Last updated: September 2026. Written by Josh Hutcheson, OnlineCourseing editor. Language rankings and job-market data re-checked at the source (Stack Overflow 2025, TIOBE September 2026, 365 Data Science 2026) on 22 September 2026. See our review methodology.
By Josh Hutcheson · E-Learning Specialist
Reviewing online learning platforms since 2019. Review methodology
THE SHORT ANSWER
Bottom line: learn Python and SQL first. Together they cover the large majority of data science work and job postings. Add R for statistics-heavy work, and specialist languages such as Scala, Julia, SAS or MATLAB only when a role or industry needs them.
- Python: the most requested language in data scientist job postings (365 Data Science, 2026) and #1 in the TIOBE index.
- SQL: moved ahead of R to become the second most requested language in 2026 (365 Data Science).
- Momentum: Python adoption rose 7 percentage points in a year among developers (Stack Overflow, 2025).
- R: climbed to #9 in the September 2026 TIOBE index, from #13 a year earlier.
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Three independent sources point the same way. In 365 Data Science’s analysis of more than 1,000 data scientist job postings, Python is the most requested language, and in 2026 SQL moved ahead of R into second place, reflecting how much data science work now happens directly in databases and data pipelines. The same research found SQL was the most in-demand skill across all data roles in 2025, and it has kept that position in 2026 (365 Data Science).
Among developers generally, Python’s adoption accelerated by 7 percentage points from 2024 to 2025 after a decade of steady growth; in the 2025 Stack Overflow survey, 58.6% of all respondents used SQL and 57.9% used Python (Stack Overflow). The TIOBE index of language popularity for September 2026 ranks Python first, SQL eighth and R ninth, up from 13th a year earlier (TIOBE).
The 8 programming languages for data science
1. Python: the default choice
Python is the most widely used language for data science because one language covers the whole workflow: pandas and Polars for data wrangling, NumPy and SciPy for numerical work, Matplotlib and Seaborn for charts, scikit-learn for classical machine learning, PyTorch and TensorFlow for deep learning, and a large set of libraries for working with large language models. Its readable syntax makes it approachable for beginners, and its dominance in AI means new tools usually appear in Python first. Its main weakness, raw speed, matters less than it sounds because the heavy lifting in data libraries runs in compiled code underneath.
Learn it with: the IBM Data Science Professional Certificate on Coursera, a 12-course program covering Python, SQL, data analysis, visualization and machine learning, or DataCamp’s Associate Data Scientist in Python track for shorter interactive lessons. See also our guide to the best Python courses on Udemy.
2. SQL: the language of data
Most organizational data sits in relational databases and cloud data warehouses, and SQL is how you get it out, filter it, join it and summarize it. Data scientists use SQL daily to build datasets before any modeling begins, and increasingly to transform data inside the warehouse itself. It is also the easiest of these languages to start with: a beginner can write useful queries within days. Employers treat it as a baseline skill, which is why it now ranks second only to Python in data scientist postings.
Learn it with: UC Davis’s SQL for Data Science on Coursera or DataCamp’s SQL courses. Our ranking of the best SQL courses compares more options.
3. R: the statistician’s language
R was built for statistics, and it remains excellent for statistical modeling, experimental analysis and publication-quality graphics through packages such as ggplot2 and the tidyverse. It is especially strong in academia, biostatistics, epidemiology and parts of finance. R has lost ground to Python in general data science job postings, falling behind SQL in 2026, but its TIOBE ranking has risen, suggesting it remains healthy in its core fields.
Learn it with: Johns Hopkins University’s R Programming on Coursera, part of its long-running data science program, or DataCamp’s Associate Data Scientist in R track. See also our guide to the best statistics courses.
4. Julia: speed for scientific computing
Julia was designed to combine Python-like readability with speed close to C, which makes it attractive for simulation, optimization, differential equations and other heavy numerical work. TIOBE notes that Julia, Python and R have all taken market share from MATLAB, and its September 2026 headline asks whether Julia will re-enter the top 20; it sits at 21st (TIOBE). The catch is a far smaller job market and library ecosystem than Python.
Learn it with: the University of Cape Town’s Julia Scientific Programming on Coursera, or see our guide to Julia courses.
