# 14 Patterns To Ace Any Coding Interview If you’re preparing for a coding interview, it can be overwhelming to know where to start. There are so many different topics and concepts to cover, and it can feel like there’s not enough time to learn everything you need to know. However, one helpful approach is focusing on patterns – common techniques that appear across many coding problems. Here in this blog we are going to talk about the 14 patterns to ace any coding interview. By learning these patterns, you can gain a deeper understanding of how to solve a wide range of problems and increase your chances of acing the interview.

## 14 Patterns To Ace Any Coding Interview

1. Brute Force: Sometimes, the simplest method is to try every possible solution and see which one works. This can be a helpful starting point for many problems, especially if the input size is small.
2. Greedy Algorithms: In some problems, the optimal solution involves making a series of locally optimal choices. Greedy algorithms prioritize immediate gains, rather than considering the long-term consequences of those choices.
3. Divide and Conquer: This pattern involves breaking a problem down into smaller sub-problems, solving each one separately, and then combining the solutions. This can be a very effective technique for problems that can be easily divided into smaller parts.
4. Dynamic Programming: This pattern involves solving a problem by breaking it down into smaller sub-problems, solving each one only once, and storing the solutions in a table or array. This can be a very powerful technique for problems that have overlapping sub-problems.
5. Backtracking: This pattern involves exploring all possible solutions by generating candidates incrementally and testing whether each candidate satisfies the problem constraints.
1. Depth-First Search: This pattern involves exploring a graph or tree by visiting each node and its children as deeply as possible before backtracking. This can be a very useful approach for problems that involve searching for a path or a sequence of steps.
2. Breadth-First Search: This pattern involves exploring a graph or tree by visiting all the nodes at a given depth before moving on to the next level. This can be a very useful approach for problems that involve searching for the shortest path or the minimum number of steps.
3. Two-Pointers: This pattern involves maintaining two pointers that move through an array or a linked list in opposite directions. This can be a very effective approach for problems that involve searching for a pair of elements that satisfy a certain condition.
4. Sliding Window: This pattern involves maintaining a window of elements in an array or a string and moving the window forward or backward as needed. This can be a very useful approach for problems that involve searching for a substring or a subarray that satisfies a certain condition.
5. Binary Search: This pattern often divides an ordered array into half until the desired element is found. This can be a very efficient approach for problems that involve searching for a specific element in a sorted array.
6. Topological Sort: This pattern involves ordering the nodes of a directed acyclic graph (DAG) in such a way that for every directed edge (u, v), u comes before v in the ordering. This can be a very useful approach for problems that involve scheduling or dependency management.
7. Minimum Spanning Tree: This pattern involves finding the minimum set of edges that connect all the nodes of a graph. This can be a very useful approach for problems that involve finding the most efficient way to connect a set of points.
8. Shortest Path: This pattern involves finding the minimum cost path between two nodes in a graph. This can be a very useful approach for problems that involve finding the most efficient way to travel between two points.
9. Trie: This pattern involves building a tree-like data structure called a trie (pronounced “try”) that allows for efficient storage and retrieval of strings. This can be a very useful approach for problems that involve searching for words or substrings in a large collection of text.

By familiarizing yourself with these 14 patterns, you’ll be better equipped to tackle a wide range of coding interview problems. However, it’s important to note that these patterns are not mutually exclusive – many problems can be solved using a combination of two or more patterns. Additionally, it’s important to practice implementing these patterns in code, so that you’re comfortable applying them in a real-world setting.