In data structure, Theta notation is a way of expressing the time complexity of an algorithm. It is used to describe the upper and lower bounds of the running time, or in other words, the best-case and worst-case scenarios.

## What is Time Complexity?

Before diving into Theta notation, let’s first understand what time complexity means. In simple terms, time complexity measures the amount of time taken by an algorithm to run as a function of the input size. It helps us analyze how efficient an algorithm is and how it will perform as the input size grows.

## Theta Notation

Theta notation is also known as “Big Theta” notation. It provides a tight bound on the growth rate of an algorithm’s running time. The notation uses Θ (capital theta) to represent it.

To give you a better understanding, let’s break down what each part of Θ notation represents:

**Θ(g(n)):**This represents the set of functions that have both an upper bound and a lower bound which are asymptotically equivalent to g(n). In other words, if an algorithm has a time complexity of Θ(g(n)), it means that its running time grows at the same rate as g(n).**Upper Bound:**The upper bound represents the maximum amount of time an algorithm will take to run for any given input size n. It denotes the worst-case scenario.**Lower Bound:**The lower bound represents the minimum amount of time an algorithm will take to run for any given input size n. It denotes the best-case scenario.

### Examples

To illustrate with examples, let’s consider two sorting algorithms – Bubble Sort and Merge Sort.

Bubble Sort has a time complexity of Θ(n^2). This means that in the worst-case scenario, it will take quadratic time (n^2) to sort an array of size n. Similarly, in the best-case scenario, it will also take quadratic time (n^2).

On the other hand, Merge Sort has a time complexity of Θ(n log n). This means that in the worst-case scenario, it will take linearithmic time (n log n) to sort an array of size n. In the best-case scenario as well, Merge Sort takes the same linearithmic time (n log n).

### Why Use Theta Notation?

Theta notation is useful because it provides a clear and concise way to express the running time of an algorithm without getting into unnecessary details. It focuses on the most significant factors that affect the algorithm’s performance.

By using Theta notation, we can easily compare and analyze different algorithms based on their growth rates. This helps in choosing the most efficient algorithm for a specific problem or optimizing existing algorithms.

### Conclusion

In summary, Theta notation is a powerful tool to describe the time complexity of algorithms. It allows us to understand how an algorithm’s running time grows as the input size increases and provides insights into its best-case and worst-case scenarios. By using Theta notation, we can make informed decisions about algorithm selection and optimization.