## In this article, let's look at some of the top algorithms to crack the coding interview

In a programmer's life algorithms and, data structures are the most important subjects if they want to go out into the programming world and make some bucks. A programming algorithm is a procedure or formula used for solving a problem. Algorithms form the basis of all programming languages and give a clear definition of the program. The typical algorithms that would learn in a data structures and algorithms class are very common in coding interviews. In this article, let's look at some of the most popular algorithms that you should have the basic knowledge of when going in for coding interviews.

Tree Traversal Algorithms: Trees are a special form of data structure that includes a root node connected to sub-trees in a linked node format. These are algorithms that allow visiting every node in a tree in a structured order. Pre-order traversal, Post-order traversal, and In-order traversal are the 3 types of tree traversals possible, but these are the three most common ones to start with.

Search Algorithms: An essential part of using any data set, searching algorithms are used to search for an element stored in any form of data structure. This is a class of algorithms that really only have one important algorithm of binary search. These algorithms form the foundation of four areas databases, virtual spaces, sub-structures, or quantum computers.

Sorting Algorithms: These algorithms help in arranging data in a particular form to make meaning out of it. Two of the most common algorithms in this class are merge sort and quick sort. Sorting algorithms are generally differentiated based on the method they use to sort the data and each technique has its own set of codes and uses.

Binary Search: Binary search works when provided with a sorted array of elements and a search key. It is fairly basic and easy to understand and implement. While linear search compares the input element with every element in the list starting from the first element, the binary search algorithm starts with the middle element.

Dynamic Programming: This programming refers to the optimization of recessive functions by eliminating the need for recursive calling. Dynamic programming or memorization eliminates problems by storing the already calculated values and using them whenever necessary. This works by storing the solutions of each sub-problem and then using these states later to simplify complexities and reduce computation time.

Linear Search: A linear search is the simplest method of searching a data set. It works by starting from the 0th element and comparing the user's input element with each term and finally returning the position of the element.

Bubble Sort: This is the easiest sorting algorithm and works by comparing adjacent values in the list and placing them in the correct order. It is a sorting algorithm that compares two adjacent elements and swaps them until they are not in the intended order.

Graph Search Algorithms: Similar to trees, graphs are also a type of non-linear data structure that consists of nodes connected to each other through edges. These work on trees, graphs with vertices and edges, and any encoding of a graph.

Quick Sort: Quicksort is a sorting algorithm based on the divide and conquers approach. Quicksort works by selecting the last element as the pivot number and placing it in the middle with smaller numbers on the left while placing the larger ones on the right.

Hashing Algorithms: Hash functions are, essentially, mathematical functions that convert complex input numbers into compressed numerical values.  Hashing algorithms take any input and convert it to a uniform message by using a hashing table. It is a mathematical algorithm that converts an input data array of a certain type and arbitrary length to an output bit string of a fixed length.

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