r/MachineLearning vs r/datascience

Side-by-side using public descriptions, members, growth and rule titles from SubMouse. Missing fields show as n/a. Adult communities are excluded.

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Machine learning room vs data science room

r/MachineLearning’s public description points beginners elsewhere and frames the room around machine-learning discussion. r/datascience describes a space for data-science professionals to debate the subject of data science.

In our directory, MachineLearning is larger (about 3.1M, rank #348) than datascience (about 2.8M, rank #402). Activity samples show MachineLearning posting more often (~15 posts/day vs ~3). MachineLearning’s sample mixes text with some images/video and uses project, discussion, research and news flairs; datascience’s sample is mostly text with discussion, career (US/Europe), ML, analysis and tools flairs.

Rule titles: MachineLearning requires title tags, sends beginner/career questions elsewhere and limits non-arxiv links to weekends; datascience steers chatter into weekly threads, bans homework dumps and listicles and limits self-promotion.

Choose r/MachineLearning for ML research and project discussion under its tag and beginner-elsewhere rules.
Choose r/datascience for professional data-science practice and career threads under its weekly-thread rules.
Both sit outside the current Top 100 cut.

Written comparison based on public descriptions and rule titles · updated 2026-11-07.

r/MachineLearning

Machine Learning

Beginners -> /r/mlquestions or /r/learnmachinelearning , AGI -> /r/singularity, career advices -> /r/cscareerquestions, datasets -> r/datasets

Members: 3.1M
7-day growth: +1.1K
Posts/day: 15.2
Directory rank: #348
Created: Jul 29, 2009
Submissions: Text or links

Sample mix: text posts 67%, images 18%, videos 7%

Topics: Artificial Intelligence

Rule titles (short names we collected)

Be nice: no offensive behavior, insults or attacks
Make your post clear and comprehensive
Posts without appropriate tag in title will be removed
Beginner or career related questions go elsewhere
Non-arxiv link posts only allowed on weekends (must be demos)*
Beginner's tutorials and projects go elsewhere
Quality Contribution
Limit self-promotion

Open r/MachineLearning on SubMouse

r/datascience

Data Science

A space for data science professionals to engage in discussions and debates on the subject of data science.

Members: 2.8M
7-day growth: +761
Posts/day: 2.9
Directory rank: #402
Created: Aug 6, 2011
Submissions: Text or links

Sample mix: text posts 72%, links 20%, images 8%

Topics: Data Science

Rule titles (short names we collected)

Be Respectful and Supportive
Stay On Topic
Use the Weekly Thread
No Video Links
No Listicles
No Surveys
Limit Self-Promotion
/r/datascience is not stack overflow
/r/datascience is not a homework helper
/r/datascience is not a crowd-sourced Google
Memes are only allowed on Mondays

Open r/datascience on SubMouse

Supported differences

Aspect	r/MachineLearning	r/datascience
Public focus (from each community’s public description)	Beginners -> /r/mlquestions or /r/learnmachinelearning , AGI -> /r/singularity, career advices -> /r/cscareerquestions, datasets -> r/datasets	A space for data science professionals to engage in discussions and debates on the subject of data science.
Size r/MachineLearning is larger in our directory.	3.1M members	2.8M members
Posting activity (our sample)	15.2/day	2.9/day
Common post types in our sample	text posts 67%, images 18%, videos 7%	text posts 72%, links 20%, images 8%
Topic tags on SubMouse	Artificial Intelligence	Data Science
Selected rule titles (short names we collected)	Be nice: no offensive behavior, insults or attacks; Make your post clear and comprehensive; Posts without appropriate tag in title will be removed; Beginner or career related questions go elsewhere; Non-arxiv link posts only allowed on weekends (must be demos)*	Be Respectful and Supportive; Stay On Topic; Use the Weekly Thread; No Video Links; No Listicles

Rules and posting patterns we can verify

MachineLearning short titles include: be nice; make your post clear and comprehensive; posts without appropriate tag in title removed; beginner or career related questions go elsewhere; non-arxiv link posts only allowed on weekends; beginner tutorials and projects go elsewhere. Our sample flairs include project, discussion, research and news.

datascience: be respectful and supportive; stay on topic; use the weekly thread; no video links; no listicles; no surveys; limit self-promotion; not stack overflow / homework helper / crowd-sourced Google; memes only on Mondays. Our sample flairs include discussion, career US/Europe, ML, analysis and tools.

Where this comparison comes from

Public descriptions and member counts from SubMouse’s safe-for-work directory. Short rule titles and activity samples SubMouse collected. Short titles only.

Members and public descriptions come from SubMouse's safe-for-work directory (as of 2026-11-02 UTC); 7-day growth measured from 2026-10-26. Posts/day and post-type mixes appear when we have an activity sample. Rule lines are short titles, not the full sidebar. Directory rank is listing order — communities outside the current Top 100 open in the info tool instead of a full community page.