Data Science

Python contains several libraries for data science application

List of data science libraries

NumPy: NumPy is a Python library used for working with arrays and numerical operations. It provides powerful tools for mathematical operations, data manipulation, and linear algebra.
SciPy: SciPy is a library built on top of NumPy that provides additional tools for scientific and technical computing. It includes modules for optimization, integration, interpolation, signal processing, and more.

Matplotlib: Matplotlib is a Python library used for creating 2D and 3D plots and visualizations. It offers a wide range of customization options and supports various plot types, including line, scatter, bar, and histogram.
Plotly: Plotly is a Python library used for creating interactive and dynamic visualizations, including charts, graphs, and dashboards. It offers a variety of customization options and can be used for data exploration and analysis.
Seaborn: Seaborn is a Python library based on Matplotlib that provides additional tools for statistical data visualization. It offers a variety of plot types, including heatmaps, scatter plots, and regression plots, and includes built-in color palettes and themes.

Scikit-learn: Scikit-learn is a Python library used for machine learning and data mining tasks, such as classification, regression, clustering, and dimensionality reduction. It provides a range of algorithms and tools for model selection, data preprocessing, and evaluation.
PyTorch: PyTorch is a Python library used for developing and training machine learning models, particularly deep neural networks. It provides a flexible and efficient platform for building and deploying models, with features such as autograd, dynamic computational graphs, and GPU acceleration.