Best Languages to Learn in Data Science (2022)

Data Scientists define Data Science as “Data Science is a field of applied mathematics and statistics that provides useful information based on large amounts of complex data or big data.” It is a collection of various programming languages that allow a data scientist to gather and analyze data, using various analytics and reporting tools to detect patterns, trends, and relationships in data sets.

What is the Future of Data Science?

Data Science has become a high-demand career skill in formal and informal business life. “According to the ‘HRWorld From The Economic Times report, “The rise of data science needs will create roughly 11.5 million job openings by 2026 globally.” It is used in banking, policy work, finance, healthcare, marketing, and sales. It helps companies to understand the market and make policies to grow their business.

Data Science is an attractive job. In the United States of America, a Data Scientist has up to one hundred thousand dollars in salary per year. If you are interested in becoming a proficient Data Scientist, you should read this article attentively. I’m going to cover all the milestones that are necessary to become an excellent Data Scientist.

Which Language is the best in Data Science?

As you know, Data Science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. In short, you have to learn a set of languages in data science to become a Data Scientist. The most demanded languages in data science are Python, SQL, R, Julia, JavaScript, Scala, Java, MATLAB, C/C++, and SAS. A Data Scientist’s research or study does not revolve around these languages only. He should be willing to learn updated content daily because we are living in artificial reality (AR), virtual reality (VR, artificial intelligence (AI), machine learning (ML), and deep learning era. Let’s explore the best languages to learn in data science.

10 Best Languages to Learn in Data Science

1- Python

Python is an easy-to-learn and open-source programming language even beginners can quickly learn it. It is a multipurpose programming language that is used to develop mobile apps, web applications, artificial intelligence, and more. If you compare it with any other programming languages like Java, Julia, and SQL, you will find it extraordinary among all its peers. It is portable and extensible.

Data scientists use this programming language to create web applications, mobile applications, and graphical user interfaces (GUI). Data Scientists create software testing frameworks, storing data libraries, and web frameworks. It is the most powerful programming language which has data collection & cleaning, data exploration, data modeling, and data visualization tools. NumPy, SciPy, and Pandas are huge deep learning libraries that use for artificial intelligence techniques.

2- Structured Query Language (SQL)

SQL stands for Structured Query Language. It is a computer programming language that is used to organize ‘collections of records,’ and these collections of records are called databases. As a data scientist, you should learn SQL to edit, update, and delete data from databases. After Python, SQL is the most specific language in data science because with ‘SQL’ a data scientist can optimize large databases, search and update data, and can perform daunting actions with a single query. In short, a data scientist should be an expert in SQL for analyzing, organizing, and performing database relations.

3. R

‘R’ is a computer programming language for statistical computer programing, data analysis, machine learning, and graphics. It is an open-source programing language supported by the R Core Team and the R Foundation for Statistical Computing.

It supports object-oriented and procedural programming. ‘R’ is used in any domain like banking, finance, support, weather forecasting, etc. It is so much easy to learn, better to handle data, and supports matrix arithmetic. R has a big scope of job prospects.

4. Julia

Julia is a high-level, fast, and easy-to-use computer coding language that is used to write for any application, numerical analysis, and computational science. It supports reading other coding languages like Python, R, C++, and more.

It was invented mainly for scientific programming purposes, data analysis, and statistical functions, but its multidisciplinary interface makes it a versatile language. Data Scientists consider it the inheritor of Python due to its multi-purpose functionalities.

5. Java

Java is a high-level, class-based, and object-oriented programming language in Data Science. It is easy to read, learn and understand computer coding language that is used in graphic user interfaces, consoles, websites, game development, and mobile applications. Java is also used to develop software for mobile and computer devices as well as electronic devices like television, washing machines, air conditioner, and so on.

There are three editions of Java ‘Standard Edition, Enterprise Edition, and Micro Edition.’ Java Standard Edition contains core libraries like ‘Java. Lang’ and ‘Java.Util.’ Java Standard has APIs like JMS, EJB, JSPs, Servlets, etc. Java Micro Edition is used to program java in cell phones, set-top boxes, handhelds, and so on.

6. JavaScript

JavaScript is the most popular and high-value programming language to build software and web applications. According to a 2022 report, “98% of websites use JavaScript on the client side for webpage behavior, often incorporating third-party libraries.” Data Scientists use it to develop web applications, and websites as well as artificial intelligence applications.

It is a versatile programming language that can perform simple to complex actions easily. Moreover, it supports several libraries like TensorFlow and Keras for deep learning and machine learning. JavaScript has so many frameworks like ReactJS, AngularJS, VueJS, NodeJS, etc which make it more powerful.

7. Scala

Scala is a strongly object-oriented and functional programming language. Scala programming language is based on Java, so it will be easy to learn Scala if you are aware of Java. It is an upgraded version of java and is used to remove unnecessary code.

It is designed to perform specific operations using multiple methods. In addition, you can become an expert in Scala if you know Python, C, and C++. it is used for distributed computing, web development, and data processing. Scala is a high-value language for data scientists to write in Apache Spark.

8. C++

C++ is a high-value programming language for developing computer applications like web browsers, operating systems, and games. C++ is a low-level language therefore it is not easy to learn. It is a faster programming language than others to create data and machine learning applications such as TensorFlow and PyTorch.

It is a high-performing language to process gigabytes worth of data in seconds. C++ is used to write deep learning algorithms and build deep learning models for system programming. Moreover, it is one of the earliest languages, so if you want to become a superficial data scientist, you should understand the fundamentals of C++ programing language.

9. MATLAB

‘MATLAB’ stands for Matrix Laboratory. It is a multi-purpose programming language that deals with matrix manipulations, the creation of user interfaces, the plotting of functions and data, and interfacing with computer programs written in other languages. MATLAB is written in Java, C, and C++. It is specifically created for scientists and engineers to develop products and systems.

It is one of the earliest computer programming languages. Now it has become the most powerful tool to perform advanced mathematical and statistical operations. It makes a data scientist more proficient and excellent in the field of data science.  

10. SAS

‘SAS” is an abbreviation for Statistical Analytical System. It is a high-performance computer programming language that is used for statistical analysis, common spreadsheets, and data visualization. It is a very high-standard programming language that is only for Windows operating systems.

It has multiple software like SAS Enterprise Guide, SAS Enterprise Miner, and SAS STAT software that learns a data scientist student. Although SAS is a high-level language, it is very easy to learn and easy to use due to its graphical user interface.

Conclusion

Data Science is an outstanding field of learning and it has unlimited knowledge to learn and apply. Nobody can achieve the whole knowledge of data science. Every developer belongs to a specific field of data science, but he acquires the basic knowledge of every language. I have described the best programming languages to learn in data science, but I think Python, Scala, and JavaScript are the best languages to learn. They are high-level languages and have a bright future.