data-science-ipython-notebooks

Why we included this project

These Jupyter notebooks work through the Python data science stack in a sensible order, starting with NumPy, pandas, and matplotlib, then moving into scikit-learn, deep learning with TensorFlow and Keras, and big-data tooling like Spark and Hadoop. It is a learning resource rather than a deployable application, so its value is in the worked examples: each notebook shows real code you can read before writing your own. The notebooks are grouped by topic, which makes it easy to jump straight to the library you are currently learning, and the Kaggle and business-analysis sections show how the pieces fit together on actual problems. New data scientists can use it to get up to speed on the fundamentals, and experienced practitioners will find it a quick way to recall syntax across many tools without digging through separate docs.