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Hands-on guides for Python data science libraries and mathematics
Pandas
Tabular data manipulation and analysis with DataFrames and Series — data wrangling, ETL, financial analysis, and time series.
NumPy
N-dimensional arrays, linear algebra, random sampling, and numerical computing — the foundation of scientific Python.
Matplotlib
Publication-quality static and interactive visualizations — data exploration, scientific figures, dashboards, and reports.
xarray
Multi-dimensional labeled arrays for scientific computing — climate data, geospatial analysis, satellite imagery, and NetCDF.
PyTorch
Deep learning framework for building and training neural networks — from basics to advanced model architectures.
Calculus
Step-by-step tutorials covering limits, derivatives, integrals, advanced applications, and real-world calculus use cases.
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