Big O notation is a mathematical notation that describes the limiting behavior of a function when the argument tends towards a particular value or infinity. It is a member of a family of notations invented by Paul Bachmann, Edmund Landau, and others, collectively called BachmannLandau notation or asymptotic notation.
Academics use big O, big Θ (theta), big Ω (omega) to describe runtimes.
O (big O)

f(n) = O(g(n)) 
Onotation gives an upper bound for a function to within a constant factor. We write f (n) = O(g(n)) if there are positive constants n0 and c such that to the right of n0, the value of f (n) always lies on or below cg(n).
Ω (big omega)

f(n) = Ω(g(n)) 
Ω notation gives a lower bound for a function to within a constant factor. We write f (n) = Ω (g(n)) if there are positive constants n0 and c such that to the right of n0, the value of f(n) always lies on or above cg(n).
Θ (big theta)

f(n) = Θ(g(n)) 
Θnotation bounds a function to
within constant factors. We write f(n) = Θ(g(n)) if there exist positive constants n0, c1, and c2 such
that to the right of n0, the value of f(n) always lies between c1g(n) and c2g(n) inclusive.
Big O notation in software development industry
In software development industry, people seem to have merge all this concept into one. In software development we use Big O notation to work out how long an algorithm will take to run. This lets us understand how a piece of code will scale. It measures algorithmic efficiency.
Using Onotation, we can often describe the running time of an algorithm
merely by inspecting the algorithm’s overall structure. For example, the doubly
nested loop structure yields an O(n^2) on the worstcase running time.
Running time of an algorithm
 O(log n)
 O(1)
 O(n)
 O(n log n)
 O(n^2)
 O(2^n)
 O(n!)
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