Data science is 1 of the fastest-growing fields. The US Bureau of Labor Statistics (BLS) projects 34% increase job growth through 2034. For anyone breaking into the field or leveling up, that’s a lot of doors opening.
The harder question is which programming language to learn first. For data scientists, Python is the dominant choice, but it’s far from the only answer. And it isn’t necessarily the best programming language for all data science roles.
Language choice means more than people expect. The right 1 speeds up everything from cleaning messy data to shipping a model. The wrong 1 has you fighting your tools instead of doing the work.
This article covers 10 of the top programming languages for data science, ranked by real-world relevance, community adoption, and use-case fit.
Key Points
- Python and SQL are 2 languages worth learning early. Python is the most widely used language in data science, while SQL tends to be non-negotiable across data roles.
- Other top data science programming languages include Julia, Scala, Java, MATLAB, SAS, JavaScript, and C++.
- Choosing the best programming language for data science comes down to understanding your domain, job market demand, and your team’s existing stack.
- Most data scientists become proficient in several languages throughout their career. You might start with 1 or 2 languages that feel most relevant and go from there.
How to Choose the Right Data Science Programming Language
With about 9,000 programming languages in existence, picking where to start can feel paralyzing. The good news is that you don’t have to learn them all to become a successful data scientist. In fact, spreading yourself across too many (or doubling down on the wrong 1) can stall your progress.
Choosing the best languages for your data science career comes down to 4 things:
- Your use case: Every programming language has its primary purpose and limitations. For example, Python is best for machine learning (ML) and data visualization. SQL is for data storage and processing in relational databases. Meanwhile, R is primarily used in statistics and analysis.
- Your existing stack: A good tech stack needs the right programming language to add functionality to your projects. For example, JavaScript is good for full-stack or front-end development, while Python is good for back-end development.
- Job market demand: Some languages get you more interviews than others. Python, SQL, and JavaScript consistently top hiring surveys, including Stack Overflow’s 2025 Developer Survey.
- The strength of the ecosystem: A mature language comes with mature libraries, active forums, and answers waiting on Stack Overflow when you hit a wall. That’s a real advantage when you’re learning.
Still weighing your options? Here’s how data scientists and data engineers differ in skills and career outlook.
The 10 Best Programming Languages for Data Science
You don’t need to learn all 10. Many data scientists work fluently in 2 or 3, with passing familiarity in a few others. Use the list to figure out where to start and what to leave for later.
1. Python
Python is the dominant programming language for data science, and for good reason. It’s an open-source and general-purpose language, making it versatile. It’s also beginner-friendly, and you’ll find it in job descriptions for many data science jobs.
- Percentage of job listings mentioning this skill: 56.7% (365 Data Science)
- Primary use cases: Machine learning, natural language processing (NLP), data wrangling, data visualization, statistical analysis, deep learning
- Standout libraries/tools: NumPy (advanced mathematical features), pandas (database manipulation), TensorFlow (ML and deep learning algorithm development), scikit-learn (ML algorithm development), Matplotlib (data visualization)
- Best for: Almost everyone, beginners included
2. R
R is the go-to language for statistical analysis and academic research. It’s open-source like Python, but where Python is a general-purpose language that happens to be great at data science, R is built specifically for it.
- Percentage of job listings mentioning this skill: 33% (365 Data Science)
- Primary use cases: Data visualization, data wrangling and cleaning, statistical modeling (linear/nonlinear), bioinformatics, graphical techniques
- Standout libraries/tools: Dplyr (data manipulation), ggplot2 (visualization)
- Best for: Statisticians, researchers, and analysts working in academia or heavily data-driven fields like health care or finance
3. SQL
SQL is foundational for querying and managing structured data in relational databases (like MySQL). Data analysts, engineers, and scientists frequently use it in their workflows.
- Percentage of job listings mentioning this skill: 30.4% (365 Data Science)
- Primary use cases: Data extraction, data querying, data transformation, data analysis, data manipulation, pipeline work
- Standout libraries/tools: MySQL database management system, Python SQL Libraries
- Best for: Every data professional, regardless of specialization; pair with Python or R for full workflow coverage
4. Julia
Julia is 1 of the fastest programming languages built for numerical computing, but it’s designed for more niche cases. It’s been gaining more traction in academia and high-performance computing (HPC) environments.
- Percentage of job listings mentioning this skill: 0.7% (365 Data Science)
- Primary use cases: Numerical computing, scientific research, deep learning, interactive computing
- Standout libraries/tools: Pkg.jl (package manager), BinaryBuilder (native binary builders with 45+ toolchains for major platforms), Yggdrasil (development and distribution of libraries and binaries in Julia’s ecosystem)
- Best for: Researchers and engineers working with large-scale simulations, computational biology, or performance-critical ML pipelines
5. Scala
Scala powers Apache Spark, the standard for big data processing at scale. If you work with datasets too large to fit on 1 machine, Scala helps handle them.
- Percentage of job listings mentioning this skill: 3.7% (365 Data Science)
- Primary use cases: Big data processing, software development, data validation, NLP and ML model training, distributed computing and data engineering pipelines
- Standout libraries/tools: Apache Spark and Spark MLlib (large-scale ML), Breeze (numerical processing, similar to NumPy)
- Best for: Data engineers and scientists working with massive datasets in enterprise environments
6. Java
Java is a staple of enterprise software that runs across many environments, including desktop and web enterprise apps. It powers production ML pipelines, data engineering, and scalable data platforms.
