Most people associate data science with the Python and R languages, right? Well, it turns out that there’s an entire universe of other data science languages and tools out there that are just as great for specific areas of data science. If you’re new to the field or have been around for a while, learning a new language or tool in this space can add some major impression to your skill set. In this blog, we’ll discuss everything you need to know about alternative data science languages and also tell you about 15 alternative data science languages that you can use in your career.
What Are Data Science Languages?
First things first, what are data science languages? Well, they’re the programming languages data scientists use to wrangle, analyze, and visualize data. They form the bedrock of anything from basic descriptive statistics to building complex machine learning models. Just like a good chef has different knives for different jobs, you’ll need a selection of languages and tools in your arsenal to tackle the multitude of problems you’ll face as a data scientist.
Now, you might ask, “Why not just stick to one language?†Think about building a house with only a hammer. You probably can do it, but it would be an inefficient struggle. Well, data science is not so different – different languages are designed for different purposes and strengths. Some languages are great at heavy numerical computing, while others are more focused on building scalable systems or even producing very nice visualizations.
At the end of the day, data science languages are just the mechanisms we use to extract insights from data. The more tools you have at your disposal, the more problems you can solve, the easier your workflows will be, and ultimately, the sharper a data scientist you’ll be.
Also Read: Data Engineer vs. Data Scientist — Everything You Need to Know
Why Are Data Science Languages Important?
So why should you care about these languages in the first place? Well, the truth is that the importance of data science languages cannot be overstated. They are essentially the very tools upon which all data science work is performed. Regardless of whether you’re cleaning up data, doing some kind of statistical analysis, or even building AI tools – the language that you opt for can vastly influence both the efficiency as well as accuracy and scalability of your work.
Picture a situation where you are given a task to analyze a very large dataset with millions of rows. If you are using a language that is not optimized for big data, you may end up waiting for hours running just a simple query. On the contrary, if you are using those languages that support high-performance computing, then even through enormous amounts of data, results can be retrieved in no time.
Furthermore, choosing the right language can open you up to completely new kinds of work. If you want to build AI tools that run on mobile devices, Swift will be a better fit than Python. If you’re working on something that needs real-time processing, knowing Erlang’s concurrency models will give you an edge.
In short, the language you choose can make or break your project. It’s like choosing what pair of shoes you want to wear for a marathon run – you wouldn’t want to run with a flip-flop, right? Knowing the strengths and weaknesses of the different data science languages will make sure that you are using the right tool for the occasion.
Also Read: Career Path to Become a Successful Data Scientist
15 Alternative Data Science Languages and Frameworks
Now let’s focus on the real subject of our post – the alternative data science languages and frameworks that could give your data science efforts a turbo boost. We’ve rounded up 15 alternatives, which all have something different to offer – and one might just become your new favorite.
1. Julia Data Science Language
Julia is like a child prodigy—new but very intelligent and quick. It is tailored for tasks of high-performance numerical and scientific computing and can be used for big data processing positions. Moreover, it is almost as simple to write as Python, which makes it a perfect choice for data scientists who are searching for speed without losing usability. Julia can be as fast as C, which is amazing for a high-level language, and is becoming more popular in academics and research because of this fact.
2. Scala Data Science Language
Scala runs on the JVM, so it is very friendly with Java code, e.g., you can call Java libraries without the need to translate them. This language is great for the development of scalable data pipelines. As it now integrates with Apache Spark, Scala has found its place under the sun in the big data analytics field, especially in processing huge datasets.
Scala stands for “scalable language†in the context of its use, meaning that it can be efficient even with a small code. One of the places where this programming language is being used is in the financial sector, especially when it comes to processing very large volumes of data.
3. SAS Data Science Language
SAS is the granddaddy of data science tools, especially in industries like healthcare, finance, and government. It’s not the flashiest tool in the box, but it’s rock solid when it comes to statistical analysis and predictive modeling. Many large organizations truly can’t do without it, given its reliability and wide-ranging functionality. SAS stands for Statistical Analysis System and was originally developed in the 1970s! But don’t let its age fool you- it is still a major player in industries that necessitate heavy statistical lifting.
4. MATLAB Data Science Language
MATLAB is the Swiss Army knife for engineers and scientists. It has amazing tools for data visualization, deep learning, etc. It’s often used in academics, but there’s so much more you can do with it outside of class, i.e., analyzing complex data or modeling.
MATLAB was originally designed for matrix programming, the reason being its name: MATrix LABoratory. It is mostly used in the field of robotics, where accurate mathematical calculations are involved.
Read Also: Python vs. R: Choosing the Right Language for Data Science
5. Rust Data Science Language
Rust is a low-level language that’s a step in the right direction. While it still allows you to play with hardware, it does its best to guarantee your memory safety. And no trash collection means both speed and absence of runtime failures. If you want to write performance-critical data science, you don’t have to sacrifice performance and compromise on safety willingly. Rust is one of the most loved programming languages in yearly developer surveys. Safety and performance-focused Rust is a hit among developers who write code for “complex systems.â€
6. Go (Golang) Data Science Language
Go is Google’s child, designed for concurrency and scalability. It’s simple and powerful, which makes it perfect for data science tasks that need to be fast and scalable. Whether you’re building AI tools or large-scale data processing systems, Go can really make a difference in terms of efficiency and ease of use. Go was created by the same engineers who built Unix, which might be why it’s designed for efficiency and scalability. It’s used a lot in cloud computing.
