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# Top 10 Algorithms for the Coding Interview (for Software Engineers)

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# Top 10 Algorithms for the Coding Interview (for Software Engineers)

### You've graduated with a degree in computer science or software engineering, and you're looking for your first job. Read up on the top 10 algorithms for coding interviews.

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You have just graduated from college with a degree in computer science or software engineering and you are looking for a career. You remember that you liked to code in bachelors and did some cool projects with your buddies and decide that you want to be a developer.

You start preparing for job interviews and cannot figure out which algorithms are important to remember for scoring a job. If you are in such a position and have your interview soon enough then this article will help you remember all the coding algorithms that you might need to score a job interview.

The basic job description of a software engineer includes designing, enhancing, and implementing systems and applications.

For that software engineers do not need to remember many complex algorithms. Instead, they are required to use the combinations of working libraries, frameworks, and databases to create something which solves their software needs.

According to experts in the field of software engineering, knowing a few advanced search algorithms helps when you are optimizing them, otherwise, you are more likely to use a built-in library. With that being said here is a list of a few important algorithms of which you should have the basic knowledge when going in for an interview.

## Dynamic Programming

Dynamic programming is the strategy of optimizing recessive functions by eliminating the need for recursive calling. Whenever we see a recursive function in which a certain part of the code is called multiple times it can be greatly improved with the use of dynamic programming. The recursiveness is removed by storing the results of the previous sub-function so that they do not have to be called back multiple times. This reduces the time complexity from exponential to polynomial time. Example of algorithms which fall under the dynamic programming category are:

• Ugly Numbers

• Fibonacci Numbers

• Binomial Coefficient

• Permutation Coefficient

## Binary Search

As the name suggests searching algorithms are used to search for an element from a given set known as the data structure. Binary search works when provided with a sorted array of elements and a search key. Binary search works by selecting the middle element and comparing it with the search key if the key is smaller than the left part of the middle element is traverse in the same way. If now than the right portion on searched for the key. The time complexity of a binary search is O(log n), where n is the number of elements in the array.

## Sorting Algorithms

Sorting algorithms are used for sorting an array, the input includes a data type which needs sorting. The data set can be sorted in either ascending or descending order. The following are a few important sorting algorithms to remember.

## Merge Sort

Merge sort works on the principles of divide and conquer algorithm. It refers to the practice of dividing a problem into smaller parts and solving them one by one and merging them together in the end. Merge sort divides the array in half and calls the sort function on the two halves, those two halves are sorted and then merged together using the merge function. The time complexity of merge sort is O(n log n).

## Quick Sort

Like merge sort, quick sort is also based on divide and conquer algorithm, it varies from merge sort in terms of its sorting functionality. Quicksort works by selecting the last element as the pivot number and places it in the middle with smaller numbers on the left while the larger ones on the right. The left and right sides are again called with the sort function which in result sortes the whole array. The time complexity of quicksort is O(n^2).

## Depth First Search

DFS is a searching algorithm that starts the search process from the node and goes all the way down to the last leaf of the leftmost branch. After it has reached the leftmost leaf the algorithm starts backtracking and traverses the right side of the tree and so on. The issue with this DFS is that if a cycle exists, a certain node can be visited more than once. The time complexity of DFS is O(V + E), where V and E represent the number of vertices and edges respectively in the graph.

BFS is a searching algorithm that starts from the root just like DFS. But instead of traversing all the leaves on the left, it searches in the neighbourhood of the node on the same level. After a level is traversed the algorithm moves forward to the next level and keeps on traversing until the element is found. The time complexity of BFS is the same as DFS, which is O(V + E).

## Custom Data structure

Sometimes the typical, pre-defined data structures do not get the job done and you need something better and more powerful. Custom data structures can be real or abstract objects depending on the uses of their data members. Data members can be considered as variables belonging to objects which are specified.

## Hash Tables

Hash tables are a type of data structure that is used to store, access, and modify data with the time complexity of O(1). Hash data structures use the Hash function to map a given value to a specific key. This key is then used to access and retrieve those values quickly, the efficiency of how the Hash would perform depends upon the type of Hash function used.

Usually, the components of an array or any linked data structures are stored in contiguous memory locations. This takes up spaces and certain chunks of memory are not accessible (that is if you have low memory). To overcome this, linked list data structures are used in which the data is not stored contiguously, instead every item in the list has a pointer that points to the next element's memory location. The first element is known as the head and the last is known as the tail.

The most important thing that a software engineer should know is what to ask its client. Most clients are not able to get their point across and if the developer does not ask any questions then it could cause a problem due to miscommunication. This way you will be able to understand the core problem of what they are trying to achieve and not just the difficulties that they are facing.

## Conclusion

Armed with the knowledge of these basic algorithms, one can easily nail an interview. Remember that software engineers usually do not rely on these algorithms to get the job done. Instead, these are used to test the understanding of the individual on whether he knows the working of the code or not. With that said, best of luck for your next interview.

Topics:
coding basics, developer job interview, programming, search algorithms, software development

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