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algebra

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Data-Science-For-Beginners-from-scratch-course

Data science for beginners involves learning to extract insights from data using statistics, programming (Python/R), and visualization. Key steps include data collection, cleaning, analysis, modeling, and communicating findings. Beginners should start with Python, basic math (linear algebra/calculus), and build projects to create a portfolio.

  • Updated Jun 19, 2026
  • Python

PyPyNum is a versatile Python math lib. It features modules for math, data analysis, arrays, crypto, physics, RNG, data proc, stats, eq solving, image proc, interp, matrix calc, and high-prec math. Designed for scientific computing, data science, and ML, it offers efficient, general-purpose tools.

  • Updated May 3, 2026
  • Python

Irene is a python package that aims to be a toolkit for global optimization problems that can be realized algebraically. It generalizes Lasserre's Relaxation method to handle theoretically any optimization problem with bounded feasibility set. The method is based on solutions of generalized truncated moment problems over commutative real algebras.

  • Updated Jun 13, 2026
  • Python

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