You need to learn several programming languages if you want to pursue a career in data science since one language cannot solve all of your problems. To keep up with the latest developments in this rapidly evolving field, data scientists need to be willing to learn more.

Leading programming languages for data science on demand

Python

Python proficiency will be the most sought-after skill set in data science for at least the next five years. In the industry, Python knowledge combined with quantitative reasoning and experimental analysis can lead to success. Python’s flexibility makes it stand out from other programming languages.

R Programming

Many statistical models can be designed using R. Almost 8,000 networks have contributed packages to the public R package archive. In statistics, it is used to perform regression tasks. Additionally, R supports a variety of chart types for data visualization.

Java

Among desktop, web, and mobile developers, Java has remained a favorite for three decades. JVM (Java Virtual Machine) is a highly sophisticated environment used for Java development. Java has become popular with enterprises due to its scalability over other modern languages. This makes it a popular choice for creating large-scale machine-learning applications.

JavaScript

The JavaScript programming language was primarily used for designing interactive web pages in the 2000s. ReactJS, AngularJS, VueJS, NodeJS, and many other frameworks have significantly evolved over the past decade. With its web-based dashboard, users can build interactive data visualizations from datasets.

SAS (Statistical Analysis System)

A statistical modeling software suite known as SAS is commonly used for data management, business intelligence, multivariate analytics, and predictive analytics. It has become a household name in the analytics industry since SAS was released in 1976. Data in SAS can be accessed in multiple formats, managed and manipulated, split and merged, and analyzed statistically.

Scala

Scala is a functional programming language that is widely used today. JVM is used to run it. If you often have to work with large data sets, this is an ideal option. It can be used in data science easily since it originated from a JVM.

Among the well-known cluster computing frameworks, Apache Spark is written in Scala. Therefore, Scala is an ideal choice for data science tasks centered around Spark.

TensorFlow

Numerical computing is made possible with TensorFlow, one of the leading libraries. Datasets of any size can be tackled using this ML framework. Distributed computing and TensorFlow work well together. TensorFlow lets you run your graphs in parallel on CPUs and GPUs by breaking them down into chunks. As a result, it allows you to quickly train large and complex neural networks.

C#

Microsoft created C#, which has become one of the most well-liked programming languages. Java serves as an inspiration for C#, which offered a modern spin to further enhance it. Microsoft introduced the Hadoop framework accessible for Windows to make data science utilizing C# easier. The ML.NET platform can build cross-platform applications for machine learning.

Ruby

Ruby is commonly used to process text. It was also utilized among developers for general activities, including developing servers and prototyping.