Top Data Science programming languages

Data science can be defined as the science of information. It is a field of study that uses algorithms, systems, processes, and scientific methods to derive information from raw data and translate it into a useful form. It includes disciplines such as data mining, machine learning, and big data.

It is a field of study that derives meaningful information from large volumes of raw and unstructured data. It uses algorithms, domain knowledge, maths, and statistics to analyze hidden patterns and derive information.

This field of study is growing at a rapid pace and it is important that you grow with it. It uses concepts of mathematics, statistics, computer science, and domain knowledge. It helps businesses grow by facilitating decision-making, making predictions, and analyzing industry trends. Learn data science from the best certification available online or you could even go for a data science and business analytics course.

A programming language is a digital language that is understood by the computer and is used to instruct the computer in the form of logical codes to develop software. This article will provide a list of top programming languages used in data science.

Which are the most popular data science programming languages?

Python

Python is the most popular data science programming language. It was developed in 1991 and has been the go-to source for machine learning, deep learning, big data, and artificial intelligence.

Features

It is very easy to use and can be used for a wide variety of purposes.
It is very popular among data scientists.
It is very easy to learn, and due to its popularity, you can easily find the solution to problems.
It is the best language for facilitating automation. With more and more tasks being automated, Python is the most sought after language.
It supports complex tasks such as visualizing, modelling, analysis, and data collection.
It has data science libraries like matplotlib, TensorFlow, Scikit Learn, and Keras.

Java

Java is a very popular data science programming language. It is used for data mining, data analysis, and machine learning. One of its salient features is that it can help build applications from scratch. It can help build simple to complex applications. It is much faster than other languages and provides quick and accurate results. Contrary to popular belief, Java is a powerful language that can be used for basic to advanced level programming.

Features

It is very fast in comparison to other languages and hence facilitates maintenance and scalability.
It has a garbage collection facility. It does not delete itself, unlike other languages.
It is highly portable and can be transferred easily.

R is a high-level language that is used for statistical computation and graphics. It is extremely useful for data scientists as it helps in statistical analysis and computing. It is the most popular programming language for data science. It is easy to learn and can be used for scripting. This language is a good choice for handling complex data sets and large volumes.

Features

It is an open-source language.
It has a large community of users which help gain support in times of crisis.
It consists of multiple packages.
It is used for quality plotting and graphic designing.
It is used for statistical computation and machine learning.

JavaScript

Java script is another programming language popular among data scientists. It is primarily used for developing web pages and websites. It is an object-oriented language. It has a huge number of libraries for supporting users. It is used to create visualizations and is helpful in big data.

Features

It is a versatile language and can handle a variety of tasks all at once.
It is used for developing complex websites.
It is home to processing frameworks like Hadoop.
It can be scaled up to support advanced applications.

C/C++

C/C++ is among the first programming languages ever developed. It is a low-level programming language and hence is used as a base language for coding. This language can facilitate the quick compilation of data and therefore is very useful for data scientists.

Features

It is very fast and can compile 1 gigabyte of data in a second. It is very helpful in big data.
It allows the user to look into the details and fine-tune them.
It allows the user to have better command over his application.

SQL

Structured Query Language or SQL is one of the most popular languages amongst data scientists. SQL is exclusively used to handle structured data sets. K knowledge of SQL is necessary as it provides access to the database. It helps in querying databases and hence is important for data scientists.

Features

SQL is easy to use when compared to other languages.
It is flexible and does not require the use of traditional programming logic.
The interface is not user-friendly.
It is very costly.

Scala

Scala is a general-purpose language that is very useful for data scientists. It is a modern language that was developed in 2003. It was developed to solve the issues encountered in Java. It can be used together with JAVA and is appropriate when working in large data sets.

Features

It can be used with Java and Spark.
It is extremely useful while working on voluminous data sets.
It has a large number of libraries and is supported by IDE’s.
It is easy to use for Java users.
It can be easily scaled up.
It can be used for data analysis.

Julia

Julia has been developed specifically for assisting quick quantitative analysis and computation. It has a built-in support system for package manager and is used in deep learning.

Features

It can be used for front-end as well as back-end programming.
It is a versatile language.
It is very fast and easy to use.
It is easy to learn as it is similar to other popular languages like Python.

Conclusion

There are 100+ programming languages currently, and each one of them is suitable for different purposes. Other popular languages for data science are MATLAB, SAS, Ruby. The languages mentioned above are the most popular in the data science domain. These languages align very well with the functionalities of data science and hence are extremely useful.