r/datascience for B2B sellers
2,773,335 members · rules and posts checked 1 November 2026
r/datascience is a data science community with 2,773,335 members. Sellers can take part, but promotion is limited: most of what you post should be non-promotional, and it needs to be relevant and useful.
Common requests here are for learning resources, technical tools, workflow advice, machine learning and MLOps topics, business intelligence automation, and professional education in data and AI.
Self-promotion: allowed with limits
Self-promotion is allowed only in limited amounts under Reddit’s 1:9 guideline, meaning most of your participation should be non-promotional. Sellers should contribute meaningfully to discussions and avoid excessive promotion or off-topic posts.
1Be Respectful and Supportive
Be respectful and supportive when interacting with others.
2Stay On Topic
Only post content related to data science.
3Use the Weekly Thread
Career-transition and career-advice questions should go in the weekly thread.
4No Video Links
Do not post video links.
5No Listicles
Do not post listicle-style collections of free resources.
6No Surveys
Do not post surveys.
7Limit Self-Promotion
Keep self-promotion limited and follow Reddit’s 1:9 self-promotion guideline.
8/r/datascience is not stack overflow
Do not use the subreddit as a substitute for Stack Overflow.
9/r/datascience is not a homework helper
Do not ask for homework help.
10/r/datascience is not a crowd-sourced Google
Do not ask basic questions that can be answered by a simple web search.
11Memes are only allowed on Mondays
Memes are only allowed on Mondays.
Summarised from the community's posted rules on 1 November 2026. Moderators change rules; read the current rules before you post.
What buyers ask for here
Sample requests from public posts, paraphrased and anonymised, found 1 November 2026.
- “Looking for high-quality resources to learn recommendation systems at scale.”
- “Seeking tools or approaches to automate creation of multiple business intelligence dashboards using AI.”
- “Looking for recommended data and AI podcasts for professional learning.”
- “Seeking resources about managing recurring organizational data processes rather than one-off projects.”
- “Needs a comprehensive introduction to a workflow framework within a few days to quickly understand concepts and usage.”
- “Looking for a free interactive tool to learn machine-learning system design.”
Of 100 recent posts we checked, 6% read as someone looking to buy or hire. How we collect this
Who it suits
Good for
- Data science training and education providers
- Analytics and business intelligence software
- MLOps and machine learning tooling
- Developer tools for data scientists and AI engineers
Less useful for
- General consumer products
- Recruiters seeking job candidates
- Broad non-technical business services
How to take part without getting removed
- Be respectful and supportive with other members.
- Keep posts and comments on data science topics.
- Put career transition and career advice discussions in the weekly thread.
- Avoid video links, surveys, and listicle-style resource collections.
- Keep self-promotion limited and follow Reddit's 1:9 guideline.
- Do not use the community for basic web-search questions, homework help, or Stack Overflow style troubleshooting.
What a good reply looks like here
Good replies teach something, answer the question directly, and add practical guidance without turning into a pitch. Help first and stay on data science.
Sample reply: Recommendation systems at scale usually means learning both modeling and serving. A good path is to study candidate generation, ranking, retrieval systems, offline and online evaluation, feature engineering, and system architecture for large datasets. Comparing collaborative filtering, content-based approaches, and modern embedding-based methods also helps you see the tradeoffs between accuracy, latency, and operational complexity. If you share your background and whether you care most about theory, implementation, or production systems, people can point you to more specific resources.
When to skip r/datascience
This community is usually a poor fit for general consumer products, recruiters looking for candidates, or broad non-technical business services. It works best when what you offer is directly relevant to data science, analytics, machine learning, AI engineering, or technical education, and when you can contribute without leaning on frequent promotion.
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