edyoda/data-science-complete-tutorial resource

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1834 stars · 771 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

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

learning-resource · maturity maintenance

machine-learning data-science data-visualization etl machine-learning data-science tutorials education python cross-platform jupyter-notebooks scikit-learn numpy pandas instructor-led course-material beginner-friendly

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