5. Scala: for big data engineering
Scala runs on the Java Virtual Machine and is the language Apache Spark is written in, so it appears most in data engineering roles that build large-scale processing pipelines. Many Spark users now work through its Python interface, PySpark, which has narrowed Scala’s role; it ranks 44th in TIOBE. Learn it if you are aiming at Spark-heavy data engineering teams. Our guide to the best Scala tutorials covers where to start.
6. Java: the enterprise backbone
Java is rarely used for exploratory analysis, but much of the big data infrastructure that data scientists rely on, including Hadoop, Kafka and many production systems, runs on the Java Virtual Machine. Data scientists who deploy models into large enterprise systems, or who move into data engineering, benefit from knowing it. See our ranking of the best Java courses.
7. SAS: still strong in regulated industries
SAS is a commercial analytics platform with its own language, long established in pharmaceuticals, banking, insurance and government, where validated and auditable processes matter. It ranks 24th in TIOBE. Few new teams choose SAS today, but if you target clinical trials or regulated finance, SAS experience still appears in job postings.
8. MATLAB: engineering and signal processing
MATLAB is widely used in engineering, control systems, signal and image processing, where its toolboxes are mature. It is commercial software, and TIOBE observes that Python, R and Julia have all taken a share of MATLAB’s traditional territory; it ranks 27th. It is most relevant if you work in an engineering organization that already uses it. See our guide to MATLAB courses.
The languages compared
| Language | Best for | Main strength | Main weakness | TIOBE Sept 2026 |
|---|---|---|---|---|
| Python | General-purpose data science, machine learning, AI | Huge library ecosystem; most requested by employers | Slower than compiled languages for raw computation | #1 |
| SQL | Querying and preparing data in databases | Essential everywhere data lives in databases | Not for modeling or general programming | #8 |
| R | Statistics, visualization, research | Outstanding statistical packages and plotting | Less used for production software | #9 |
| Julia | High-performance scientific computing | Speed close to C with readable syntax | Small job market and ecosystem | #21 |
| SAS | Regulated analytics in pharma, banking, government | Validated, long-established in regulated industries | Commercial license; declining new adoption | #24 |
| MATLAB | Engineering, signal processing, simulation | Strong toolboxes for engineering math | Commercial license; losing ground to Python and Julia | #27 |
| Scala | Big data engineering with Apache Spark | Spark is written in Scala; strong typing | Steeper learning curve; niche outside Spark | #44 |
| Java | Big data platforms and production systems | Runs large-scale enterprise data systems | Verbose for exploratory analysis | #4 |
Other languages appear at the edges of data science: JavaScript for interactive web visualizations, C++ and Rust for high-performance libraries, and Go for data infrastructure, covered in our guide to the best Go courses.
Python vs R: a closer look
| Python | R | |
|---|---|---|
| Designed for | General-purpose programming | Statistical computing |
| Strongest at | Machine learning, AI, automation, production code | Statistical modeling, experiments, graphics |
| Key libraries | pandas, NumPy, scikit-learn, PyTorch | tidyverse, ggplot2, caret, Shiny |
| Where it dominates | Tech companies, AI teams, most job postings | Academia, biostatistics, public health |
| Learning curve | Gentle, readable syntax | Quirky syntax, but quick for analysis |
| Job-market signal | Most requested in data scientist postings | Now behind both Python and SQL (365 Data Science, 2026) |
The two are not rivals so much as tools with different centers of gravity. Many teams use R for analysis and reporting and Python for machine learning and deployment, and notebooks make it easy to move between them. If you are unsure, start with Python: it keeps more career doors open.
Common mistakes when choosing a language
- Language-hopping. Switching between Python, R and Julia every few weeks leaves you shallow in all three. Pick Python or R and stay with it until you can complete a project.
- Skipping SQL. Many beginners jump straight into machine learning and then struggle to get data out of a real database. Employers notice.
- Learning syntax without data. Tutorials alone do not build skill; practice on real, messy datasets from the start.
- Chasing niche languages for a CV. Scala, Julia or SAS help only if your target roles use them; check job postings in your area first.
- Ignoring statistics. Knowing a language is not the same as knowing what analysis to run. Statistics is what turns code into sound conclusions.
