You learned Big O notation: a way to describe algorithm speed as input grows. You saw why constants and lower terms do not matter for asymptotic analysis.
You know the standard complexity classes: O(1) constant, O(log n) logarithmic, O(n) linear, O(n log n) linearithmic, O(n²) quadratic, O(2^n) exponential, O(n!) factorial.
You analyzed code by counting loops, identifying dominant terms, and comparing growth rates. You practiced with problems: finding max (linear), two sum (hash map), and binary search (logarithmic).
Next section: applying Big O to evaluate and optimize real algorithms.