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Software developers love arguing about which programming language is the best. However, the criterion for what is “best” is confusing. When we discuss software development for the machine learning and data science fields, this question is timeless and will never lose its relevance.

Most useful programming languages need to have ease of syntax and use, which is subjective, but factors such as type safety, speed, libraries, and community support are not. So, let us look at the top six programming languages for data science and machine learning.

• Scala

Scala is a popular programming language, and chances are most data scientists have come across it, mainly if they worked in IT. It is an open-source multi-paradigm concurrent programming language developed in 2003 by Martin Odersky. Scala is just a shortened form of “Scalable Language.” It was created to communicate standard programming basics in an elegant, type-safe, and concise way.

If you are familiar with the syntax of Java, you can quickly learn Scala. Learning this language is considerably smoother when you know other languages such as Python, C, or C++. Scala is preferred by many as it is stable, flexible, fast, and scalable. Coding in Scala is arranged and accomplished must quicker compared to Python. You can apply Scala to create profitable products that run with Big Data.

• Julia

Julia is gaining popularity in the data science and machine learning world. Some experts are already comparing it to Python, which might be premature, but it doesn’t lessen Julia’s capability in the slightest.

This programming language is modern, high-performance and significant, and was created by a group of MIT mathematicians and computer scientists. It is open source and is mostly applied for data manipulations and scientific calculations. If you have worked on Matlab, R, or Python before, you will get used to Julia quickly. Julia’s speed makes it an excellent language for machine learning and data science.

• JavaScript

If you are trying to get into data science as a developer and you don’t want to study a new language, JavaScript is your access. JavaScript is lightweight, easy to implement, and it is a powerful programming language.

Before picking up JavaScript, it doesn’t hurt to have a background in HTML and prior experience in object-oriented programming concepts. This gives you a fundamental idea of developing online applications. This also becomes useful when you are extending your machine learning module in the browser or mobile apps. Besides this, JavaScript has amazing libraries for developing dashboards and data visualization. Many machine learning methods like music composition, objection and gesture recognition, etc., can be accomplished using TensorFlow.js, which is a dynamic library on JavaScript for data science.

• Swift

If you are an Apple fan and you enjoy using their many devices and their compelling iOS, chances are, you will love Swift. This programming language is open source, flexible and easy, and Apple generated it for OS x and iOS apps. Swift is based on the best of Objective-C and C but without the limitations of C adaptability. It is a natural language for beginners because of its precise yet expansive syntax and quick speed to operate apps.

Recently, Swift started to gain attention among the community of data science and machine learning. It is highly suggested by Jeremy Howard, the co-founder of fast.ai. There are multiple libraries for doing tasks like high-performance uses for matrix math, numerical computation, using deep learning techniques, processing the digital signal, developing models of machine learning, etc. 

• Golang

Go, or Golang is a programming language developed by Google. Go is efficient, simple, and reliable. The focus on Go is very singular, and it keeps disagreements at bay by concentrating on one technique at a time as compared with other languages where problems have multiple solutions.

There is an immense number of resources, tools, and packages for doing data science jobs with Golang. This includes statistical and arithmetic computations, data gathering, data parsing, EDA, data organization, creating models of machine learning, etc.

• Spark

Spark is less of a language and more of a framework, but there is a reason why it’s on this list and why it is so popular among data scientists and data engineers.

Spark offers high-level APIs (Application Programming Interface) in R, Python, Scala, and Java, and provides an optimized powerhouse that sustains great execution graphs. It is open source, and a fast cluster framework for computing which is applied for processing, questioning, and evaluating Big Data. The benefit of Spark over other structures of big data is that it is built on in-memory computation. This allows estimates to operate fast.

Necessary background in Python is enough to learn Spark quickly. Spark can perform many data engineering and data science jobs, such as exploratory data evaluation, creating and debugging Spark applications, feature extraction, model evaluation, supervised learning, etc. 

Conclusion

The field for data science and machine learning languages is broad, and though R and Python are high for it, the objective was to show you how these other languages can be used as well.

Additional Helpful Resources:

Data Science Trends for 2019
Top 20 Machine Learning Tools and Frameworks
Benefits Of Switch From Web Development To Data Science