# sreeharierk/datascience

This repository is a compilation of free resources for learning Data Science.

Repository: https://github.com/sreeharierk/datascience
Canonical: https://ross.abutalabs.com/products/datascience
Homepage: https://twitter.com/sreeharierk
License: GPL-3.0
License Family: copyleft
Topics: data-science, machine-learning, machine-learning-algorithms, deeplearning, artificial-intelligence, computer-vision, natural-language-processing, neural-networks, datascienceproject
Last push: 2026-02-01T18:20:19+00:00

## Health v2 (maintenance only)
Score: 62/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 65, release rhythm 35, longevity 100
- inputs: {"age_days": 2044, "days_push": 213, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5153, forks 521 (observed 2026-08-28T04:09:11.389147+00:00)

## What it is
A curated compilation of free resources for learning data science, organized as a roadmap covering fundamentals like linear algebra, data structures, databases, machine learning, deep learning, NLP, and computer vision. It serves as a structured study guide pointing learners to external learning materials rather than containing software itself.

## Use cases
- find free resources to learn data science
- follow a structured roadmap to become a data scientist
- learn machine learning fundamentals from scratch
- study linear algebra and statistics for data science
- find learning materials for deep learning and neural networks
- plan a self-taught data science curriculum

## When to choose
- you want a curated, structured path through free data science learning materials
- you are self-teaching data science and need guidance on topic order
- you want links to resources across ML, deep learning, NLP, and computer vision in one place

## When to avoid
- you need runnable code, libraries, or tools rather than learning links
- you want an interactive course with exercises and grading
- you need up-to-date content, as parts of the roadmap date from 2021

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, machine-learning, deep-learning, nlp, computer-vision
- domain: data-science, machine-learning, artificial-intelligence, tutorials, education
- platform: cross-platform
- tags: roadmap, curated-resources, free-resources, study-guide, awesome-list

## Member repositories
- sreeharierk/datascience (main) score 62

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:11.389147+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:01:56.230819+00:00, confidence not recorded.
  - readme: https://github.com/sreeharierk/datascience (fetched 2026-08-28T04:09:11.389147+00:00, sha 26d0bf399700)
- Data as of 2026-08-30T08:39:29.467469+00:00.