Reddit hosts thriving data science communities where practitioners share everything from beginner tutorials to advanced research. The discussions go beyond what you find in online courses, covering real world challenges like messy data, stakeholder communication, and production deployment. Whether you need help with a pandas question or want to discuss career strategy, Reddit has a community for you.
22.8M
Total Subscribers
15
Communities
Promo Tolerance
The main data science community covering career advice, technical discussions, and industry trends. Particularly strong on topics like interview preparation, salary negotiation, and career transitions.
Best Content Type
Career advice, discussions, and articles
Posting Tip
Share candid career experiences and lessons learned from real data science projects, including failures and what you would do differently.
Focused on the data engineering side, covering ETL pipelines, data warehouses, orchestration tools, and infrastructure. A rapidly growing community as data engineering becomes a distinct discipline.
Best Content Type
Architecture discussions and tool comparisons
Posting Tip
Share data pipeline architecture decisions with context about scale, team size, and the trade offs you considered.
Dedicated to statistical methods, theory, and applications. Discussions cover hypothesis testing, Bayesian methods, experimental design, and the proper application of statistical techniques.
Best Content Type
Questions, discussions, and educational content
Posting Tip
Provide mathematically rigorous answers and cite relevant literature when discussing statistical methods.
A supportive community for people learning machine learning, from linear regression to deep learning. Members share study plans, course recommendations, and project ideas.
Best Content Type
Tutorials, resources, and questions
Posting Tip
Share structured learning paths with specific courses, books, and project milestones that worked for you.
The R programming community covering statistical computing, data visualization with ggplot2, and the tidyverse ecosystem. Strong focus on academic and research applications.
Best Content Type
Packages, visualizations, and tutorials
Posting Tip
Share reproducible R code with sample data so others can run and modify your examples immediately.
A community for sharing and finding datasets for analysis, machine learning, and research. Members share open datasets, data collection methods, and dataset quality assessments.
Best Content Type
Dataset links and data requests
Posting Tip
When sharing datasets, include format details, size, collection methodology, and potential use cases to help others evaluate relevance.
One of the largest data related subreddits, focused on compelling data visualizations. Posts should include original content visualizations with clear sources and methodology.
Best Content Type
Original data visualizations
Posting Tip
Create original visualizations using unique datasets and follow the strict OC posting format that requires tool and source disclosure.
Covers business analytics, web analytics, and data driven decision making. Topics include Google Analytics, A/B testing, attribution modeling, and analytics tool selection.
Best Content Type
Tool discussions and methodology advice
Posting Tip
Share analytics frameworks and methodologies that helped drive business decisions, with anonymized results if possible.
Dedicated to Tableau data visualization software, covering dashboard design, calculated fields, and data connection strategies. Members share dashboards and seek design feedback.
Best Content Type
Dashboards, tips, and questions
Posting Tip
Share interactive Tableau Public dashboards with explanations of your design decisions and data transformation approach.
The Power BI community covering DAX formulas, report design, dataflows, and enterprise deployment. Active with daily questions about specific Power BI challenges.
Best Content Type
DAX tips, report designs, and solutions
Posting Tip
Share reusable DAX patterns and report templates that solve common business reporting challenges.
Covers SQL across all database platforms, from basic queries to advanced optimization and database design. A great resource for both beginners learning SQL and experienced developers tuning queries.
Best Content Type
Query help, optimization tips, and tutorials
Posting Tip
Include table schemas and sample data when asking questions, and explain the business context behind your query requirements.
Covers big data technologies including Spark, Hadoop, Kafka, and cloud data platforms. Discussions focus on processing large scale data and distributed computing architectures.
Best Content Type
Architecture discussions and tool reviews
Posting Tip
Share real world big data architecture decisions with context about data volume, velocity, and the specific challenges you faced.
Dedicated to the Python pandas library for data manipulation and analysis. Members help each other with DataFrame operations, data cleaning, and performance optimization.
Best Content Type
Questions, tips, and code snippets
Posting Tip
Always include a minimal reproducible example with sample data when asking pandas questions.
Focuses on business intelligence strategy, tools, and implementation. Topics include BI tool selection, data modeling, and building a data driven culture within organizations.
Best Content Type
Strategy discussions and tool comparisons
Posting Tip
Share BI implementation experiences including stakeholder management challenges and how you built adoption across the organization.
A community for data analysts covering Excel, SQL, Python, and visualization tools. More practical and career focused than the academic statistics communities.
Best Content Type
Portfolio advice and technical help
Posting Tip
Share portfolio project ideas that demonstrate real analytical skills and go beyond common tutorial datasets.
Each subreddit has its own culture around self-promotion. Knowing the tolerance level before posting helps you avoid bans and build genuine credibility.
These communities welcome product mentions and project sharing as long as you follow subreddit rules. You can include links to your product in posts and comments, but genuine value should still come first.
Self-promotion is allowed in specific threads or under certain conditions (like designated weekly threads). Read the sidebar rules carefully. Build some post history before sharing your own products or content.
These subreddits strictly prohibit self-promotion. Focus on providing value through comments and educational posts. Build karma and credibility first. Mention your product only when directly asked for recommendations.
This list covers the top communities, but there are hundreds more niche subreddits where your target audience hangs out. MediaFast's subreddit finder analyzes your product and matches you with the most relevant communities, including hidden gems most marketers miss.
Common questions about finding and using the best data science communities on Reddit.
r/datascience is the most active community for data science career discussions, covering interview preparation, salary negotiation, and career transitions. The weekly career thread is particularly useful for getting specific advice about job offers and career moves.
r/dataisbeautiful is the premier subreddit for data visualizations, with over 20 million members. However, it has strict rules requiring original content with sources. For tool specific feedback, r/tableau and r/PowerBI are excellent for getting design input from practitioners.
Yes, r/datascience focuses on analysis, modeling, and deriving insights from data. r/dataengineering focuses on building the infrastructure that makes data available, including pipelines, warehouses, and orchestration. Both are important, and many professionals follow both communities.
r/datasets is specifically designed for sharing and finding datasets for analysis and machine learning. Members regularly post interesting datasets with descriptions and potential use cases. You can also make requests for specific types of data, and the community is usually helpful.
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