# edyoda/data-science-complete-tutorial

For extensive instructor led learning

Repository: https://github.com/edyoda/data-science-complete-tutorial
Canonical: https://ross.abutalabs.com/products/data-science-complete-tutorial
Homepage: https://www.edyoda.com/micro-degree/generative-ai-and-ai-agent-career-track-micro-degree
Language: Jupyter Notebook
License Family: other
Topics: machine-learning, scikit-learn, linear-regression, nearest-neighbors, feature-selection, decision-trees, numpy, pandas, pipeline
Last push: 2022-10-31T09:44:29+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2909, "days_push": 1402, "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 1834, forks 771 (observed 2026-08-28T04:05:42.486509+00:00)

## What it is
A collection of Jupyter Notebook lessons and case studies accompanying an instructor-led data science program, covering NumPy, Pandas, plotting, and classic machine learning with scikit-learn. It serves as a structured codebook for learners following the edYoda Data Scientist Program.

## Use cases
- learn machine learning from scratch with python notebooks
- study scikit-learn algorithms like decision trees and SVMs with examples
- practice data wrangling with pandas and numpy
- follow a structured data science course curriculum
- find worked case studies for classic ML problems like regression and cancer prediction
- learn model selection, feature selection, and clustering techniques

## When to choose
- you want free, notebook-based tutorials covering the full classic ML workflow
- you are a beginner learning NumPy, Pandas, and scikit-learn together
- you prefer example-driven learning with case studies

## When to avoid
- you need production-ready machine learning code or a library
- you want up-to-date content on deep learning or LLMs
- you need actively maintained material with a license

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, data-visualization, etl
- domain: machine-learning, data-science, tutorials, education
- platform: python, cross-platform
- tags: jupyter-notebooks, scikit-learn, numpy, pandas, instructor-led, course-material, beginner-friendly

## Member repositories
- edyoda/data-science-complete-tutorial (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:42.486509+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 2026-08-30T03:18:35.740152+00:00, confidence not recorded.
  - readme: https://github.com/edyoda/data-science-complete-tutorial (fetched 2026-08-28T04:05:42.486509+00:00, sha 79e993368c63)
- Data as of 2026-08-30T08:39:29.467469+00:00.