Which language should you learn first?
| Your goal | Learn first | Then add |
|---|---|---|
| Data analyst | SQL | Python, plus a BI tool |
| Data scientist | Python | SQL, then statistics depth |
| Machine learning engineer | Python | SQL, software engineering, cloud |
| Data engineer | SQL and Python | Scala or Java, Spark, cloud platforms |
| Statistician or researcher | R | Python for machine learning |
| Scientific computing | Python | Julia for performance |
| Pharma or regulated finance | Python or R | SAS if employers require it |
For almost everyone, the answer is Python plus SQL. The two complement each other: SQL gets and shapes the data; Python analyses and models it. Learning both to a solid level is more valuable than a shallow knowledge of five languages.
A practical learning path
- Weeks 1 to 4: SQL basics. SELECT, filtering, joins, grouping and aggregation on a real dataset.
- Weeks 3 to 10: Python fundamentals. Variables, loops, functions, then pandas for cleaning and analyzing data.
- Weeks 8 to 14: statistics and visualization. Distributions, hypothesis testing and regression, with charts in Python.
- Weeks 12 to 20: machine learning. scikit-learn for classification, regression and evaluation.
- Ongoing: projects. Build two or three portfolio projects that combine SQL and Python on data you care about.
- Later: specialize. Add R, Spark with Scala, or Julia only if your target role calls for it.
These timelines assume several hours of study a week and will vary; the order matters more than the pace. Our guides to data science tools and data visualization courses cover the software you will use alongside these languages, and our guide to unstructured data covers working with text, images and audio.
Careers: why these languages pay off
The US Bureau of Labor Statistics reports a 2025 median wage of $120,230 for data scientists and projects employment growth of 35% from 2025 to 2035, much faster than the average for all occupations (BLS). Programming skills are also spreading beyond data scientist titles: 365 Data Science notes they are moving from specialist machine learning roles down to data analyst roles (365 Data Science). For the bigger picture, see is data science a good career and how to become a data scientist without a degree.
Best courses to learn data science languages
| Course | Language | Best for |
|---|---|---|
| IBM Data Science Professional Certificate (Coursera) | Python and SQL | Beginners wanting a full program and certificate |
| Associate Data Scientist in Python (DataCamp) | Python | Short interactive practice sessions |
| SQL for Data Science (UC Davis, Coursera) | SQL | A focused SQL foundation |
| R Programming (Johns Hopkins, Coursera) | R | Statistics-oriented learners |
| Associate Data Scientist in R (DataCamp) | R | Hands-on R practice |
| Julia Scientific Programming (University of Cape Town, Coursera) | Julia | Scientific and numerical computing |
Coursera removed its free audit option for most courses in 2025, so check the price or trial terms before enrolling. For broader comparisons, see our rankings of the best data science courses, data analytics courses, machine learning courses and coding courses.
Frequently asked questions
What is the best programming language for data science?
Python is the best first choice for most people: it is the most requested language in data scientist job postings and covers everything from data cleaning to machine learning. Pair it with SQL, which 365 Data Science’s 2026 analysis found is now the second most requested language, ahead of R.
Should I learn Python or R for data science?
Learn Python first unless you are heading into statistics-heavy research, biostatistics or academia, where R remains strong. Python is more versatile and more requested by employers; R is excellent for statistical analysis and visualization, and many data scientists eventually use both.
Is SQL a programming language for data science?
SQL is a query language rather than a general-purpose programming language, but it is essential for data science because most organizational data lives in databases. 365 Data Science found SQL was the most in-demand skill across data roles in 2025 and it remained so in 2026.
Is Julia worth learning for data science?
Julia is fast and well designed for numerical and scientific computing, but it has a much smaller job market than Python or R. It ranked 21st in the September 2026 TIOBE index. Learn it if you work on high-performance scientific problems; otherwise start with Python.
How many programming languages does a data scientist need?
Most data scientists work mainly in two: Python and SQL. R, Scala or Java become useful in specific roles, such as statistical research or big data engineering, but depth in one general language plus SQL matters more than a long list.
Do data scientists need to know Java or C++?
Usually not. Java and Scala are common in big data engineering, for example with Spark, and C++ matters for performance-critical libraries, but typical data science work is done in Python and SQL.
The verdict
The data science language question has a clear answer in 2026: Python and SQL. Python is the most requested and most versatile, SQL is the skill employers ask for across every data role, and together they cover most real work. R remains the best tool for serious statistics, and Scala, Java, Julia, SAS and MATLAB each earn their place in specific industries and roles. Start with the core two, build projects, and add a specialist language when your career path calls for it.