What is the difference between Θ(n) and O(n)?

Short explanation:

If an algorithm is of Θ(g(n)), it means that the running time of the algorithm as n (input size) gets larger is proportional to g(n).

If an algorithm is of O(g(n)), it means that the running time of the algorithm as n gets larger is at most proportional to g(n).

Normally, even when people talk about O(g(n)) they actually mean Θ(g(n)) but technically, there is a difference.

More technically:

O(n) represents upper bound. Θ(n) means tight bound. Ω(n) represents lower bound.

Big O means your algorithm will execute in no more steps than in given expression asymptotically (like n^2)

Big Omega means your algorithm will execute in no fewer steps than in the given expression asymptotically (like n^2)

When both condition are true for the same expression, you can use the big theta notation....

f(x) = Θ(g(x)) iff f(x) = O(g(x)) and f(x) = Ω(g(x))

Basically when we say an algorithm is of O(n), it's also O(n²), O(n^1000000), O(2ⁿ), ... but a Θ(n) algorithm is not Θ(n²).