# donnemartin/data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

Repository: https://github.com/donnemartin/data-science-ipython-notebooks
Canonical: https://ross.abutalabs.com/products/data-science-ipython-notebooks
Language: Python
License: NOASSERTION
License Family: other
Topics: python, machine-learning, deep-learning, data-science, big-data, aws, tensorflow, theano, caffe, scikit-learn, kaggle, spark, mapreduce, hadoop, matplotlib, pandas, numpy, scipy, keras
Last push: 2024-03-20T13:52:34+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": 4240, "days_push": 896, "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 29326, forks 8027 (observed 2026-08-28T04:11:53.269115+00:00)

## What it is
A large curated collection of IPython/Jupyter notebooks covering data science topics including deep learning (TensorFlow, Keras, Theano, Caffe), scikit-learn, pandas, NumPy, matplotlib, Spark/Hadoop big data, Kaggle analyses, and AWS. It serves as a hands-on tutorial resource rather than a software library.

## Use cases
- learn deep learning with tensorflow and keras through notebooks
- jupyter notebook tutorials for pandas and numpy
- learn scikit-learn machine learning by example
- spark and hadoop big data notebook examples
- kaggle competition starter notebooks
- learn matplotlib data visualization in python
- aws and big data tutorials for data scientists

## When to choose
- you want hands-on, runnable notebook tutorials across the Python data science stack
- you are learning deep learning, pandas, scikit-learn, or Spark by example
- you need curated links to quality tutorials in one place

## When to avoid
- you need a production-ready library or tool rather than educational material
- you need up-to-date coverage of the latest framework versions, as some notebooks reference older APIs
- you want a structured course with assessments instead of reference notebooks

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, data-science, data-visualization
- domain: data-science, machine-learning, deep-learning, big-data, tutorials
- platform: python, cross-platform
- tags: ipython-notebooks, jupyter, tensorflow, keras, scikit-learn, pandas, numpy, spark, kaggle, aws, educational-notebooks

## Member repositories
- donnemartin/data-science-ipython-notebooks (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:53.269115+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 2026-08-29T16:53:03.666104+00:00, confidence not recorded.
  - readme: https://github.com/donnemartin/data-science-ipython-notebooks (fetched 2026-08-28T04:11:53.269115+00:00, sha a832e25a84ae)
- Data as of 2026-08-30T08:39:29.467469+00:00.