🚀 500+ curated resources for Data Analysis & Data Science: Python, SQL, Statistics, ML, AI, Visualization, Cheatsheets, Roadmaps, Interview Prep. For beginners and experts.
1.6k
Stars
234
Forks
10
Open issues
11
Contributors
AI Analysis
Awesome Data Analysis is a comprehensive curated collection of 500+ resources for learning and practicing data analysis, data science, and related fields. It aggregates tools, libraries, roadmaps, cheatsheets, and tutorials across Python, SQL, statistics, ML, and visualization. This resource is best suited for beginners seeking structured learning paths and practitioners looking for tool recommendations; it is not a framework or library for building applications.
Inferred from signals mentioned in the README (tests, CI, type safety) — not a review of the actual code.
AI's overall editorial judgment — not an average of the bars above, can weigh other factors too.
Curated resource list for data analysis and science learners; 1,491 stars, actively maintained, moderate growth trajectory.
Awesome Data Analysis is a curated collection repository aggregating 500+ resources (tools, libraries, roadmaps, cheatsheets, interview prep) across Python, SQL, Statistics, ML, visualization, and related domains. It targets learners and practitioners seeking structured entry points into data analysis workflows. The project is community-maintained, license-free (CC0-1.0), and serves as a discovery and reference tool rather than a software framework or platform.
Created July 2025 by PavelGrigoryevDS, the repository entered a maturing market of data science awesome-lists. It emerged during a period when similar curated lists (academic/awesome-datascience with 29k+ stars, igorbarinov/awesome-data-engineering with 8.8k stars) were already established. The project positioned itself as a comprehensive, multi-domain resource aggregator.
Gained 1,491 stars and 223 forks from inception (~11 months) to current date. Measured growth: 42 stars in the 7 days prior to 2026-06-29, indicating sustained but modest ongoing interest. Last push on 2026-06-02 suggests recent, active curation. Growth appears driven by organic discovery through search and cross-listing rather than viral adoption, consistent with reference-material projects.
Adoption not verified. No documentation of institutional use, corporate adoption, or integration into training programs. Star count (1,491) positions it as a mid-tier reference resource, but stars alone do not confirm deployment in real-world contexts. Web interface is available but usage metrics are not public. Likely used by individual learners rather than organizational training infrastructure.
Not a software project with executable code — this is a curated markdown-based resource index with a web interface (GitHub Pages). README lists structured categories with resource links but contains no implementation details. Curation quality cannot be assessed from metadata alone.
Not applicable; this is a reference list, not a testable software product.
Last push 2026-06-02 is 27 days prior to analysis date, indicating recent activity. README includes contribution guidelines, discussion forums, and PR welcome badges — evidence of open gatekeeping. Project appears actively maintained rather than stagnant, though pace is measured and appropriate for a reference list.
ADOPT IF: you are building a learning journey and need a single, curated entry point across multiple data domains (Python, SQL, stats, ML, visualization, BI); you prefer open-source discovery over paywalled platforms; your organization seeks a reference site for onboarding. AVOID IF: you need hands-on tutorials, executable code examples, or formal curriculum structure (use OSSU, DataExpert handbook, or Microsoft's courses instead); you rely on tools, not learning paths. MONITOR IF: you are evaluating whether to contribute; the project is young, growing modestly, and receptive to PRs, but does not dominate its category.
Independent dimensions
Mainstream potential
3/10
Technical importance
2/10
Adoption evidence
2/10
- Reliance on single maintainer (Pavel Grigoryev) with no visible secondary stewardship — contributor turnover risk typical of personal projects.
- Link rot risk: 500+ external resource links require ongoing verification; README contains no automation or CI/CD for validating link health.
- Category overlap and curation depth not independently audited — difficult to verify if all listed resources are current, high-quality, or representative.
- Highly competitive market; similar repositories have 5–20× more stars, reducing visibility and discoverability in algorithmic recommendations.
- No measurable adoption or impact metrics provided; success defined by star count rather than verified learner outcomes or institutional adoption.
Project likely sustains modest, stable growth as a secondary reference resource. Unlikely to displace established competitors (academic/awesome-datascience, OSSU) but may find niche adoption among learners seeking more recent, comprehensive multi-domain curation. Ongoing maintenance is plausible if maintainer remains engaged; eventual archival or stagnation risk typical of community-driven reference projects without institutional backing.
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Languages
No language breakdown available.
Information
- License
- CC0-1.0
- Last updated
- 1mo ago
- Created
- 12mo ago
- Analyzed with
- anthropic/claude-haiku-4-5
Stars over time
Contributors over time
Top 100 contributors only — repos with more will plateau at 100.
Open issues
Top contributors
Recent releases
No releases published yet.
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Roughly 20× larger star count; established longer; broader historical adoption. This project competes on comprehensiveness and up-to-date curation rather than novelty.
Narrower scope (data engineering only); this project spans analysis, science, and engineering, offering wider domain coverage at potential cost of depth.
Significantly larger; includes executable notebooks rather than pure curation. Targets hands-on learning; this project is reference-oriented.
Focused on big data and distributed systems; this project includes big data but integrates broader data analysis and statistics domains.
OSSU is structured as a self-study degree path; this project is a resource index, serving different use cases (discovery vs. structured progression).