7. Swift Data Science Language
Apple is betting big on Swift—its language of choice for iOS and macOS apps. In the data science space, Swift is becoming popular, particularly in developing AI tools that are meant to run on Apple devices. Thanks to its modern syntax and performance improvements over Objective-C, Swift is a good fit for mobile and on-device machine learning. Swift is actually a safer and faster language compared to its predecessor- Objective-C. It’s the language developers are using if they’re building any AI-powered app on any of Apple platforms.
8. Haskell Data Science Language
Haskell is a functional programming language that is purely so and has a very strong type system. It uses reliability and accuracy to characterize the languages. Thus, they are good for those data science tasks that require precision and accuracy. Haskell was named after the logician Haskell Curry, and functional programming is based on his work. Being a language that emphasizes purity and immutability, most developers who like their code to be correct love using it.
Also Read: What Does a Data Scientist Do?
9. F# Data Science Language
F# is mainly a functional programming language. It also runs on the .NET platform and is concise and expressive, which makes for good data manipulation languages, statistical modeling languages, and complex algorithm development languages. F#, on its part, integrates perfectly with other .NET languages if you are already caught in the Microsoft ecosystem. F# was actually inspired by OCaml, and many of its functional programming features are the same and are especially common in finance, where they need to handle complicated mathematical computations.
10. Clojure Data Science Language
Clojure is a modern Lisp dialect that’s built for simplicity and functional programming. Since it runs on the JVM, it can interoperate smoothly with Java code — this makes it a great language for scripting applications or testing the waters with new ideas on your existing codebase. Plus, its unique and elegant approach to handling concurrency makes it a natural fit for big data problem spaces.
Clojure’s syntax might seem a little strange when you first encounter it, but once you get the hang of it, you won’t miss any other language and will come to love its simplicity and power. It is also famous for having the best support of immutable data structures out of the box, which makes writing concurrent software less prone to error.
11. Kotlin Data Science Language
Kotlin is also a JVM-based language that is fully interoperable with Java yet more concise and modern. It is particularly popular for Android development but can also be used in creating data science applications that interact with Java based systems. Kotlin was created with the goal of improving Java, which is known to have many pain points for developers. It has become the first choice language in Android development, and its use is steadily increasing in other areas, too.
12. Erlang Data Science Language
Erlang was built to develop highly concurrent and distributed systems, particularly for real-time data processing and systems that are expected to be fault-tolerant and scalable. If you’re building real-time AI applications, its capability to manage millions of simultaneous connections is a great benefit. Erlang was first developed by Ericsson for telecommunication systems, and that’s why it’s so reliable and concurrent. It is still heavily used in systems where you need 99.9% uptime and fault tolerance.
Also Read: Machine Learning vs. Data Science — Which Has a Better Future?
13. D3.js Data Science Language
D3.js is not your typical programming language; it is a library that helps in the creation of data visualization using JavaScript. However, this fact should not deceive you – D3.js is a very powerful instrument that allows you to make fascinating, interactive visualizations that completely revive your data system.
14. Perl Data Science Language
Perl may seem like an old language, but it is still a mighty weapon of data science, especially when it comes to text processing and data cleansing. Perl regular expressions are well-known and really great at dealing with huge amounts of unstructured data. Many people refer to Perl as the “Swiss Army chainsaw†programming language because it does a lot of stuff and works really fast. This is specifically true in bioinformatics, where you deal with a lot of data cleaning/processing.
15. Elixir Data Science Language
Elixir is actually designed to run on the Erlang virtual machine, and that means it takes over all of the good stuff about concurrency and fault tolerance from Erlang. However, Elixir adds a more modern syntax on top of it all and focuses on developer productivity. So, if you want to build really scalable AI tools or just real-time applications, then Elixir might be the right tool for you.
Elixir was created by José Valim, a former Rails developer who believed that the simplicity and productivity of Ruby on Rails could be applied to concurrent programming. It’s gaining rapid popularity in web development, especially with applications that require real-time development.
Also Read: Data Science Interview Preparation: A Step-by-Step Guide
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FAQs:Â Data Science Language
1. Can these alternative data science languages and frameworks replace Python or R?
Not necessarily. While these languages offer unique advantages, Python and R are still the most widely used in the data science community.
2. How do I choose the right language or framework for my project?
If you need high-performance computing, Julia or Rust might be a good fit. If you’re focused on data visualization, D3.js is an excellent choice.
3. Is it worth learning multiple data science languages?
Absolutely! Learning multiple languages makes you more versatile and allows you to tackle a wider range of projects.
4. Are these data science languages and frameworks difficult to learn?
It depends on your background. Some, like Julia or Swift, are designed to be user-friendly, while others, like Haskell, might have a steeper learning curve.
5. How do these data science languages compare in terms of community support?
Python and R have the largest communities, but many of these alternatives also have strong, active communities.
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