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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →In Kaggle’s 2022 Machine Learning and Data Science Survey, Python and SQL were the two most common programming skills reported by data scientists. The survey’s cleaned dataset contained 23,997 responses, making it a useful snapshot of the field—not a census or proof that either language is best for every task. For a practical learning path, consider Python for a broad data-science workflow, SQL for working with data in databases, and R as a strong option for statistical computing.
What the 2022 surveys say
Kaggle’s 2022 State of Machine Learning and Data Science report says Python and SQL remained the two most common programming skills for data scientists. Kaggle’s survey overview notes that the survey ran in 2022 and that cleaning left 23,997 responses. That makes Kaggle the most directly relevant source here, but its finding establishes reported prevalence, not a universal ranking by quality or performance.
A separate source offers broader context, but measures a different population. In the Stack Overflow Developer Survey 2022, 71,547 respondents answered the programming-language question. Across all respondents, 48.07% reported extensive development work with Python in the past year, 49.43% with SQL, and 4.66% with R. These are broad developer figures, not data-scientist-specific usage rates, so they should not be combined with Kaggle’s finding as though the surveys measured the same group.
How the leading options fit data science
| Language | What it is useful for | What the 2022 evidence establishes |
|---|---|---|
| Python | A broad data-science workflow, including analysis and machine-learning work. Choose it when the libraries and tools required for your project or team support it. | Kaggle identifies it as one of the two most common data-science programming skills. The cited report passage does not establish an exact share. |
| SQL | Querying and manipulating data stored in databases. It complements languages used for analysis rather than serving as a direct substitute for every part of a data-science workflow. | Kaggle identifies it alongside Python as one of the two most common skills. The cited report passage does not establish an exact share. |
| R | A substantial alternative for statistical computing and analysis. It can be a good fit when your work, collaborators, or required tools already use it. | The Stack Overflow survey reports 4.66% for all respondents’ extensive development work in the past year; that is not a data-scientist-specific estimate. The retrieved Kaggle finding does not establish a precise R percentage. |
Should you learn Python or R?
Start with the work you want to do, not a popularity figure. Python is a reasonable first choice if you want one language for a broad range of data-science tasks and the libraries or systems you need are available in its ecosystem. R is worth considering when statistical analysis is central to your work or your team already relies on R tools. The survey findings establish Python’s prevalence among Kaggle’s data-science respondents, but they do not show that Python is superior to R for every statistical task.
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- Check which language your employer, collaborators, or target projects use.
- Look for the libraries and tools required for your specific analysis or model.
- Take existing skills into account: the best next language may be the one that helps you complete useful work sooner.
Do data scientists need SQL?
SQL has a distinct role: it lets you query and work with data stored in databases. Because Kaggle identified SQL alongside Python as one of the two most common data-science programming skills in 2022, it is a sensible skill to add if your work involves database-backed data. It complements Python or R; learning SQL does not mean you must give up either language.
What about other programming languages?
The available Kaggle finding supports Python and SQL as the leading reported skills, but does not provide precise, comparable shares for every alternative. These sources therefore do not support a complete numerical ranking of languages for data science in 2022. Consider another language when a particular task, required system, or team environment calls for it rather than treating a popularity ranking as a checklist.
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A Python learning resource
If you choose Python and want a book-length reference, O’Reilly’s Python Data Science Handbook, 2nd Edition is aimed at beginner-to-intermediate readers. The publisher lists it as 588 pages, published in December 2022, and covering IPython/Jupyter, NumPy, pandas, Matplotlib, scikit-learn, and related tools. It is a Python resource, not a comparison of Python, SQL, and R.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to read the 2022 ranking
Both surveys describe historical patterns in 2022, not current popularity in 2026. Kaggle’s data-science-focused result is the better fit for this question; Stack Overflow’s figures are useful only as a separate, broader developer comparison. Neither source is a controlled performance test, so neither establishes which language is fastest or best for a particular person or project.
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