There are more options than you may think.
One of the essential skills one needs to master to get into any data science branch is programming. Now, if we overlook how confusing it is to start learning data science, choosing a programming language to use on your learning journey is an entirely independent challenge.
There are many things that you need to consider when choosing a programming language. Which one will perform better in the field you’re targeting? Has a better future? Supports more features? And if you’re a beginner or on a time-limit, which is easier to learn?
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All these are valid questions; some are more important than others based on the person and their current situation. If you lookup data science tutorials and programming languages online, 99% of the results will be either in Python or R.
Python and R are great options to use when learning or building data science applications. So, it makes perfect sense that they will be the most commonly used, with Python being more popular than R. The reason Python is very popular for data science and many other fields is that it is easy to learn and very versatile.
I am not trying to push you away from Python or R; in fact, I am a huge Python fan myself. Instead, I just want to show you that you have options; there are other programming languages out there that you can use on your learning journey, and they are fairly easy to learn, just like Python.
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JavaScript
JavaScript is the #1 web developing programming languages. It is one of the most popular languages of all time. The question is, can it be used to build data science applications?
Although Python and R have more advantages in having a massive amount of libraries and packages designed, especially for data science, JavaScript can offer a few things to the field. JavaScript has many frameworks that can make it more powerful, such as Hadoop, which also runs on Java, and Java is one of the languages the can be used to build data science applications.
Maybe JavaScript is not yet that powerful on itself to be used for building large applications. However, it can be paired with Python or R to create better and clearer visualizations than any Python or R can produce.
If you’re interested in learning JavaScript for data science, check this course.
Scala
Another language that would make a good option for data science is Scala. Scala is a modern and simple programming language that was first introduced in 2003. It was originally designed to solve some problems with Java.
This language can be used to build a wide range of applications, from web apps to data science and machine learning applications. Moreover, Scala supports object-oriented programming and can be used to create large scale data science projects because of its efficiency when dealing with large datasets.
Perhaps the only downside to Scala is, the typing in it gets a bit complex to fully understand because of its dual nature of being both functional and object-oriented.
But, it still offers a great option for many machine learning applications. A resource to learning Scala for data science is this course by cognitiveclass.ai.
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Julia
The Julia language was created in 2009 by a four-person team and made available to anyone to use in 2012. Julia was originally designed to be as simple as Python but without its disadvantages.
Mainly, Julia was designed to overcome Python slowness by being a compiled language with fast numerical analysis and outstanding performance with computational science tasks. You can use Julia to quickly implement mathematical concepts like regression and vectors/ matrices manipulation.
Moreover, it combines the strength of static-typing or the flexibility of dynamic-typing. Julia also can quickly call Python libraries and interact with Python code using the PyCall library. But, in my opinion, the best thing about Julia is that it has a full-featured debugger that is simple to use and makes finding problems in your code simple and fast.
Many great resources focus on how Julia can be useful for data science, including the Julia for Data Science book and the Julia for Beginners in Data Science program on Coursera.
MATLAB
MATLAB (Matrix Lab) is a full environment language for technical and scientific computing. This language integrates writing simple code and effective visualization in one environment that can be used either online or offline, making it easy to write and share code with others.
Since it is based on matrices, it makes it easier to implement all types of computational maths, such as data analysis and exploration, modeling and simulations, scientific graphics, and building user interfaces.
This language is great for data science because it can simulate real-life systems and interact with physical-world objects like data from sensors, images, or videos. Moreover, it offers multiple packages for practical machine learning and statistical analysis. In addition to image/ video processing and various types of system optimization.
MathWorks, the MATLAB creators, offers a practical course on how you can use it for data science on Coursera.
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Go
The last language on this list is the famous Golang. Go is a statically typed, compiled programming language designed at Google. When you read Go code, you will see that it’s very similar in syntax to C, but it offers more memory safety, structural typing, and garbage collection.
Go offers the support needed for the basic and advanced data science applications such as data gathering, cleaning, and analyzing by having its own unique libraries and having API support for the commonly used packages such as Mongo and Postgres. It also has libraries for various maths functionality, EDA, visualization, and machine learning.
An great advantage of Go is its supportive and welcoming community that is willing to offer help and support for newcomers curious about the language and its usages.
My favorite book to learn the Go language for data science is Machine Learning With Go.
Takeaways
There are over 200 programming languages out there for any applications you might think about. But, when it comes to data science, there are two clear winners, Python and R. It makes sense why they win, mostly Python, as it has more the 60,000 libraries and 8 million users worldwide. It’s a way to learn, simple to read, and very versatile.
That’s why it is a clear choice for anyone starting with data science. Python is not the only reasonable option for data science; however, it is difficult to see any other programming languages since it shines very bright.
Mastering a programming language is an essential first step to learning any branch of data science; having choices is always right; not everyone likes Python; some faster languages or languages are just better at creating compelling and clear visualizations.
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In this article, I showed you 5 programming languages that you can use for data science, some of which are as cool and fixable as Python. If you’re new, give all of them a try and see which suits you better, and if you’re already in the field, learning a new language is always a good thing to up your skills and open up new career perspectives.
