# nborwankar/LearnDataScience

Open Content for self-directed learning in data science

Repository: https://github.com/nborwankar/LearnDataScience
Canonical: https://ross.abutalabs.com/products/learndatascience
Language: Jupyter Notebook
License: NOASSERTION
License Family: other
Last push: 2025-05-24T04:19:20+00:00

## Health v2 (maintenance only)
Score: 43/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 23, release rhythm 35, longevity 100
- inputs: {"age_days": 4787, "days_push": 466, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2980, forks 1632 (observed 2026-08-28T04:07:33.101316+00:00)

## What it is
A collection of open IPython Notebook-based learning materials for self-directed study in data science, covering linear regression, logistic regression, random forests, and k-means clustering. It includes associated datasets and worksheets for iterative code exploration.

## Use cases
- learn data science basics on my own
- jupyter notebook tutorials for linear regression
- understand logistic regression with real data
- learn random forests with worked examples
- practice k-means clustering on datasets
- free open data science course materials

## When to choose
- you want free, notebook-based introductions to core ML techniques
- you learn best by exploring runnable code with real datasets
- you need math-wary-friendly explanations of regression and clustering

## When to avoid
- you need up-to-date coverage of modern deep learning or LLM topics
- you want a maintained course with active community support
- you need production-grade ML tooling rather than learning content

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, data-visualization
- domain: data-science, machine-learning, education, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, self-directed-learning, open-content, regression, clustering, random-forests

## Member repositories
- nborwankar/LearnDataScience (main) score 43

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:33.101316+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 2026-08-30T07:31:30.683043+00:00, confidence not recorded.
  - readme: https://github.com/nborwankar/LearnDataScience (fetched 2026-08-28T04:07:33.101316+00:00, sha ec80392645c7)
- Data as of 2026-08-30T08:39:29.467469+00:00.