academic/awesome-datascience
:memo: An awesome Data Science repository to learn and apply for real world problems.
From the publisher
README & documentation
Source previewRead the project’s overview, installation instructions and usage examples. The original README is the source of truth.
[](https://github.com/sindresorhus/awesome) Contributions are welcome - see CONTRIBUTING.md. An open-source Data Science repository to learn and apply concepts toward solving real- world problems. This is a shortcut path to start studying Data Science. Just follow the steps to answer the questions, "What is Data Science, and what should I study to learn Data Science?" [](https://creavit.studio/?utmsource=github&utmmedium=sponsorship&utmcampaign=creavitfoundingalpha&utmcontent=crvtbanner) [](https://graphyn.ai/?utmsource=github&utmmedium=sponsorship&utmcampaign=graphynfoundingalpha&utmcontent=awesomedatasciencebanner) Become a sponsor! github@academic.io ^ back to top ^ Data Science is one of the hottest topics on the Computer and Internet farmland nowadays. People have gathered data from applications and systems until today and now is the time to analyze them. The next steps are producing suggestions from the data and creating predictions about the future. Here you can find the biggest question for Data Science and hundreds of answers from experts. ^ back to top ^ While not strictly necessary, having a programming language is a crucial skill to be effective as a…
Read the full README ↗ · Preview checked 2026-11-01T08:52:04.884Z
Inside the original README — Document outline
- AWESOME DATA SCIENCE
- $ academic
- Sponsors
- Table of Contents
- What is Data Science?
- Where do I Start?
- Beginner Roadmap
- Agents
- Frameworks
- Tools
- Research & Knowledge Retrieval
- Workflow
Headings are captured from the source. Links open the publisher’s document, not a locally hosted copy.
Links from the README
References supplied by the publisher, not independently verified endorsements. Check the destination before downloading files or entering credentials.
- Training Resources
- Tutorials
- Datasets
- ai-evaluation
- Spraay x402 Gateway
- TutorialSearch
- Your Guide to Latent Dirichlet Allocation
- Tutorials of source code from the book Genetic Algorithms with Python by Clinton Sheppard
- Tutorials to get started on signal processing for machine learning
- Python for Data Science: A Beginner’s Guide
- Best CV/Resume for Data Science Freshers
- AgentFEM Simulation-to-Surrogate Mini-Lab
Documentation belongs to its respective authors. Reported project/model license: MIT. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.
What this repository does
:memo: An awesome Data Science repository to learn and apply for real world problems.
Repository facts
- Owner
- academic
- Primary language
- Not reported
- Stars
- 30,262
- Forks
- 6,701
- Open issues + pull requests
- 11
- License
- MIT
- Archived
- No
- Default branch
- live
- Created
- 2014-07-05T07:07:43.000Z
- Last push
- 2026-10-31T21:12:43.000Z
Topics and intended use
Owner-supplied topics: analytics, awesome-list, data-mining, data-science, data-scientists, data-visualization, deep-learning, hacktoberfest, machine-learning, science
Review the README for scope, installation, examples and limitations. We do not run repository code or certify it.
Evaluate before installing
Review licensing and dependencies, inspect recent commits and unresolved issues, and test in an isolated environment before production use. Stars and forks alone cannot answer these questions.
Source and freshness
Source: GitHub. Metadata observed 2026-11-01T12:19:42.808Z. Daily imports are snapshots, not real-time monitoring.
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