Update 101-big-theta.md (#7364)

* Update 101-big-theta.md

Replace 'Theta' with 'θ' where applicable and remove unnecessary escaping

* Update 101-big-theta.md

Remove unnecessary parentheses

src/data/roadmaps/datastructures-and-algorithms/content/104-algorithmic-complexity/103-asymptotic-notation/101-big-theta.md

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 # Big-θ Notation
 
-Big Theta \(\Theta\) notation is used in computer science to describe an asymptotic tight bound on a function. This essentially means it provides both an upper and lower bound for a function. When we say a function f(n) is \(\Theta(g(n))\), we mean that the growth rate of f(n) is both bounded above and below by the function g(n) after a certain point. This is more precise than Big O and Big Omega notation, which provide only an upper and a lower bound, respectively. Big Theta notation tells us exactly how a function behaves for large input values. For example, if an algorithm has a time complexity of \(\Theta(n^2)\), it means the running time will increase quadratically with the input size.
+Big Theta (θ) notation is used in computer science to describe an asymptotic tight bound on a function. This essentially means it provides both an upper and lower bound for a function. When we say a function f(n) is θ(g(n)), we mean that the growth rate of f(n) is both bounded above and below by the function g(n) after a certain point. This is more precise than Big O and Big Omega notation, which provide only an upper and a lower bound, respectively. Big Theta notation tells us exactly how a function behaves for large input values. For example, if an algorithm has a time complexity of θ(n^2), it means the running time will increase quadratically with the input size.