TOP 10 PROGRAMMING LANGUAGES THAT DATA SCIENTISTS SHOULD LEARN IN 2022

 

Data science is such an important profession, and new jobs are arising that require people with expertise in this realm.

 

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Table of Content

1.      JavaScript

2.      Java

3.      Scala

4.      Python

5.      R

6.      SQL

7.      C++

8.      Julia

9.      MATLAB

10.  HTML

Data science is a field where skills in software engineering, analytics, and mathematics are all useful. Given the demand for data scientists with programming skills, mastering only one of these areas could restrict your opportunities. A data scientist with a software background is more independent and does not need outside resources to work with data because these two worlds interact so often these days. Data science is such an important profession, and new jobs are arising that require people with expertise in this realm. Below is a list of the most popular programming languages that data scientists should learn in 2022.

1. Javascript

In the modern tech world, it is hard to imagine a developer that doesn’t use Javascript at least on some level. It’s a high-level programming language and one of the core technologies of this era. Javascript was originally made for front-end development but now it can be geared towards back-end work with the advent of NodeJS. It’s also one of the best programming languages to learn as a data scientist in 2016.

2. Java

Java was initially built by James Gosling, Mike Sheridan and Patrick Naughton in 1991 as the language Oak. It had a big global impact being the first programming language to enable efficient high-level abstractions. Java is one of the most widely used programming languages used these days. Java is back-End code, which makes it a good choice for any device that has an internet connection. This includes not only web development but also Android and other programs. It’s also common to use Java on desktops because this language is easy to use and provides a lot of support if you have

3. Scala

Scala has a close relationship to Java, which is why it’s often associated with data engineering. To that end, Scala’s interoperability benefits from being able to compile and execute on the Java Virtual Machine. In addition, Scala was developed as an extension of Java in response to certain perceived problems with that programming language. Scala is an excellent choice for high-speed data handling. Seen in enterprise data science, Scala also has a ton of other uses that are great for any company.

4. Python

The Python programming language will continue to be among the most popular ones out there and it’s all thanks to its readability, fantastic support for many industries, ease of acquisition, and incredible versatility. This language shows a 2.17% increase in popularity, as opposed to the decline for Java in the same period.

5. R

R is a programming language that is widely used in data science, while Ruby is also very popular. It’s particularly favored by startups, with companies such as AirBnb and Twitch using it. The demand for Ruby on Rails is up – it’s fuelled by a full-stack web application framework by the same name which runs on the Ruby language.  

6. SQL

SQL is used by programmers to locate and retrieve data from a database and also to update, add or remove records. A common misconception is that SQL stands for “Seletal Quadrangle Language” but this isn’t true. SQL is a very useful programming language for database management, which finds a broad application in many market fields.

7. C++

One of the most shocking answers you will find out below is that C++ is used in analytics along with research as well as in game development. The popular game development engine, Unreal Engine, uses C++ for scripting in games and software alike. You can do a wide range of things through scripting with the help of this versatile programming language.

8. Julia

Julia is a relatively new language and it isn’t as mature as older languages. However, Julia is becoming more popular among data scientists that work in high-performance computing environments. It not only has a fast development process, but also produces apps that perform as well as those created in languages with less resources.

9. MATLAB

Machine Learning Toolbox is a programming language and environment that caters to statistical computing. It offers a set of dynamic visualizations and also comes with a deep learning toolbox. The toolbox favors trends in the recent technologies and supports most languages.

10. HTML

HTML is a popular platform for developing cross-platform games and applications that render well on different browsers. HTML5, a newer version of the language, can often be used in place of JavaScript to accomplish similar tasks. A programmer usually doesn’t need programming understanding or fluent knowledge of algorithms to be able to use HTML, which is a simple language. It’s because of this that it’s increasingly becoming the preferred choice for game designers.

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