Data science combines statistics, programming, and domain expertise to uncover actionable insights from data. Choosing the right programming language for your data projects can impact everything from analyzing data to deploying models into production.
In this comprehensive guide, we will explore the top 7 languages used by data scientists and data engineers. Whether you‘re performing ad-hoc analysis in a notebook or building an enterprise-grade pipeline, understanding the strengths of each language will help you become a well-rounded data pro.
Let‘s dive in!
A Brief History of Data Science
Before covering the languages, it helps to understand how data science has evolved to where it is today.
While statistics and computer science have long histories, "data science" as a field really began taking shape in the 1990s and 2000s.
As storage became cheaper and datasets grew in size, traditional analysis methods struggled to keep pace. New technologies like Hadoop and Spark emerged to handle "big data" using parallel and distributed techniques.
The demand for people who could extract signal from the noise kicked off the rise of the data scientist role.
Programming languages and tools evolved to make data wrangling and modeling more accessible. As data powered decision-making across industries, fluency with these languages became highly valued.
Now, let‘s look at the key options available to data science practitioners today.
Python – The Most Popular All-Rounder
Python tops surveys as the most popular language among data professionals, with over 56% using it daily according to Kaggle‘s 2021 survey.
Originally created by Guido van Rossum in 1991, Python provides a balance of usability, versatility, and scale. The simple syntax reduces code clutter while enabling programmers to express concepts clearly.
For data tasks, Python offers many advantages:
- Clean, readable syntax makes code easier to write and maintain
- Robust data science ecosystem with pandas, NumPy, SciPy, matplotlib, and more
- Interoperability with languages like R, Scala, Java, and C++
- Web framework support like Django and Flask for building applications
- Growing adoption across roles means more tutorials, resources, and collaborators
Python is a great first language for analysts getting started with programming. The syntax maps closely to expressing data operations. Packages like scikit-learn, TensorFlow, and PyTorch provide machine learning capabilities.
Jupyter notebooks enable an exploratory coding environment while integrating code, visualizations, and text in a shareable document. Python‘s flexibility makes it a "swiss army knife" suitable for most data tasks.
However, Python can underperform with big data pipelines that require heavy computation across clusters. Lower-level languages like Java/Scala may be better suited for distributed systems. But overall, Python provides an unparalleled balance for most data professionals.
No wonder it‘s the top option for mastering data science today!
R – Specialized for Statistics and Visualization
Originally developed as a statistical language, R has grown into an open ecosystem for data analysis and visualization used across industries.
R was initially created by Ross Ihaka and Robert Gentleman at the University of Auckland in the mid-1990s. Since then, it has expanded through contributions from data scientists and academics across the world.
Today, over 16,000 R packages allow practitioners to work efficiently with specialized data techniques. The Comprehensive R Archive Network (CRAN) hosts user-contributed packages to facilitate discovering new functionality.
For machine learning tasks, popular R packages include:
- caret – Cleaning, preprocessing, and modeling framework
- randomForest – Ensemble trees and prediction models
- e1071 – Naive Bayes, SVM, clustering algorithms
- rpart – Recursive partitioning for decision trees
- nnet – Feedforward neural networks
- keras – Deep learning library
RStudio provides a polished IDE tailored for data tasks. R Notebooks allow embedding visualizations, code, and analysis narratives in an interactive document. R Markdown produces publish-ready documents integrating all components.
The Tidyverse collection of packages like ggplot2, dplyr, tidyr, and purrr apply consistent grammar and verbs for manipulating and visualizing data.
While R excels at statistics and visualization, it falls short on general software engineering. But for exploratory analysis and research, R provides unmatched capabilities thanks to its specialized ecosystem.
Julia – Built for Speed without Sacrificing Usability
Julia is a relative newcomer created specifically for high-performance scientific and numerical computing. It combines ease of use with the speed normally associated with lower-level languages.
Julia was born in 2009 out of a collective frustration with trade-offs in existing languages. The creators at MIT sought to build a language well-suited for everything from prototyping models to deploying large-scale applications without compromises.
For data science, Julia hits a sweet spot:
- Approachable syntax – builds on Python and R with clean, readable code
- Fast and parallel – integrated just-in-time compiling and primitives for parallel computing
- Type system – optional static typing for improved performance without verbosity
- Mathematical roots – vector/matrix operations help express data operations cleanly
- Functionality – thousands of packages for data analysis, visualization, and modeling
Usage of Julia has grown rapidly in domains like astronomy, biology, economics, and AI/ML. Performance benchmarks show Julia matching or exceeding C, Java, Python, and R for data workloads.
The plotting ecosystem is also impressive. Packages like Plots.jl and Makie.jl make beautiful visualizations simple.
For those seeking high productivity and performance without code clutter, Julia is emerging as a formidable option for data science.
Java – Bringing Scale and Stability to Enterprise Data
For enterprise-grade data systems, Java remains a trusted workhorse thanks to its portability, performance, and stability.
