πŸ› οΈ
Awesome Datascience tool by academic
⭐ 50.6
πŸ› οΈ
Tool

Awesome Datascience

by academic academic/awesome-datascience
Free2AITools Nexus Index
50.6
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 62
P: Popularity 75
R: Recency 91
Q: Quality 70
Tech Context
Vital Performance —
Source β†’
- Lang
Open Source 29.0K Stars
Alpha Reliability
Tool Information Summary
Entity Passport
Registry ID academic/awesome-datascience
License MIT
Provider github
πŸ“œ

Cite this tool

Academic & Research Attribution

BibTeX 
@misc{academic_awesome_datascience,
  author = {academic},
  title = {Awesome Datascience Tool},
  year = {2026},
  howpublished = {\url{https://github.com/academic/awesome-datascience}},
  note = {Accessed via Free2AITools.}
}
APA Style
academic. (2026). Awesome Datascience [Tool]. Free2AITools. https://github.com/academic/awesome-datascience

πŸ”¬Technical Deep Dive

Full Specifications [+]

Quick Commands

πŸ™ GitHub Clone
git clone https://github.com/academic/awesome-datascience
πŸ™ Git Clone
git clone https://github.com/academic/awesome-datascience

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 62
Popularity (P) 75
Recency (R) 91
Quality (Q) 70

πŸ’¬ Index Insight

FNI V2.0 for Awesome Datascience: Authority (A:62), Popularity (P:75), Recency (R:91), Quality (Q:70). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data

πŸ“‹ Specs

Language
β€”
License
MIT
Version
β€”
Explore Source Node

Technical Documentation

Special thanks to Sponsors:

Requestly sponsorship

[Requestly - Free & Open-Source alternative to Postman](https://requestly.com/awesomedatascience)

All-in-one platform to Test, Mock and Intercept APIs

AWESOME DATA SCIENCE

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?"


Sponsors

Sponsor Pitch
--- Be the first to sponsor! github@academic.io

Table of Contents

Social Proof

GitHub Repository
29.0KStars
6.5KForks
Repo Issues
πŸ™ Data Source: GitHub β†—
πŸ”„ Updated daily

Source summary: Based on GitHub metadata. Not a recommendation.

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Tool Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

id
gh-model--academic--awesome-datascience
slug
academic--awesome-datascience
source
github
author
academic
license
MIT
tags
data-science, machine-learning, data-visualization, science, data-mining, awesome-list, deep-learning, analytics, data-scientists, hacktoberfest

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag
other

πŸ“Š Engagement & Metrics

downloads
0
stars
29,049
forks
6,483
github stars
29,049

Data indexed from public sources. Updated daily.