use them.


Nowadays, Python is the most used programming language. Several programming languages indices like PYPL and TIOBE confirm this.

Most used programming languages table by PYPL.

Python is one of the most powerful and flexible languages out there, and it's also vastly used in data science. The main reason is its easy and elegant syntax, along with a large collection of third-party libraries.

A tool that you'll find everywhere in the data science field is Jupyter.

With Jupyter notebooks, you can quickly see the results of the code you're working with, plot data, and create documentation of your code via markdown blocks.

This is not a Python-only tool, but the most common combination is Python and Jupyter.

Jupyter notebook

Python's community is always friendly with newcomers. You'll always have forums and sites like Stack Overflow to solve your doubts.

If you want to start learning this language, we have the perfect Python learning resource list for your purposes.


R is an open-source programming language first introduced in 1993 used for statistical computation, data analysis, and machine learning.

According to a Stack Overflow analysis, R's popularity has been increasing over the course of the last couple of years.

R's increasing popularity

Although R is widely used by researchers, it's nowadays being used by Big tech companies like Google, Facebook, and Twitter, for purposes related to data analysis and statistics.

We could talk for hours about the advantage of this language.

R, just like Python, is an interpreted language, so you can run your code without the need for any compiler. At the same time, R is cross-platform, so you don't need to worry about your OS.

R is such a popular language that you have plenty of editors and IDEs to choose from. But for many years, RStudio has been the most popular IDE for R development.


You can go beyond the conventional statistics usage. With R, you have access to an immense repertoire of libraries that let you build applications of any kind. For example, with the Shiny package, you can develop aesthetic web apps from the comfort of your R IDE.

If you're into statistics or research, using R should be a no-brainer.


Julia takes the best from languages like Python, Ruby, Lisp, and R, combines it with the speed of C, and includes familiar mathematical notation just like Matlab.

We can refer to Julia as the ambitious attempt of creating a language good enough for general programming while being astonishing in specific disciplines of computer science, such as machine learning, data mining, distributed and parallel computing.

One of the main advantages of Julia is its speed, being comparable to languages like C, Rust, Lua, and Go. This is because it's Just-In-Time (JIT) compiled.

Julia benchmarks

For the last few years, Julia has dramatically increased its user base. We can see this in the number of accumulated downloads as of 2022.

Julia is incredibly good at data science because:

  • The language is easier to learn for mathematicians. It uses a syntax similar to math formulas used by non-programmers.
  • Automatic memory management with manual control over the garbage collector.
  • Optimized for machine learning and statistics out of the box.
  • Dynamic typing, almost as if it were a scripting language.
  • Multiple Julia libraries to interact with your data (DataFrames.jl, JuliaGraphs, among others).

Julia's community is so vigorous that they created a song in honor of this language.

If you want a language with support for data science out of the box, the ease of use of Python, and the speed of C, Julia is your language of choice.


Scala is a high-level programming language first introduced in 2004 that runs in the JVM (Java Virtual Machine) or with JavaScript in your browser.

It was created to improve some aspects that Java programmers considered tedious and restrictive. Among these improvements, we find the incorporation of functional programming aside from the already familiar object-oriented paradigm. It's likewise a plus that Scala is a faster language compared to Python or even Java itself.

Many data scientists have incorporated Scala into their toolset because it is invaluable when talking about the analysis of large datasets.

According to the Stack Overflow 2021 survey, Scala is the 7th most paid language worldwide. But you have to be careful with this statistic since Scala jobs are not that common in the industry.

Because Scala runs on the JVM, you'll have access to a ton of existing libraries and some Scala-only packages used in big data, math, databases, and computer science in general.

If you're already fluent in Java, Scala could be the right language for transitioning into data science.

Here's the official tour so you can start this adventure right away.


Java has been one of the most used and loved programming languages for decades. It's an all-around language that can be used in almost any imaginable situation.

Data science is not an exception. Although Java is primarily used in mobile and web applications, because of its strong user base, it's being used along with other popular frameworks such as Hadoop or Spark to do heavy data analysis.

In conclusion, more than talking about Java as the best fit for data science, we should realize that due to the number of Java developers out there and the companies that already have their software written in it, it's more comfortable doing everything in the same language.

Java usage over the years

With that being said, Java is usable in most fields of data science, such as database management, machine learning,

If you know Java, it's much easier to learn a couple of libraries than to learn the usage of a completely different language like R or Julia.


MATLAB is a proprietary programming language used by millions of engineers and scientists for math and statistical computing.

Data scientists mainly use this language for data analysis and machine learning. The best part is that you have everything in one workspace.

It is mostly used in academics, but it's still a great choice to build a deep foundation on data science concepts.

The only downside of MATLAB is that it's a paid software, so you would mostly use this language if you're enrolled in a university or already using it at your job.

Check the official MathWorks resource list to start your learning path today.


To finish this list up, we have C++. Although it's mainly used for creating applications and operating systems, we couldn't have seen the modern boom of data science without it.

Data scientists prefer easy-to-use and debug languages like Python or R because they don't want to spend time fixing some strange C/C++ bug.

However, C++ has a major role in data science because many libraries used across other languages are written in it. Creating a machine learning model takes computational effort, so using an efficient language like C++ makes sense.

If you want to participate in the data science industry by developing libraries for other languages, C++ may be the right choice.


In this post, we explored the top used programming languages for data science. This field is growing explosively and today is the perfect moment to start your career as a data scientist.

If you're just starting, I would recommend you start either with Python, or R. Once you've got some real-world experience creating projects, you can begin to expand your toolset by learning other languages like Julia or Scala.

No matter what you choose, remember that creating a portfolio is the way to get a high-paying job in tech, but you have to start from something. What about these data science learning resources?

Happy Coding!