Top 10 Data Science Programming Languages for 2019

Learn these Top 10 Data Science Programming Languages for learning data science in 2019. These programming language is used by Data Scientist along with main data science modeling language to develop productive applications.

Top 10 Data Science Programming Languages for 2019

Learning Data Science: Top 10 Data Science Programming Languages for 2019

If you are an aspiring data scientist, you should have expertise in some sought-after programming languages. Here we are going to showcase some of the useful programming languages for that the data scientists should master.


Python is the most popular programming language for data scientists. Considered by many as the easiest programming language to read and learn Python is learned widely by data scientists thanks to the dynamic capacity of Python to interface with sophisticated algorithms written in C or Fortran.


It is the second most popular programming language among data scientists following Python. This programming language was conceptualised basically by the R Foundation for the purpose of Statistical Computing and so, it provides data scientists an array of ready to use statistical models.

Top 10 Data Science Programming Languages


Java, the grand old programming language that is used for almost everything on the web and digital interfaces is also learned by data scientists for backend development. When you need to leverage the data science for large backend systems and enterprise mobile as well as web apps, Java comes as an invincible choice.


SQL or Structured Query Language is another popular query language used by the data scientists. It is mainly used by the data scientists for storing and querying data and for managing large databases.  SQL is one of the common language skills for most data science professionals all over the world.


Just like the R, SAS is often preferred by data scientists for Statistical Analysis. Being one of the earliest languages built for statistical modelling SAS is widely used by enterprise software development companies for a variety of purposes including predictive modelling, advanced data analytics and business intelligence.


In case you want to make your foray into the field of data science without really learning a new language from scratch, JavaScript is the ideal one to start with. JavaScript offers a lean, lightweight and easy approach to object-oriented programming and comes as a really handy tool for applying sophisticated AI or Machine Learning models in the mobile apps or browser.

C Programming Language

C is one of the earliest programming languages till date that went through an entire spectrum of evolution and has played elementary role in giving birth to most other modern programming languages. A data scientist should learn C with the objective of learning something elementary and basic that can help in getting introduced to other languages easily. As for programming capacity, C is widely used now in embedded systems of devices and firmware.


Scala runs on Java Virtual Machine (JVM) and so Scala can run wherever Java runs. With a larger user base it is now widely used for building sophisticated algorithms and data-driven programs. 


If as a data scientist you need to learn a solid language that can help building sophisticated algorithms based on complex math, this is the invincible choice to opt for. This is a hard-core language that can play an effective role in dealing with complex systems mathematicians and statisticians deal with.


Julia is a dynamic programming language used mainly for statistical analysis and computational tasks. It is rapidly gaining popularity among the data scientists because of the core capabilities such as random number generation, signal processing, string processing, etc.

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