Comparison
Data-Science-EBooks vs awesome-LLM-resources
Verdict
Pick Data-Science-EBooks if data-Science-EBooks provides a broad range of eBook resources covering foundational to advanced aspects in Data Science and AI; pick awesome-LLM-resources if awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a.
Markdown twin · Data-Science-EBooks alternatives · awesome-LLM-resources alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | Data-Science-EBooks | awesome-LLM-resources |
|---|---|---|
| Maintenance | Slowing (242d since push) As of 3w · github_public_v1 | Very active (2d since push) As of 1w · github_public_v1 |
| Provenance | Not a fork · Personal account As of 3w · github_public_v1 | Not a fork · Personal account As of 1w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- Data-Science-EBooks
- Repository of high-quality eBooks on Data Science, Machine Learning, AI
- awesome-LLM-resources
- Summary of the world's best LLM resources.
Stars
- Data-Science-EBooks
- 949
- awesome-LLM-resources
- 8.8k
Forks
- Data-Science-EBooks
- 300
- awesome-LLM-resources
- 950
Open issues
- Data-Science-EBooks
- 0
- awesome-LLM-resources
- 23
Language
- Data-Science-EBooks
- -
- awesome-LLM-resources
- -
Adopt for
- Data-Science-EBooks
- Data-Science-EBooks provides a broad range of eBook resources covering foundational to advanced aspects in Data Science and AI.
- awesome-LLM-resources
- awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a
Persona
- Data-Science-EBooks
- -
- awesome-LLM-resources
- -
Runtime
- Data-Science-EBooks
- -
- awesome-LLM-resources
- -
License
- Data-Science-EBooks
- -
- awesome-LLM-resources
- Apache-2.0
Last pushed
- Data-Science-EBooks
- Nov 30, 2025
- awesome-LLM-resources
- Aug 14, 2026
Categories
- Data-Science-EBooks
- Developer Tools
- awesome-LLM-resources
- AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training
Trust and health
Maintenance
- Data-Science-EBooks
- Slowing (36%)
- awesome-LLM-resources
- Very active (96%)
Days since push
- Data-Science-EBooks
- 242d
- awesome-LLM-resources
- 2d
Open issues (now)
- Data-Science-EBooks
- 0
- awesome-LLM-resources
- 23
Stars delta
- Data-Science-EBooks
- Unknown
- awesome-LLM-resources
- +142 (30d)
Open issues delta
- Data-Science-EBooks
- Unknown
- awesome-LLM-resources
- -13 (30d)
Full report
- Data-Science-EBooks
- Trust report
- awesome-LLM-resources
- Trust report
Choose Data-Science-EBooks if…
- Tags unique to Data-Science-EBooks: ai, computer-vision, data-analysis, data-mining.
- Use when seeking self-paced learningmaterials that covera vast arrayof subjectsfrombasics tomoredetailed aspects of data science, machinelearning,AI.
- Leaner open-issue backlog (0).
When NOT to use Data-Science-EBooks
- Avoid ifyour needsalignmore closelywith interactivecontentorhands-on courseswhich this repositorydoesnotprovide.
- Do not use if you are looking for materialswrittenin a language other than English.
Choose awesome-LLM-resources if…
- Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models.
- Also covers AI Agents, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training.
- - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies.
When NOT to use awesome-LLM-resources
- - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage.
- - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (aniketpotabatti/Data-Science-EBooks) · observed Jul 31, 2026
- GitHub forks (aniketpotabatti/Data-Science-EBooks) · observed Jul 31, 2026
- Last push (aniketpotabatti/Data-Science-EBooks) · observed Nov 30, 2025
- License file (unknown) · observed Jul 31, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (WangRongsheng/awesome-LLM-resources) · observed Aug 17, 2026
- GitHub forks (WangRongsheng/awesome-LLM-resources) · observed Aug 17, 2026
- Last push (WangRongsheng/awesome-LLM-resources) · observed Aug 14, 2026
- License file (Apache-2.0) · observed Aug 17, 2026
- Decision facts (enrichment) · observed Jul 10, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: Data-Science-EBooks 949 · awesome-LLM-resources 8.8k (synced Jul 31, 2026).
Common questions
- What is the difference between Data-Science-EBooks and awesome-LLM-resources?
- Data-Science-EBooks: Repository of high-quality eBooks on Data Science, Machine Learning, AI. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories.
- When should I choose Data-Science-EBooks over awesome-LLM-resources?
- Choose Data-Science-EBooks over awesome-LLM-resources when Tags unique to Data-Science-EBooks: ai, computer-vision, data-analysis, data-mining; Use when seeking self-paced learningmaterials that covera vast arrayof subjectsfrombasics tomoredetailed aspects of data science, machinelearning,AI; Leaner open-issue backlog (0).
- When should I choose awesome-LLM-resources over Data-Science-EBooks?
- Choose awesome-LLM-resources over Data-Science-EBooks when Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models; Also covers AI Agents, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training; - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies.
- When should I avoid Data-Science-EBooks?
- Avoid ifyour needsalignmore closelywith interactivecontentorhands-on courseswhich this repositorydoesnotprovide. Do not use if you are looking for materialswrittenin a language other than English.
- When should I avoid awesome-LLM-resources?
- - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content.
- Is Data-Science-EBooks or awesome-LLM-resources more popular on GitHub?
- awesome-LLM-resources has more GitHub stars (8,845 vs 949). Stars measure visibility, not whether either tool fits your constraints.
- Are Data-Science-EBooks and awesome-LLM-resources open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to Data-Science-EBooks or awesome-LLM-resources?
- GraphCanon lists graph-backed alternatives at Data-Science-EBooks alternatives and awesome-LLM-resources alternatives (Data-Science-EBooks markdown twin, awesome-LLM-resources markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, Data-Science-EBooks or awesome-LLM-resources?
- Data-Science-EBooks: Slowing. awesome-LLM-resources: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for Data-Science-EBooks and awesome-LLM-resources?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Data-Science-EBooks trust report; awesome-LLM-resources trust report.