What Programming Languages Are Used in Data Science?

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By Doug Wintemute

Published on September 9, 2021

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Data science helps businesses better understand their information and their customers. Data science can also help organizations make better decisions and strategize more effectively. For data scientists to do all this, however, they need programming skills and familiarity with at least one programming language.

Data scientists may use general-purpose programming languages as well as languages for application and database design. They may also use specialized languages for statistical problem-solving and visualizations.

With hundreds, if not thousands, of programming languages out there, prospective data scientists may not know which ones to prioritize. In this guide, we look at the most popular data science programming languages and highlight how they are used in the field.

What Are Programming Languages?

Programming languages allow coders to provide computers with instructions that they can understand. This type of communication is used in computer programming, application design, web development, and data science. Just like traditional tools, some programming languages perform better at certain tasks than others.

Programming languages may specialize in design, ordering, or analysis, whereas others minimize the learning curve by encompassing multiple capabilities. The more fluent a data science professional is with a programming language, the more efficient and accurate their analysis can be.

Types of Programming Languages

Programming languages are classified as high- or low-level languages. Low-level languages look like computer processor languages and may be difficult for many people to understand, even coding professionals. While low-level languages provide fast and reliable instructions to computers, they are rarely used for data science projects.

Many of the most important coding languages used in the industry today are high-level. These languages use syntax that is similar to human language to process instructions, making them easier to understand and work with.

The Best Programming Languages for Data Science

Python

Python is among the most popular languages in every field, including data science. Due to its simple syntax, readability, and streamlined concepts, Python can be easy to learn. Data scientists typically use it for its many libraries.

These dense libraries provide access to data analytics algorithms, predictions, and visualization tools, along with data processing and machine learning capabilities. Data scientists can use Python for nearly every task, as it's compatible with most data formats, accepts imported tables, and allows users to create data sets.

You can learn the language by taking a Python coding bootcamp or an online Python bootcamp. You can even learn Python for free with online courses, resources, and videos.

R

Though R may be more difficult to learn than Python, R is another popular data science language. R offers some of the most powerful statistical operations available and a considerable amount of data-focused functions.

R can perform effective data analysis, create data visualizations, and build web applications. It can work on most operating systems and connect to most databases, which allows creators and users to work on different systems without issue. The R community also provides substantial support.

SQL

SQL is one of the best data science languages for working with databases. The simple query system can help users retrieve, update, edit, and manipulate information from massive data sets.

SQL's sheer analytical power has made it essential for working with data. Gaining familiarity with SQL should be a priority for those looking to enter the data science field.

JavaScript

JavaScript is a lightweight language with both front-end and back-end capabilities. Though mainly used for web and mobile application development, this evolving and versatile language has many data science libraries that make it useful for handling real-time data and conducting data analysis and visualization.

Most data scientists use JavaScript for its data visualization and web integration features, but the prevalence of the language online makes it worth learning for any professional working with code. JavaScript can be used for model simulations and data exploration, and it allows for easy collaboration and communication between multiple coders.

Scala

Scala is among the more popular big data programming languages because of its powerful performance, particularly at the enterprise level. In concert with a big data platform, this programming language can handle high-volume data sets.

Scala is not typically for beginners. Data scientists often use it to create machine learning models and manage Java code.

C/C++

C/C++ is one of the more powerful computer programming languages, but it may be challenging for new users to pick up. It works well with Python and allows users to compile large data sets and perform statistical computations, making it one of the more common data science programming languages.


Frequently Asked Questions About Data Science Programming Languages

What is the best language for data science?

Python is one of the most popular coding languages used in data science due to its versatility and the number of available data science libraries. R is also a good data science programming language, as it works with multiple platforms and has a massive library.

Which programming language should I learn first for data science?

Python is one of the best starter data science languages, as it is one of the easier languages to pick up. Python can be challenging to master, however, so prospective data scientists should invest the appropriate time and energy into learning it at a deeper level.

Is Python enough for data science?

If data scientists only learn one language, Python would be a good choice. This language can handle nearly all of the processes required by the field. While other languages may be better suited for certain tasks, Python is usually capable of accomplishing most aspects of a project.

Reviewed by:

Born and raised in upstate New York, Brian Nichols began his IT education through a vocational high school where he focused on computer science, IT fundamentals, and networking. Brian then went to his local community college, where he received his associate of science in computer information science. He then received his bachelor of science in applied networking and system administration from a private college. Brian now lives in Kansas City, where he works full-time as a DevOps engineer. Brian is also a part-time instructor in cybersecurity. He's passionate about cybersecurity and helping students succeed.


Brian Nichols is a paid member of the Red Ventures Education freelance review network.