Big O notation is one of the most fundamental tools to analyze the
cost
of an algorithm.
Big O notation is used to represent the
time
complexity of an algorithm.
Time
complexity
is the computational complexity that describes the amount of time it takes to run an algorithm.
Big-O is about finding the
Asymptotic
upper bound or the worse case runtime.
Asymptotic analysis helps to analyze the algorithm
performance
change in the order of input size.
Big O Notation in Data Structure is used to express algorithmic complexity using
algebraic
terms.
Understanding
of algorithms like Big O helps you to decide what does or does not matter when designing algorithms.
7 things about the MLOps engineer role you must know