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Build deep expertise in algorithms and data structures — from arrays and linked lists to graphs and dynamic programming.

Master differential and integral calculus, real analysis, and multivariable functions through structured, concept-first teaching.

Develop a strong intuitive and rigorous understanding of linear algebra — the mathematical language of machine learning and engineering.

A rigorous treatment of Newtonian mechanics — from kinematics and Newton's laws to energy, momentum, and rotational dynamics.

Essential probability theory and statistical methods for engineering, data analysis, and scientific research.

Logic, sets, relations, graph theory, combinatorics, and proof techniques — the mathematical backbone of computer science.