The Best Programming Languages For Data Science

best programming languages for data science

Are you considering a career in data science but are not sure which programming languages to learn? Well, learning to code is one of the most important steps for all aspiring data scientists. However, getting started with programming languages for data science can be a daunting task, especially if you do not have prior coding skills and experience.

To choose the right programming language for data science, you need to start by understanding what data scientists do. Basically, this is a technical expert who utilizes statistical and mathematical techniques to analyze, manipulate and extract actionable information from data.

A number of domains fall within the data science realm, from network analysis to machine learning and deep learning, geospatial analysis all the way to natural language processing. Data scientists rely on computers to perform their work, and programming languages are the tools data scientists need to interact with computers and generate results.

Factors to consider when choosing a programming language for data science

Before choosing a programming language for data science, you need to take a number of things into account. Here are some of them:

  • What kind of data science tasks will you be performing?
  • What are your career interests?
  • What programming languages are you already conversant with?
  • What challenges are you ready to tackle?
  • What are your learning goals?

In-Demand Data Science Programming Languages


Python is one of the world’s most widely used programming languages. It has straightforward and intuitive syntax, but it also integrates best with other programming languages. Most data scientists prefer Python, with 16% reported using it in 2018.

Python applications in data science

Python is a high-level programming language that provides an excellent approach for object-oriented programming.

Python is the best language of choice for data science projects and applications as an open-source language.

Python has excellent mathematics, scientific and statistical functionality. Its straightforward syntax makes it easier for anyone with no engineering background to adapt to it and perform quick prototyping.

Python features scientific packages and a deep learning framework in its APIs, making it versatile and highly productive.

This programming language is primarily applicable in sentiment analysis and natural language processing (NLP). It has an extensive library that helps developers solve complex business issues and build a robust system.

Why Python?

There are a few factors that make Python tick, including:

  • Versatility: Python programming language is versatile, readable, straightforward, and well-structured.
  • Vibrant community: Python can be downloaded for free, and you can use it to write asynchronous codes easily and fast. Working with Python is straightforward and hassle-free. It also has a vibrant community of top developers and programmers who provide professional support on multiple forums.
  • Access to a vast library: You can access a Python library for anything you may need. Whether web or mobile development, machine learning, and even game development, you can access many resourceful libraries and even create your own.

Where to learn Python


R, developed in 1993, is the brainchild of Ross Ihaka and Robert Gentleman. R is a free software-cum-programming language that features an extensive catalog of graphical techniques. It focuses on statistical inference, machine learning algorithms, time series, and linear regression.

R is the ultimate programming language for high-level academic institutions and large companies such as Airbnb, Facebook, Google, and Uber. R follows a series of steps in data analysis, including programming, discovering, transforming, and communicating the results.

R Application In Data Science

R is one of the latest cutting-edge softwares out there. As a free tool, it’s widely used by millions of data analysts, researchers, and top brands to solve complex issues. R is applicable in various sectors, including

  • Banking: Banking institutions use R for credit risk modeling and risk analytics.
  • Finance: R’s advanced statistical suite makes it ideal for performing specific financial tasks such as downside risk measurement.
  • Healthcare: Companies in this industry use R to crunch data and process information related to Bioinformatics, Genetics, Epidemiology, and Drug Discovery.
  • Ecommerce: E-commerce brands utilize R to perform product cross-selling analysis.
  • Social media: R is used by social media companies to perform social media analytics, customer targeting, and segmentation.

Where to learn R


Julia provides many features and resources necessary for data science and machine learning. It focuses primarily on scientific and numerical computation. 

Since the language has a math-friendly syntax, it’s the best programming language for R, Matlab, Octave, and  Mathematica users. Julia has its own native machine learning libraries, making it another popular choice for more data scientists.

Application of Julia in Data Science

Julia is a top-level, general-purpose language suitable for code writing. It’s fast and easy to use, especially when

implementing scientific calculations. It caters to the needs of scientific researchers and data scientists and assists in design optimization,  experimentation, and implementation.

Why Julia?

  • Fast and efficient
  • Math-friendly syntax
  • String handling
  • Automatic memory management
  • Native machine learning libraries such as Flux
  • Interoperability

Where to learn Julia


Scala is a multi-paradigm programming language. It contains functional programming and object-oriented programming features. Scala is statically typed with a source code compiled into bytecode and operated by Java virtual machine. As an object-oriented language, every value in Scala is an object.

Scala Application In Data Science

Software engineers and data engineers use Scala alongside Apache Spark to process massive amounts of data. The software’s primary focus is to grow with user demands from small script writings to large data processing systems.