- Percentage of job listings mentioning this skill: 9.3% (365 Data Science)
- Primary use cases: Production ML pipelines, scalable data platforms, data engineering, distributed computing
- Standout libraries/tools: Weka (open-source machine learning software), Deeplearning4j (deep learning framework)
- Best for: Engineers in large organizations where Java is already in the stack
7. MATLAB
MATLAB is a commercial language built for numerical computing, simulation, and engineering work. It’s especially prominent in academia and in industries such as aerospace and automotive. While scalable and fairly user-friendly, MATLAB isn’t free.
- Percentage of job listings mentioning this skill: 1.9% (365 Data Science)
- Primary use cases: Numerical computing, signal processing, simulation, algorithm development, engineering data analysis
- Standout libraries/tools: Simulink (model-based design), interactive apps (for data analysis/visualization), built-in visualizations graphics, AI agents
- Best for: Scientists and engineers who need robust numerical computation and simulation tools
8. SAS
SAS is an enterprise-grade analytics platform geared toward regulated industries and statistical reporting. While SAS has been around for quite some time, it’s still a somewhat niche data science language. In fact, it’s mostly used in health care, pharma, banking, and government. Unlike many of the languages on this list, it’s not open-source.
- Percentage of job listings mentioning this skill: 5.1% (365 Data Science)
- Primary use cases: Business intelligence, advanced numerical computing processes, statistical data analysis
- Standout libraries/tools: SAS Viya (data management and visualization), Base SAS (core language), SAS/STAT (statistical procedures)
- Best for: Analysts in compliance-heavy fields where SAS is an industry standard
9. JavaScript
JavaScript is the language of the browser. If you need to ship interactive dashboards, embed visualizations on the web, or run machine learning models client-side without a server round trip, JavaScript is how you do it.
Not only is JavaScript versatile, but it also integrates well with other languages. It also has libraries that can support ML and deep learning, as well as front- and back-end development.
- Percentage of job listings mentioning this skill: 4.7% (365 Data Science)
- Primary use cases: Data visualization, in-browser ML, dashboard development, front-end development, back-end development
- Standout libraries/tools: D3.js and Chart.js (data visualization), TensorFlow.js (browser-based ML), Keras (ML and deep learning)
- Best for: Full-stack developers who want to integrate data science outputs directly into web applications
10. C++
C++ is an object-oriented language, though it isn’t a data science language in the traditional sense. It’s primarily used in performance-critical ML applications. It’s also key in building the infrastructure that other languages rely on. For example, TensorFlow was built on C++ and JavaScript.
- Percentage of job listings mentioning this skill: 0.6% (365 Data Science)
- Primary use cases: Statistical/data tool development, ML framework development, application development
- Standout libraries/tools: Eigen (linear algebra), mlpack (machine learning), Dlib (ML and computer vision), TensorFlow C++ API (production model deployment)
- Best for: ML engineers focused on model deployment and optimization at scale
| | Primary use case | Difficulty level | Best for |
| Python | ML NLP Data wrangling Data visualization Statistical analysis Deep learning | Beginner-friendly | Almost everyone |
| R | Statistical analysis Academic research Data visualization Data wrangling and cleaning Statistical modeling (linear/nonlinear) Bioinformatics Graphical techniques | Beginner to intermediate | Statisticians, researchers, and analysts working in academia or heavily data-driven fields |
| SQL | Data extraction Data querying Data transformation Data analysis Data manipulation | Beginner-friendly | Every data professional (regardless of specialization) |
| Julia | Numerical computing Scientific research Deep learning Interactive computing | Beginner to intermediate | Researchers and engineers working with large-scale simulations, computational biology, or performance-critical ML pipelines |
| Scala | Big data processing Data validation NLP and ML model training Distributed computing and data engineering pipelines | Intermediate | Data engineers and scientists working with massive datasets in enterprise environments |
| Java | Production ML pipelines Data engineering Scalable data platforms Distributed computing | Beginner-friendly | Engineers in large organizations where Java is already in the stack |
| MATLAB | Numerical computing Statistical analysis Data visualization Deep learning Signal processing Data analysis Algorithm development | Beginner to intermediate | Scientists and engineers who need robust numerical computation and simulation tools |
| SAS | Business intelligence Advanced numerical computing processes Statistical data analysis | Intermediate | Analysts in compliance-heavy fields where SAS is an industry standard |
| JavaScript | Data visualization In-browser ML Deep learning Dashboard development | Beginner-friendly | Full-stack developers who want to integrate data science outputs directly into web applications |
| C++ | Statistical/data tool development ML framework development Application development | Intermediate to advanced | ML engineers focused on model deployment and optimization at scale |
Start Building Your Data Science Skills
Before choosing programming languages to further your data science career, consider your goals. Are you most interested in machine learning and deep learning? Does data analysis and visualization appeal to you? How about statistical research?
Knowing your biggest goal can help you choose which language to pick first. Once you’re proficient in that, you can then move on to the next.
And when you’re ready to see what opportunities are out there, explore Intuit’s data science jobs and resources.
FAQs
Do data scientists need to learn more than 1 programming language?
Most data scientists end up learning 2 or 3 programming languages. If you take an online bootcamp, you might only learn 1 (like Python). If you pursue more formal education, chances are your curriculum will cover the top programming languages for data scientists today. A good starting point might be to research job postings to see what employers are looking for in your industry.
How does choosing the wrong language impact data science projects?
When you choose the right programming language, your data science workflows become faster and more efficient. But when you choose the wrong 1—or at least 1 that’s less relevant to what you’re doing—you end up with friction. You risk complicating projects and going over your budget or timeline. You might also have trouble scaling. That’s why taking the time to choose the right language for the task is crucial.
How does programming language difficulty impact software development?
The harder a programming language is to learn, the longer it takes to implement. This could mean longer project timelines and higher costs. This doesn’t necessarily mean that beginner-friendly languages are always the way to go, however. It just means that difficult or complex languages don’t automatically translate to the “right” choice.