Data Science and Artificial Intelligence

Data Science Programming: Top 7 Languages

Data science is the field to be in for professionals from a variety of backgrounds – especially software engineers and programmers. The field offers numerous opportunities that pay well and allow fast career growth. For this reason, 6 out of 10 developers are looking to learn machine learning skills. This is not to say that their present skills are useless. Programming is a key skill to have in data science. What makes a great data science programming language? And, which data science programming language should aspiring data scientists learn? Here’s an overview of the top 7 languages for data science programming.

Top 7 Data Science Programming Languages


This general-purpose programming language is popular among data scientists. Python was created by Dutch programmer Guido van Rossum in 1991. Since then, others have contributed to the development of the language.

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The Python Package Index is a rich repository of code developed for specific purposes by programmers in Python’s community. It is an easy language to learn and often recommended as a great language to start learning programming. Python has packages like pandas, scikit-learn and TensorFlow that are handy when developing machine learning applications.


R is a great data science programming language for statistical computing. It evolved from S and was created in 1995. It easily integrates with programming languages like C and Fortran making it easy to work with.

R has a wide range of packages that are open-source and cover neural networks, non-linear regression, etc that are useful in developing statistical applications. R is also useful in visualizing data through libraries like ggplot2.


Another popular general-purpose language – Java runs on its virtual machine JVM. Supported by Oracle, it is a highly portable computing language. Many applications are built using Java. Hence, the ability to integrate data science into existing code is highly valued.  It is strong-typed. Therefore, priceless in developing big data applications. Lastly, it is suitable for creating Extract, Transform, Load (ETL) processes, as well as, running machine learning algorithms.


A multi-paradigm language that runs on JVM – Scala is a data science programming language that suits objected-oriented and functional programming. Apache’s cluster computing framework Spark is written in Scala. Together, Scala and Spark allow data scientists to manage and work with high volumes of data effortlessly. Scala is useful in developing big data applications for this reason.


Perhaps the oldest language on this language, SQL (Structured Query Language) has been around since 1974. It is best suited for, as its name suggests, querying relational databases. This data science programming language has undergone many changes since its inception, but fundamentally remains the same. It is easy to read due to a declarative syntax. SQL is useful in developing a variety of applications. It easily integrates with other languages thanks to SQLAlchemy.


Developed by MathWorks, MATLAB is a numerical computing language. It is ideal for quantitative applications that must perform complicated mathematical functions. MATLAB is also useful in data visualization because of its plotting capabilities. The language was always popular among professionals with a quantitative background such as Physics or Applied Mathematics. Hence, it isn’t so surprising that data scientists find this data science programming language handy.


Lastly, Julia is a data science programming language for the future. It was launched about six years back with a keen focus on numerical computing. This data science programming language is popular in the finance industry. Julia is easy to read. It offers good performance and is useful in developing applications for numerical analysis. It is a data science programming language worth keeping an eye on for the future.

Best Language for Data Science Programming

There are many programming languages that can find use in data science. To determine which data science programming language you should learn or use, you may make the following considerations – what is your requirement? Do you need a general programming language or something more specific?

Then there is also the question of quality vs quantity that bogs many professionals – including me as a writer. In this context, this translates to performance vs productivity. A highly productive data science programming language will allow you to deliver more output in a short time. A high-performing data science programming language, on the other hand, will help you develop programs that are less prone to failure.

These are considerations that all data science professionals need to make. Which is the best data science programming language according to you? Do let me know in the comments below.

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