Scala is also used in web development and distributed computing. It supports the data engineering infrastructure for many companies across different industries.

Why Scala?

  • Easy to set up
  • Highly functional
  • Excellent IDE Support
  • Better quality codes
  • It helps minimize the risks of inherently immutable objects and thread-safety concerns.

Where to learn Scala


Structured Query Language or SQL is a communication software and standard language for database management systems. SQL is used for executing various operations on the data, including updating or deleting records, creating tables, etc.

SQL Application In Data Science

Working with data requires it to be extracted from databases where it’s stored. SQL comes into play as it supports Relational Database Management, a vital part of Data Science. Data scientists can utilize SQL commands to control, define, and manipulate a database.

Furthermore, many companies use SQL as the ultimate intelligence tool for data management and in-office operations. Examples of relational database management systems for SQL include Microsoft Access, Microsoft SQL Server, Ingress, Oracle, and  Sybase.

Why SQL?

  • Portable
  • No coding skills are required, ideal for beginners
  • Interactive language
  • Multiple data views
  • Faster Query Processing

Where to learn SQL


JavaScript is a text-based programming language that assists data scientists in performing interactive web pages. JavaScript adds interactive elements to web pages and supports user engagement.

Examples of common JavaScript include the Amazon search box, Twitter feeds, and video news recaps on The New York Times. JavaScript was designed to support user experience by making web pages more engaging and less static.

JavaScript Application In Data Science

JavaScript is primarily suitable for web-based apps and browsers.

Java is the language of choice for most data scientists when it comes to security applications like fraud detection and network security. It also helps with web servers and application development.

Why JavaScript?

  • Fast and versatile
  • Easy to use
  • Interoperability
  • Extended Functionality
  • Rich user interfaces

Where to learn JavaScript


Dennis Ritchie created the C programming language to help design system applications that connect with drivers, kernels, and similar hardware devices. C is a powerful,  general-purpose programming language suitable for operating systems, compilers, databases, and other forms of system software development.

C Application In Data Science

C is primarily suitable for beginner programmers. It can be used to quickly implement machine learning algorithms that would otherwise require more time to process. C is typically used to perform the following functions:

  • Language Compiler Design
  • Operating system design
  • Browser design
  • GUI design
  • Computer game development
  • Development of New Programming Languages

Why C?

C has several notable advantages such as:

  • Portability
  • Easy to understand and write
  • Features extensive libraries
  • Ideal for quick execution of algorithms and data structures
  • It’s a procedure-oriented language
  • Has extensive memory allocation

Where to learn C


C++ is a general, case-sensitive programming language that supports different types of programming. It’s a middle-level language focusing on both high and low-level language features.

C++ Application In Data Science

Like JavaScript and C, C++ is a powerful general-purpose programming language. Its mostly ideal for developing web browsers, operating systems, and web games. 

C++ supports different programming techniques, including functional, procedural, and object-oriented. C++ is both powerful and flexible and assists in developing the following:

  • Windows applications
  • Device drivers
  • Client-Server application
  • Embedded firmware

Why C++?

The advantages of using C++ are many. Some include:

  • Portability
  • Object-oriented
  • Mid-level programming language
  • Memory management support (supports Dynamic Memory Allocation)
  • Multi-paradigm programming language
  • Scalability
  • Fast and efficient
  • Vibrant support community
  • Extensive native library
  • Has a vast job market

Where to learn C++


MATLAB® is not a programming language but a programming platform that engineers and scientists use to develop products and analyze how they impact the world. The core of MATLAB is the matrix-based MATLAB language that supports the natural expression of computational mathematics.

MATLAB Application In Data Science

MATLAB enables data scientists to deploy enterprise applications and embedded devices to transfer research ideas to production. It also supports integrations with Simulink® and Model-Based Design.

The primary use of the MATLAB programming platform is data analysis, development of algorithms, and creation of models and applications. You can also use MATLAB to explore data, develop machine learning models and perform predictive analytics.

With MATLAB, data professionals can perform matrix manipulations, plotting,  algorithm implementation, and user interface creation. The following are a few examples of industries that use MATLAB in data science:

  • Biotech and pharmaceuticals
  • Aerospace and defense
  • Communication
  • Energy production


  • Easy to use
  • MATLAB compiler
  • Predefined functions
  • Device-Independent Plotting
  • Platform independence

Where to learn MATLAB®

Best programming languages for data science: Final thoughts

Data science is one of the most on-demand careers right now. If you are interested in starting a career in data science, then you need to start by becoming proficient in various programming languages. Eventually, your data science environment, interests, platform framework and the desire to learn will lead you to specialize in specific programming languages.

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