programming languages for data science

Popular Programming Languages Used In Data Science

Data Science is a combination of machine learning principles, algorithms, and many tools which are used to find out the secret patterns that are hidden from the original data. The data scientist and data analyst have to deal with machine learning, advanced algorithms, exploratory data analysis, business administration, and data product engineering.

The essential eligibility for any data scientist for data analyst would be to upgrade themselves with the changing standards of the competitive data science world. A piece of amazing knowledge and certification in data science will land you in a great data scientist job. The demand for data professionals and data scientists is huge in the market. The two main requirements for the data science industry is the application of knowledge and programming languages.

 Here is a list of of the languages which are used in data science:


Python is considered to be a very object-oriented, open-source, flexible, unique, and easy to learn among all the other programming languages. The libraries and the set of tools that are inbuilt in Python are considered to be very rich in resources that are specially designed for data science. For beginners, they are learning & it is natural to get many queries when they are experimenting with the code so in this case, Python has a huge database and also a huge community where you can upload your query and discuss the solution with someone who has more experience in coding. The top choice for data science by the developers and data scientists have always been Python and it is used for a very long time.


Compared to the other programming languages R is considered to be a very unique language because of its extremely interesting inbuilt features and it is originally a vector language. In a single vector, many functions can be added so that all the things can be done simultaneously without letting the task to be performed in a loop. Because of its extremely good features, the applications of this language start from financial studies to Biology, medicine, and even genetics.

Structured Query Language (SQL)

It requires a very good database to hold the database and to use the data productively and one of the very best domain-specific languages is SQL. The primary function of any data scientist is to use the original data into productive insights and by using SQL for data retrieval and the other task any data scientist can achieve good productivity. The knowledge of extracting data from any database by using a language like SQL is an essential requirement for a data scientist who works using SQL.


This is a language which is irreplaceable by any other. This low-level programming language works very well for the data science frameworks. The fastest and extremely powerful language which can be used in data science with high-level execution, this can be used very easily since the framework is very simple. The data scientists who work on these applications should have a broader knowledge of coding in C++.


To develop any enterprise there should be a very good data framework like Spark, Flink, Hive, and Hadoop; all these are written in one of the oldest languages which are Java. This language has a great set of tools and libraries which can be very well used for machine learning and data science. Since it has these features it helps to solve most of the issues caused by data science and machine learning.


A very essential qualification for any data scientist is to know JavaScript because it is excellent for data visualization. JavaScript has excellent inbuilt libraries that simplify the work for visualization and professionals suggest this language to be used for any data visualization. Many advancements in technology have done so that it is easy for any data scientist to develop any code on the server-side or browser.


Real-time formats elementary binary sensor video image is supported by MATLAB. This has an inbuilt library that supports machine learning functionality and a full set of statistics also with advanced methods like system identification, nonlinear optimization, and thousands of pre-built algorithms that are further used in video and image processing control system design and financial modeling.


Compared to Python, the syntax used in this language is very simple and readable and that is why it is a fast programming language. Since it is secure and stable it is considered to be an efficient programming language. For most of the companies like Google and Apple, it is one of the official languages for developing the applications.