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Nine top programming languages for data science


Programming skills are critical whichever direction you go in data science. While languages like Python, R, and SQL act as foundations for many data science or analytics roles, others are useful for career paths in areas such as data systems development or better suited specifically for aspiring data scientists.

Use this post as a starting place to explore nine programming languages and when they’re used in data science.

How is programming used in data science?

The field of data science relies on programming across all job functions, from automating cleaning and organizing raw data sets to designing databases to fine-tuning machine learning algorithms.

What programming language is best for data science?

R and Python are popular, foundational programming languages in data science, but choosing the right language to learn depends on your level of experience, role, and/or project goals.

Nine data science programming languages

1. Python

Python is a general purpose popular programming language. Learning Python can open up doors not only in data science, but also in web and software development.

Python is an open source object-oriented programming language, grouping data and functions together for flexibility and composability. In data science, it's commonly used for data processing, implementing data analytics algorithms, and training machine learning and deep learning algorithms. Python supports multiple data structures and uses a plain English syntax, making it a useful language for beginner programmers.

“There is no comparison in terms of online documentation, user community, ease-of-learning, and general capabilities of Python,” says Dr. Clayton Miller, Associate Professor, Department of Building, School of Design and Environment, National University of Singapore (NUS), and instructor for the NUS course Data Science for Construction, Architecture, and Engineering on edX. “I also suggest data science-focused learners pick up the R language in parallel as it provides encapsulated libraries that aren’t always available in Python.”

When to use Python in data science: Python is a great place to start if you’re learning to code for the first time, want something scalable, and/or are looking to keep your career options open.

2. R

While Python is general purpose, R is more specialized, suitable for statistical analysis and intuitive visualizations. R is built to handle massive data sets and complex processing through RStudio. Its statistics-specific syntax is intuitive for researchers with statistics backgrounds, and powerful visualizations offer more intuitive communication of results.

When to use R in data science: Data scientists with some programming experience or beginning data scientists looking to make a mark in the research field should consider learning R. If you have experience as a statistician, you'll also recognize the structure of R.

3. SQL

Learning SQL, or structured query language, is vital for manipulating structured data. Large-scale datasets can contain millions of rows, making it difficult to find precisely what data you need. SQL is a querying language, allowing you to adjust, locate, and check massive data sets. As a domain-specific language, it's convenient to manage relational databases.

“Scripting with Python, fundamental statistics, and SQL are critically important regardless of which direction you go in data,” says Gwen Britton, former Associate Vice President of Southern New Hampshire University (SNHU) Global Campus STEM & Business Programs and instructor for edX MicroBachelors programs in data management and business analytics.

When to use SQL in data science: If you’re using relational databases, you must learn SQL.

"Scripting with Python, fundamental statistics, and SQL are critically important regardless of which direction you go in data."

4. Scala

Scala is an extension of Java, a language associated strongly with data engineering, with interoperability thanks to Java bytecode compiling and running on Java Virtual Machine. Built as a response to perceived problems in Java, it's a newer, more elegant language.

Scala enables high-performance frameworks for handling siloed data, perfect for enterprise-level data science. With vast libraries and support on common integrated development environments (IDEs), it's functional and scalable. Scala also supports concurrent and synchronized processing.

When to use Scala in data science: Data systems developers faced with high volume datasets regularly can use Scala to analyze without overloading.

5. Julia

Another specialized language, Julia is specifically designed for computations and numerical analysis. Although purpose-built, it provides versatility and supports both parallel and distributed computing and is incredibly fast. It's fast enough for interactive computing and can switch to a low-level programming language if necessary.

When to use Julia in data science: If you're focusing on data visualization or deep learning, numerical analysis, or interactive computing, the niche focus of Julia offers fast performance.

6. JavaScript

JavaScript is closely associated with web development and applications, bringing the capability to build vibrant web pages into the world of data visualizations. It's another general-purpose choice for data scientists with a good selection of packages and great web integration.

JavaScript helps convey insights from truly big data. It offers data scientists a considerable set of libraries for building dashboards, visualization, and just about any task a data scientist would need. It's scalable, but it functions best as a secondary language rather than a primary data science language.

When to use JavaScript in data science: Data scientists with development needs or who want to pick up a concurrent language for visualizations would be well served by learning JavaScript.

7. Java

Though it’s in the name for “JavaScript,” Java is a completely different programming language used for different purposes. It tends to be used for Android apps, credit card programming, desktop applications, and web enterprise applications.

There are many key differences in how Java and JavaScript are written, assembled, and executed. Java code must be compiled and is used to build applications to run in a virtual machine or browser, whereas JavaScript is all-text and runs on a browser only. Java is an object-oriented programming language, while JavaScript is (as the name suggests) an object-oriented scripting language.

“Building skills in Java can be a positive step towards gainful employment in some of today's most popular and cutting-edge companies. Thousands of technology companies like Uber, Airbnb, Netflix, and Slack reportedly use the language in their software infrastructure,” says Olufisayo Omojokun, Chair of the School of Computing Instruction at Georgia Institute of Technology and instructor for introductory Java courses on edX.

When to use Java in data science: Java typically dominates if development is taking place entirely on the server side, whereas JavaScript is usually more client-facing, with a focus on making web pages more interactive. Learning Java offers frameworks for data science areas such as deep learning or data handling and big data tools like Apache Spark and Hadoop are written in Java.

"Building skills in Java can be a positive step towards gainful employment in some of today's most popular and cutting-edge companies."

8. C/C++

Learning C/C++ offers excellent capabilities for building statistical and data tools. These will translate well to Python and scale well for performance-based applications.

C/C++ is also surprisingly useful because it compiles data quickly. It builds highly functional tools and allows for serious fine-tuning. It can be complicated to pick up if you've never studied programming languages before.

When to use C/C++ in data science: Web developers with experience in low-level languages could use C/C++ for scalable projects.


MATLAB is a programming language and environment specific to mathematical and statistical computing. It offers built-in tools for dynamic visualizations and offers users a deep learning toolbox that transitions well. It allows you to ease challenging mathematical processes.

It scales well and provides built-in graphics for custom plot points and visualizations. You frequently see MATLAB in teaching contexts to train things like linear algebra or numerical analysis. If you're carrying out complex mathematical processes, MATLAB can be very useful. However, it's not free, and Python now has multiple options that mimic MATLAB.

When to use MATLAB in data science: If you're in academia or your workplace is already using the environment, you have good reason to invest time into learning MATLAB.

Learn more: Six programming skills you can learn online

Bonus: Excel

Digging deep into Excel is almost like learning programming languages. With built-in features such as VLOOKUPS, pivot tables for quick data analysis, and basic tools for high-level data science applications like machine learning and regression, Excel is powerful enough to manage structured data. Learning Excel is a great starting point for jumping into business and data analytics. If you are interested in exploring this topic more in depth, you may want to consider a data analytics boot camp.

When to use Excel in data science: If you're a beginner and not quite ready for full programming languages, try leveling up your Excel skills. Excel is great for working on business analytics relatively quickly without much time for training or expensive tools.

Where to start: Explore programming and data science courses on edX

Python, R, and SQL will give you a great head start if you are interested in building a career in data science. Ultimately, though, there is no “best programming language”. If you are looking for direction, use your own data or objective as a starting place for learning code.

Last updated: January 2024