Kaggle’s 2022 survey found Python and SQL were the most common programming skills among working data scientists in its presentation’s focus group; VS Code led editor use, and scikit-learn was the most popular machine-learning framework. These are findings about Kaggle respondents in 2022—not a current census or a prescription for the best tools today.
What the survey covered—and who its findings describe
Kaggle described the project as its sixth annual industry-wide survey of data science and machine learning. Its executive presentation says the survey was conducted in September 2022 and received 23,997 responses from 173 countries across approximately 43 questions. The official competition listing gives a separate opening and closing window of October 10 through November 27, 2022; those are competition dates, not the fieldwork dates.
For many of the presentation’s findings, Kaggle focused on nearly 2,000 respondents whose current job title was “data scientist.” That smaller group is not interchangeable with all 23,997 survey respondents. The findings describe Kaggle’s respondent community, and the reviewed materials do not establish a probability sampling frame that would make the results representative of all data professionals. See Kaggle’s 2022 survey competition listing and official executive presentation.
Which tools did working data scientists report using?
The presentation describes patterns of reported use, not comparative product tests or recommendations. Its technology findings concern different tool categories, so they should be read separately rather than as a single ranking of complete data-science stacks.
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Programming languages
Python and SQL were the two most common programming skills among working data scientists in the presentation’s focus cohort. Kaggle’s accessible presentation text does not provide exact percentages for these claims.
Editors and notebook environments
More than 50% of the working data scientists in the focus cohort reported using VSCode, according to Kaggle’s 2022 presentation. The same presentation identified Colab as the most popular cloud-based Jupyter notebook environment. These describe distinct categories: VSCode is an editor, while Colab is a hosted notebook environment.
Rank #2
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Machine-learning frameworks and deep learning
Scikit-learn was the most popular machine-learning framework in the survey findings. Kaggle described PyTorch use as growing steadily year over year, and reported increasing popularity of transformer architectures for deep learning on both image and text data. The presentation text does not provide exact percentages for those trends.
Cloud computing and accelerators
Kaggle’s presentation said all major cloud-computing providers saw strong year-over-year growth in 2022 among its data-science respondents. It also described specialized hardware such as TPUs as gaining initial traction. The accessible presentation text does not quantify these changes, name individual provider growth rates, or establish which cloud or accelerator is appropriate for a particular workload.
Recommended Free Tools
Rank #3
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What Kaggle reported about geography and representation
The slides characterized India and Japan as locations where an increasing number of data scientists were living and working. They also characterized the industry as highly gender imbalanced. These are qualitative descriptions in the reviewed presentation text; it does not supply figures here to quantify the geographic changes or the imbalance.
Quick Recap
Best Value
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How to use these findings
- Use them as a dated snapshot: the survey describes reported patterns in 2022, not the state of the workforce in 2026 or another later year.
- Keep the group in view: findings about nearly 2,000 respondents with the current title “data scientist” should not be presented as results for all survey participants.
- Do not confuse adoption with quality: the most-used tool in a category is not necessarily the best choice for a particular project, team, or workload.
- Do not add unsupported precision: the accessible executive presentation text gives a few figures, but not exact adoption rates for most trends.
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