# dataprofessor/code

Compilation of R and Python programming codes on the Data Professor YouTube channel.

Repository: https://github.com/dataprofessor/code
Canonical: https://ross.abutalabs.com/products/dataprofessor-code
Homepage: http://youtube.com/dataprofessor
Language: Jupyter Notebook
License Family: other
Topics: shiny, exploratory-data-analysis, python, streamlit, r, datascience, data-science, machinelearning, machine-learning, python-data-science, data-science-python, dataprofessor, data-professor, scikit-learn, scikit-learn-python, pandas
Last push: 2026-01-10T08:25:58+00:00

## Health v2 (maintenance only)
Score: 60/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 61, release rhythm 35, longevity 100
- inputs: {"age_days": 2467, "days_push": 235, "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 1049, forks 1450 (observed 2026-08-28T04:03:22.683713+00:00)

## What it is
A compilation of R and Python code notebooks accompanying the Data Professor YouTube channel's data science tutorials. It covers exploratory data analysis, classification models, and web app building with Shiny and Streamlit.

## Use cases
- learn data science with python tutorials
- example code for exploratory data analysis
- build classification models with scikit-learn
- learn to build streamlit web apps
- learn R shiny app development
- follow along with data professor youtube videos

## When to choose
- you want runnable example code to accompany beginner data science video tutorials
- you are learning pandas, scikit-learn, Streamlit, or Shiny through hands-on examples

## When to avoid
- you need production-ready, maintained software with a license
- you want a coherent library or framework rather than loose tutorial scripts

## Facets
- artifact type: learning-resource
- maturity: active
- function: data-science, machine-learning, nlp
- domain: data-science, machine-learning, education, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, streamlit, shiny, r, pandas, scikit-learn, youtube-tutorials, exploratory-data-analysis

## Member repositories
- dataprofessor/code (main) score 60

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:22.683713+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 2026-08-30T07:00:28.780227+00:00, confidence not recorded.
  - readme: https://github.com/dataprofessor/code (fetched 2026-08-28T04:03:22.683713+00:00, sha d6fe9804d4ab)
  - homepage: http://youtube.com/dataprofessor (fetched 2026-08-29T13:02:09.749000+00:00, sha 3b43d3b55370)
- Data as of 2026-08-30T08:39:29.467469+00:00.