donnemartin/data-science-ipython-notebooks

Project overview

A collection of instructional, executable IPython notebooks covering data science, deep learning, big data, and cloud concepts. It provides curated examples using TensorFlow, scikit-learn, Spark, pandas, and AWS for interactive learning.

Project type

AI Coding · Data Processing

Use cases

Data Analysis · Learning & Education

Deployment

Refer to project documentation

License

License pending

Best for

• Developers, educators, and researchers seeking an open, executable tutorial resource for data science, deep learning, and big data education.

Key capabilities

• Notebooks demonstrating deep learning functionality, including TensorFlow and Keras tutorials.
• Notebooks demonstrating scikit-learn, statistical inference, pandas, matplotlib, numpy, Spark, and MapReduce.
• Notebooks and tutorials covering Amazon Web Services and command-line tools.

Limitations and risks

• The tutorial requires a local Jupyter/IPython notebook installation, and hardware requirements such as GPU availability are not documented.

Getting started

• Users must install IPython/Jupyter notebooks locally, which presents medium setup difficulty, before running the provided notebooks.

Evidence and sources

• GitHub project description: Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy,…
• README: * [scikit-learn](#scikit-learn) * [statistical-inference-scipy](#statistical-inference-scipy) * [pandas](#pandas) * [matplotlib](#matplotlib) * [numpy](#numpy) * [python-data](#py…
• README: IPython Notebook(s) demonstrating deep learning functionality.
• README: Learn basic operations in TensorFlow, a library for various kinds of perceptual and language understanding tasks from Google.
• README: * [notebook-installation](#notebook-installation)

Reviewed at: 2026-07-15T01:23:49Z