academic/awesome-datascience
Rank not captured·Top 100·All-time stars·Cached ranking snapshot·
:memo: An awesome Data Science repository to learn and apply for real world problems.
First read
Visible and active, but adoption proof is still thinner
academic/awesome-datascience has enough public attention and recent movement to stay on the shortlist, but package usage is still partial, so the next step should be source and ecosystem validation rather than a quick yes.
Visible
29.8K public stars in the current GitStar snapshot.
Recognizable in the ecosystem
Active
Last commit Aug 10, 2026.
Fresh activity
Adopted
Treat stars as discovery context until a linked package appears.
No linked package mapping
Confidence
One or more key signals are partial, so GitStar keeps the interpretation conservative.
Partial snapshot
Snapshot facts
- 29.8K stars
- 6.6K forks
- Last commit Aug 10, 2026
- Package usage not mapped yet
Compare lens
ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code and binhnguyennus/awesome-scalability are the closest comparison targets GitStar found. A side-by-side comparison usually tells you more than a single raw rank.
Signal trail
Trajectory
Read the recent motion first. This block is for deciding whether the repository still looks alive, compounding, or flattening before you trust stars alone.
Daily momentum
Recent momentum is not captured
Weekly momentum
Recent momentum is not captured
Monthly momentum
Recent momentum is not captured
Last commit
Aug 10, 2026
Package reality
No linked npm or PyPI package is mapped for this repository yet, so the page leans more heavily on GitHub-visible popularity and should be read more conservatively.
No linked package signal is expected for this project type, so the read leans more heavily on repository-level public signals.
Hype vs Reality
Insufficient Package Data
Package mapping
No linked package yet
Cross-links
Standalone repo read
Validation note
GitStar can summarize public signals for academic/awesome-datascience, but the GitHub repository is still the primary place to confirm release cadence, issue activity, and maintainer intent.
analyticsawesome-listdata-miningdata-sciencedata-scientistsdata-visualizationdeep-learninghacktoberfestmachine-learningscience
🎓The Professor🦕Living Fossil
🛡️ Editorial Context
GitStar surfaces public popularity and package signals. These rankings are not endorsements, security reviews, or investment advice.
Why this rank
This repository stands out because it combines fresh update and stable visibility.
Fresh updateStable visibility
📈 Momentum & Adoption Signals
Approximate star trajectory
Reconstructed from current stars and cached daily/weekly/monthly deltas.
Now: 29.8K
Daily momentum
No signal
Weekly momentum
No signal
Monthly momentum
No signal
Signal mix (log-scaled for readability)
Current stars29.8K
🧭 Hype vs Reality
Insufficient Package DataGitStar can see repository momentum, but it does not have a reliable linked package signal yet.
Treat stars and recent movement as discovery context only until npm or PyPI usage is available.
Status
Insufficient Package Data
Package footprint
No linked npm or PyPI usage signal
Method note
This label appears when GitStar cannot find strong enough package telemetry to compare attention against adoption. Compare it against other Unknown repos before treating stars as a moat.
Related alternative
ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
Shares the learning category footprint with academic/awesome-datascience, so the comparison is closer to a same-problem decision than a same-language coincidence.
Related alternative
binhnguyennus/awesome-scalability
Shares the learning category footprint with academic/awesome-datascience, so the comparison is closer to a same-problem decision than a same-language coincidence.
Related alternative
tangyudi/Ai-Learn
Shares the learning category footprint with academic/awesome-datascience, so the comparison is closer to a same-problem decision than a same-language coincidence.
Related alternative
streamlit/streamlit
Shares the learning category footprint with academic/awesome-datascience, so the comparison is closer to a same-problem decision than a same-language coincidence.
Research links
Cross-links
Compare against related repos
GitStar picked 500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code + awesome-scalability as the closest next comparison from the related repository set.
Repo utility
Embed Badge
Preview:
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<a href="https://gitstar.space/repo/academic/awesome-datascience"><img src="https://gitstar.space/api/badge/academic/awesome-datascience" alt="GitStar"></a>Styles:
Wider nearby ecosystem
Unknown
36.3K starsashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
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Unknown
73.3K starsbinhnguyennus/awesome-scalability
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Unknown
13.3K starstangyudi/Ai-Learn
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Python
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Next step after the validation read
Move into a compare preset, organization view, or the heuristic notes once this first fold tells you whether the repo looks visible, active, adopted, and credible enough to keep researching.
Learn and methodology
Keep trust-building context reachable, but behind the first data read instead of ahead of it.
About This Page
This page provides a quick overview of academic/awesome-datascience based on GitStar's cached data. The signal chart reconstructs approximate checkpoints from current stars plus cached daily, weekly, and monthly star deltas, so it is best read as directional context rather than as a precise historical audit log.
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