That means it’s essential to fill your toolbox with some programming languages, especially if you’re looking to jumpstart — or advance — your data science career. Programming languages help to organize and visualize data on various scales and extract insights from business and customer data.
To give you an idea of where to go from here, we rounded up some prominent programming languages to know.
Best Data Science Programming Languages
- Python: intuitive syntax, large number of resources, extensive libraries for data analysis, visualization and machine learning.
- R: data mining and statistical analysis capabilities, robust support community.
- SQL: crucial for querying data and managing databases.
12 Data Science Programming Languages to Learn
What Programming Languages Are Used for Data Science?
How Python is used in data science: Python has become widely used in data science for areas like data manipulation, data analysis (NumPy, SciPy, Pandas) and data visualization (Matplotlib, Seaborn, Bokeh). Python is also a strong tool for machine learning and deep learning, producing watershed platforms and libraries like Scikit-Learn, PyTorch and TensorFlow.
Whether it’s one of the most popular programming languages in the world or simply in the top three according to TIOBE, Python has undoubtedly boomed since the 2010s. Even though it was released in 1991, the language finally began to catch on more broadly around 2007 when Python-heavy Dropbox launched, providing extensive real-world proof of concept for its strengths.
How R is used in data science: R is still considered best-suited for data mining and statistical analysis, of which it offers a wide range of options. It’s also regarded as more approachable than Python for non-developers, since it’s possible to whip up a statistical model — and a sharp-looking visualization — with just a few lines of code.
R also sports a robust constellation of packages. Most notable is the tidyverse family, created by R-community member Hadley Wickham. It features popular packages for data organization (tidyr), data manipulation (dplyr) and data visualization (the groundbreaking ggplot2).
You can see R in action via the much-loved #TidyTuesday project. Each week, a new data set is released for data scientists to practice and demonstrate their data wrangling and visualization skills. It’s the kind of learning (and honing) in public that’s also emblematic of R’s famously supportive — and inclusive — community.
How SQL is used in data science: SQL sometimes isn’t even considered a proper programming language since it’s domain-specific, though it remains a must-know in the realm of database management. SQL is primarily used as a way to communicate with relational databases, and SQL extensions can be added to allow more complex processes to run alongside a database.
Most data scientists won’t be dealing with actual database administration, but querying the data — and being able to investigate the nuances of how data might be manipulated by the database — are crucial skills to have.
How C/C++ is used in data science: General-purpose language C and its object-oriented counterpart, C++, offer significant lift for high-performance data science applications and projects. C and C++ are some of the fastest and most powerful programming languages to use due to their compiled nature, making them ideal for intensive big data tasks, rapid data management and building machine learning tools.
Additionally, machine learning libraries PyTorch and TensorFlow are both written largely low-level C++ at its core, making it important to know even if employing Python.
How Java is used in data science: An object-oriented language, Java is most associated with web development, but it has a prominent role in data contexts as it can tackle statistical analysis, data visualization and machine learning projects where needed.
Java Virtual Machines (JVMs) are a common component in big data frameworks. The Apache Hadoop ecosystem (including Hive, Spark and MapReduce) relies on JVMs, which means at least a passing familiarity will be helpful for efficiently running data jobs that require large storage and processing requirements.
How Scala is used in data science: Scala was explicitly designed to be a cleaner, less wordy alternative to the Java language. Scala also runs on the same JVMs used in Apache’s big data frameworks, which makes it a great fit for distributed big data projects and data pipelines. “You can write hundreds of lines of confusing-looking Java code in less than 15 lines in Scala,” wrote Packt.
A principal software engineer at Seattle-based Hiya told Built In in 2019 that he’s built petabyte-scale data projects on Scala.
How Go is used in data science: Go, or GoLang, is a general-purpose language that can be used to build scalable data servers, data analytics platforms and machine learning systems.
Google developed Go in 2007 to support high-performance programming tasks similar to C but still be easily understood by beginners like with Python or Java. The language can handle native concurrency, garbage collection, memory safety plus offers cloud service and DevOps support.
How Julia is used in data science: Much has been made of Julia’s ascendancy in data science circles since its release — and for good reason. While Julia’s ecosystem is general-purpose, it’s known to support tasks in data interaction, data visualization, machine learning and parallel computing.
As developer Anupam Chugh noted in Built In, Julia is faster than Python; dynamic, yet more type-safe (thanks to its just-in-time compiler and multiple dispatch); and better equipped for distributed and parallel computing projects.
Julia’s power has especially made it a go-to option for big data analysis in the private sector and scientific research. The language can be found in noteworthy projects for climate modeling, weather forecasting and astronomical surveys, among others. Its wide-ranging interoperability — compatible with everything from Python to old-school Fortran — and its continued boost from MIT, also make Julia a likely long-term contender.
Like similarly emergent languages Rust and Clang, Swift is backboned by the powerful compiler framework LLVM. Swift is notably interoperable with Python and compatible with TensorFlow, plus is strongly supported by Google, making it a popular choice for machine learning. The program’s creator even joined Google Brain, a top deep learning research team, in 2017.
How MATLAB is used in data science: MATLAB, though remaining associated with projects in academia and scientific research labs, has also found its way into aerospace, automotive and robotics data science applications. MATLAB is actually part language and part working environment, allowing room to develop and validate new algorithms. Toolboxes — or libraries of application-specific functions — are another central component.
A downside: MATLAB is proprietary. But you can get a lot of what made the language notable years ago, like intuitive plotting, from various free, open-source alternatives. So while it remains niche and hardly a must-know for most working data scientists, its endurance, particularly in some higher education research corners, means it shouldn’t be omitted when considering the landscape.
How SAS is used in data science: One of the first leaders in analytics software, SAS is able to perform statistical modeling, data analytics and data visualization on complex scales. SAS can mostly be found in industries where old-school systems can persist, like finance, heavy manufacturing or healthcare, but it’s definitely worth being aware of. Being compared against languages like Python and R for big data projects, job seekers may encounter it in sporadic job postings for data science or analyst roles.
As economist and data science specialist Edward Hearn noted in Built In, the data science industry still lives in a state of tension where preference for open source climbs in comparison to proprietary software. Legacy businesses think languages like SAS do just fine for analytics code, and upending established software infrastructure can make for steep costs. Due to this, it’s enough to keep SAS on the radar.