# DrSkippy/Data-Science-45min-Intros

Ipython notebook presentations for getting starting with basic programming, statistics and machine learning techniques

Repository: https://github.com/DrSkippy/Data-Science-45min-Intros
Canonical: https://ross.abutalabs.com/products/data-science-45min-intros
Language: Jupyter Notebook
License: Unlicense
License Family: permissive
Last push: 2019-10-02T19:33:51+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 4731, "days_push": 2527, "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 1585, forks 467 (observed 2026-08-28T04:05:07.513693+00:00)

## What it is
A collection of IPython notebook presentations from a data science team covering Python programming, statistics, and machine learning fundamentals in 45-minute lesson formats. It is designed for self-study or team-based learning sessions.

## Use cases
- learn data science basics with jupyter notebooks
- introduction to pandas and scikit-learn tutorials
- teach my team statistics and machine learning in short sessions
- learn python programming concepts like oop and generators
- understand a/b testing and causal inference
- intro to k-means clustering and logistic regression
- self-study materials for data munging and analysis

## When to choose
- you want hands-on notebook-based introductions to data science topics
- you are running weekly team learning sessions and need ready-made lessons
- you are a beginner wanting short intros to Python, statistics, and ML

## When to avoid
- you need comprehensive, up-to-date course material (content dates to ~2019)
- you want production code or libraries rather than educational notebooks
- you need structured curriculum with assessments or certification

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: data-science, machine-learning, nlp, testing, developer-tools
- domain: data-science, machine-learning, education, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, tutorials, statistics, python, team-learning, ipython

## Member repositories
- DrSkippy/Data-Science-45min-Intros (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:07.513693+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 2026-08-30T03:55:30.841077+00:00, confidence not recorded.
  - readme: https://github.com/DrSkippy/Data-Science-45min-Intros (fetched 2026-08-28T04:05:07.513693+00:00, sha fa742431e814)
- Data as of 2026-08-30T08:39:29.467469+00:00.