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Enter any two safe-for-work communities. We’ll place their public descriptions, members, growth, topics and collected rule titles side by side — instantly, from data we already have.

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.

Swap sides New comparison Related to r/MachineLearning Related to r/datascience

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

About this community: r/MachineLearning (#348, about 3.1 million) is for ML research/discussion; its description redirects beginners, AGI, careers, and datasets elsewhere.

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

About this community: r/datascience (#402, about 2.8 million) is for data-science professionals to discuss and debate the field. Rules: respectful, on-topic, use weekly threads, no video links/listicles/surveys, limited self-promo; not Stack Overflow, not homework help, not crowdsourced Google; memes only Mondays. Low volume (~2.9 ppd, n=25).

Supported differences

Aspectr/MachineLearningr/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.

Comparisons we prepared

Hand-written explanations for pairs where the public descriptions and rules support a real distinction: