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Scientific Computing in Python

A collection of Python projects in numerical methods, optimization, and data analysis, organized by topic.

Folder Contents
ordinary-differential-equations/ Numerical solution of ODEs: initial-value problems, discretization schemes, and stability experiments
numerical-linear-algebra/ Direct and iterative methods, conditioning and round-off error, matrix factorizations
data-assimilation/ Data-assimilation techniques combining model forecasts with observations
data-driven-optimization/ Data-driven optimization for machine learning: formulations, algorithms, and experiments
big-data-analysis/ Large-scale data processing and analysis
financial-mathematics/ Computational methods in financial mathematics

Note: data-driven-optimization/Data Assimilation_extended.ipynb is filed by its content (data-driven optimization) rather than its filename.

Usage

pip install numpy scipy pandas matplotlib scikit-learn jupyter

Open any notebook with jupyter notebook, or run the .py scripts directly.

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A collection of Python projects in numerical methods, optimization, and data analysis, organized by topic.

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