This course introduces algorithmic complexity and explains how Big-O notation helps measure algorithm efficiency. It covers common complexity types, asymptotic analysis, simplification rules, best, average and worst-case scenarios, and time and space complexity. Learners will also explore amortized analysis, basic recurrence relations, and practical examples to understand how algorithms perform as input sizes grow.