Created by James Gosling at Sun Microsystems in 1995, Java builds on syntax familiar to C and C++ programmers. It provides improved memory management, security features, and automatic garbage collection.
According to Kaggle‘s survey, nearly 75% of data professionals report using Java. It powers leading big data platforms like Hadoop, Kafka, Cassandra, and Spark.
For data engineers building massive distributed systems, Java brings several advantages:
- Established frameworks like Spring, Struts, and JPA for enterprise development
- Performance at scale through optimized, multi-threaded code execution
- Streaming data libraries like Kafka and Flink for ingesting and processing
- Deep open source ecosystems like Apache projects supporting industrial use cases
- Portability and maturity running consistently across operating systems and chip architectures
The downside of Java is verbosity compared to Python and R. However, Java provides the backbone enabling data systems to handle tremendous scale and throughput for critical business applications.
For this reason, Java remains a trusted choice for building robust data infrastructure relied upon by companies worldwide.
Scala – The Best of Both Worlds
Scala combines object-oriented and functional programming models while running on the battle-tested Java Virtual Machine (JVM).
Created by Martin Odersky in 2001, Scala allows developers to leverage existing Java libraries while using more concise and flexible syntax.
For data engineers building large systems, Scala boosts productivity compared to Java. It facilitates parallel computing through immutable variables, actor model-based concurrency, and lazy computations.
Popular big data Scala frameworks include:
- Apache Spark – Unified engine for large-scale data processing
- Kafka – Distributed messaging system
- Akka – Toolkit and runtime for concurrency and scalability
Scala eliminates much of the ceremony associated with static types while still allowing type safety for mission-critical apps. Furthermore, Scala code compiles to bytecode portable across Java runtimes.
While Scala‘s learning curve prevents casual adoption, its fusion of power and productivity makes Scala a great choice for building data pipelines and distributed data systems.
MATLAB – Convenient Exploratory Environment
MATLAB provides an interactive, consistent experience specialized for data exploration, discovery, and visualization.
Originally created in the 1970s by Cleve Moler, MATLAB excels at matrix manipulations. It integrates an approachable desktop environment with toolboxes providing tested algorithms for signal processing, machine learning, computer vision, and other domains.
For engineers and academic researchers, MATLAB is valued for getting up and running quickly with data analysis and modeling. The combination of documentation, code, and visuals within MATLAB notebooks facilitates experimentation.
However, relying too heavily on MATLAB can result in less maintainable and portable code compared to general-purpose languages. Additionally, the proprietary license model makes MATLAB cost-prohibitive for individual learners or smaller organizations.
While MATLAB skills remain valued in many research and engineering roles today, expanding your toolkit to include more broadly applicable languages opens additional opportunities.
But for quickly prototyping complex techniques, MATLAB provides a convenient playground for discovery before translating to other languages.
C/C++ – The secret sauce powering data science
While not used directly for most analysis, C and C++ are still foundational for the data science ecosystem. They provide low-level control and performance simply not accessible from higher-level Python and R code.
Many core data tools and libraries integrate C/C++ under the hood for compute intensive tasks. For example, Google‘s TensorFlow machine learning framework leverages C++ and CUDA for GPU acceleration while exposing Python APIs.
Other examples include:
- Pandas – C/Cython kernels for fast DataFrame computations
- R tools – Packages like Rcpp and RInside for integrating C++
- Apache Spark – Built on Scala but with mission-critical components in Java/C++
- NumPy/SciPy – foundational Python libraries relying on C/C++ and Fortran
Mastering C/C++ unlocks opportunities to optimize performance bottlenecks that plague data systems. For senior infrastructure engineering roles, fluency in lower-level programming is a huge advantage.
While you may not use them daily, appreciate the power that C/C++ provide behind the scenes!
There are several excellent programming languages for mastering data science:
- Python brings simplicity, versatility, and a vast toolset – great for beginners and professionals alike.
- R delivers unparalleled packages for statistical modeling and visualization tasks.
- Julia combines high productivity with fast parallel performance across data workloads.
- Java powers ultra-scale and real-time data infrastructure through industry-hardened frameworks.
- Scala increases developer productivity for building distributed systems and pipelines compared to Java.
- MATLAB provides an interactive environment to get running quickly with analysis and modeling.
- C/C++ are the secret sauce enabling performance-critical components across data tools.
Rather than focusing on any single language, build fluency across multiple languages suited for different data tasks and roles. A polyglot data scientist can expand their capabilities and opportunities tremendously.
The best way forward is to pick a starting language based on your background and jump into real-world data problems. Let your needs guide which additional languages to layer in over time.
Whichever path you pursue, immerse yourself in the data community around that language – read blogs, attend meetups, participate in forums. Learning alongside others will accelerate your progress and enable you to leverage these powerful languages for unlocking impactful insights.
Happy data science programming! Please reach out with any additional questions.