Technology / Programming

Top 9 Programming Languages in Data Science

– and what they're most used for.

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Published on October 5, 2023

Data science and data analytics are hot topics for IT executives today, and data analyst and data scientist are among the most popular IT career development paths. The primary difference between these two positions comes down to the categories of data set – structured or unstructured – that these positions work with. 

Structured data sets are stored in databases, and data analysts can readily retrieve them for analysis in spreadsheets, data visualization platforms, and other similar tools. Structured data is typically clean and does not demand extensive manipulation with programming tools. 

On the other hand, data scientists work with unstructured or ‘messy’ data. Unstructured data sources include PDF files, printed reports, voice and video recordings, social media postings, public websites, and Internet of Things sensor logs. The data in these sources must be extracted and then ‘cleaned’ before being used for analysis and visualization

Data scientists design and write programs to locate and pull the data – before cleaning it and creating usable data sets. Now, you don’t need to be an expert programmer to become a data scientist, but it helps! Aspiring data scientists who are proficient coders will be self-sufficient and more effective than those who must rely on the IT department’s programmers.

What are the Most Used Data Science Programming Languages?

There are several programming languages that are popular choices for data scientists. These include Python, the R statistical language, Julia, Java, C/C++, Swift, Go, SQL, and MATLAB. Let’s start with Python.

Python: The Go-To Language for Data Science

According to the Developer Nation’s recent 30,000 developer survey, Python is among the top three programming language choices of 2023. Python was rated the most popular in data science, machine learning, and artificial intelligence.

Python is an interpreted language and thus may suffer in run-time performance compared to compiled languages. However, Python is recognized as easy to learn and has a large community of developers and an expansive supply of reusable libraries designed specifically for data analysis, visualization, and machine learning. These libraries help make it faster and easier to implement the complex algorithms that data scientists require. Check out our recent blog post for an in-depth view of why Python is the programming language of choice for data scientists.

R: A Language Built for Statisticians

R is a programming language for data manipulation and graphics. It is primarily intended for statistical use. Although statistical libraries are available for Python, they tend to be less powerful than the ones available to data scientists in the R language environment.

Is Python Better Than R for Data Science Programming?

It’s not an either/or choice! Generally, you'll use R when dealing with complex data computation, statistical analysis, and data visualization. If you’re dealing with more general data science applications – such as data manipulation, machine learning algorithms, web-based visualization, and big data – then Python is a better choice.

Julia: A High-Performance Language for Technical Computing

The Julia programming language for data science and data analysis applications is relatively new on the data science scene. As such, it lacks the mature interfaces, documentation, support, and reusable libraries found with more established languages. However, according to its supporters, Julia has key advantages over Python. First, it’s a run-time compiled programming language and thus is significantly faster than an interpreted language. Julia’s run-time memory allocation eliminates the hassle and complexity of memory management and clean-up that Python programmers face. 

You’re not going to find Julia specified in many data scientist job openings unless it’s for an early-adopter enterprise that sees the intrinsic value of the language.

SQL: Managing and Querying Large Datasets

As we earlier described the role of the data scientist, the end result of data cleaning and data wrangling are large structured data sets typically kept in SQL-accessible databases. Therefore, every data scientist should have at least a working knowledge of SQL, but not necessarily the in-depth skills expected of data or business intelligence analysts.

Java: Object-Oriented Approach to Data Science

Java is among the top three programming languages highlighted in the Developer Nation survey. Java is easy to learn and has a solid following as a programming language for data science applications. Data scientists chose to use Java for data science programming due to the availability of frameworks such as the Hadoop distributed storage/parallel processing framework. They also appreciate the scalability and portability afforded by the wide range of platforms that support Java Virtual Machines.

Of course, Java’s general support of object-oriented programming (OOP) principles allows data scientists to apply OOP to their data science projects and to build modular, reusable, and scalable code that is easy to maintain.

C/C++: For Performance-Critical Applications

C/C++ have been the go-to industry languages for high-performance systems and applications since their creation over forty years ago. C and its C++ object-oriented extension are still used extensively in building operating systems (think Windows, Linux, MacOS, etc.), IoT applications, and embedded systems. Both language variants are compiled, which results in faster run times than Python.

Why use C/C++ for data science applications? You would not go out of your way to learn the low-level C/C++ programming languages for data science. However, when the highest performance is demanded and C/C++ skills are available, C/C++ can be used to great effect.

Swift: A Language for iOS Data Science Applications

Planning to build data science applications in an Apple iOS environment? Then Apple’s Swift language may be a good choice. It was designed as an easy-to-learn, efficient language and introduced initially on Apple’s MacOS/iOS operating systems. Swift is now available on MacOS/iOS, Linux, and Windows and is gaining in popularity. Developer Nation’s survey showed 5 million Swift developers – compared with Python’s 17+ million – with the most popular usage in developing AR/VR and mobile applications.

Python and C/C++ are also available on the iOS platform, but if that’s the primary target platform for your data science applications, then Swift is probably the better choice.

Go: A Language Designed for Scalability

The Go programming language was designed by Google and released in 2007. The language was designed with large-scale, data-heavy multiprocessor distributed applications in mind. Go has a growing community of developers – estimated at 4.7 million by the Developer Nation 2023 survey. The most popular use case for Go was in cloud and AR/VR applications. As a compiled language, Go executables are significantly faster than their Python equivalents. For a detailed feature comparison with Python, check out our blog post on Go vs Python.

MATLAB: A Language for Mathematical Computing

MATLAB is different from the other languages that we have reviewed. It is a proprietary product developed by MathWorks and primarily targeted at engineers and scientists. MATLAB is an effective option as a programming language for data science with its matrix manipulation, function plotting, and visualization design capabilities.

MATLAB is a compelling functional alternative to Python and other data science programming languages. However, unlike other alternatives, MATLAB carries a licensing cost. So, unless you already have access to a MATLAB environment, opting for an alternative is probably a better choice. 

Criteria for Selecting a Programming Language for Data Science

The programming languages for data science that we’ve discussed can all do the job. Some may do it easier, some faster, but your selection is unlikely to be made on a feature-by-feature comparison. Here are considerations that will inform your selection decision:

  1. How Steep is the Learning Curve? Do you have current expertise in one of the languages? If so, that may drive your decision. If not, how easy is it to learn a new language?

  2. Is There a Strong Community? Is the language well-subscribed in your area? Do you have experienced practitioners close at hand (or online) who can provide help and guidance?

  3. Are There Libraries for Data Science? Does the language support a wealth of libraries of reusable components? These allow the data science programmer to save development and test time by simply pulling pre-built, ready-tested components and plugging them into the application.


There is no single best programming language for data science. Each language has its merits and weaknesses. However, we should acknowledge that Python is the current popular choice of programming language for data scientists.

If you’re an aspiring data scientist or current data analyst looking to add Python programming to your skill set, check out our new online course, Programming for Data Science.

If you’re already up to speed on Python, then you may benefit from one of our related courses:

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