If you’re looking for a job in the tech industry, then you know that interviews are a big part of the process. And if you’re hoping to ace your next coding interview, you need to be prepared for some tough questions. In this blog post, we’ll go over the top coding algorithms that are commonly asked in interviews.
We’ll provide step-by-step instructions on how to solve them, and we’ll also give you some tips on how to prepare yourself for the best big day. So whether you’re just starting out your career or you’re gearing up for your next interview, read on for all the information you need to know about acing the tech interview!
Table of Contents
9 Top Coding Algorithms
1. Binary Search
The first algorithm on our list of the top coding algorithms is the binary search. This is a classic algorithm that is used to find an element in a sorted array. The binary search works by dividing the array in half, and then comparing the target element to the middle element of the array. If the target element is less than the middle element, then we know that the target element is in the first half of the array.
On the other hand, if the target element is greater than the middle element, then we know that it is in the second half of the array. We can then repeat this process, dividing the array into smaller and smaller pieces until we find the target element.
Example
Binary search is a searching algorithm that finds an element in a sorted array by repeatedly dividing the array in half until the desired element is found. The time complexity of this algorithm is O(log n).
Here is an example of how binary search works:
Let’s say we have an array of integers that are sorted in ascending order: [0, 11, 12, 23, 33, 45]
We want to find the number 12 in this array. We can do this by using binary search.
First, we compare 12 with the middle element of the array (23). Since 12 is less than 23, we know that the desired element must be in the left half of the array.
Next, we compare 12 with the middle element of the left half of the array (11). Since 12 is greater than 11, we know that the desired element must be in the right half of this sub-array.
Finally, we compare 12 with the middle element of the right half of the array (12). Since they are equal, we have found the desired element and the search is complete!
The binary search algorithm can be implemented in any programming language. Here is an example of how it would work in Python:
def binary_search(array, target):
min = 0
max = len(array) –
while min <= max:
mid = (min + max) //
if array[mid] == target:
return mid
elif array[mid] > target:
max = mid –
else:
min = mid +
return –
This algorithm will return the index of the target element if it is found in the array. If the target element is not found, the algorithm will return -.
You can use this algorithm to find an element in any sorted array. However, keep in mind that binary search only works if the array is sorted!
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2. Dynamic Programming
The top coding algorithm on our list is dynamic programming. Dynamic programming is a technique for solving problems by breaking them down into smaller subproblems.
Once the subproblems are solved, the solution to the original problem can be found by combining the solutions to the subproblems. Dynamic programming is typically used for problems with overlapping subproblems or problems that can be divided into subproblems independent of each other.
Example
One of the most popular dynamic programming algorithms is the Bellman-Ford algorithm, which is used to find the shortest path between two nodes in a graph. The algorithm works by relaxi ng the edges of the graph, one at a time, until it converges on the shortest path.
Another popular dynamic programming algorithm is the knapsack problem, which is used to find the combination of items that maximizes the value of a given knapsack. This problem can be solved using a greedy algorithm, but it is typically solved using dynamic programming.
Finally, the Travelling Salesman Problem (TSP) is a classic problem that can be solved using dynamic programming. The goal of the problem is to find the shortest path that visits all of the nodes in a graph. This problem is NP-hard, meaning that there is no known algorithm that can solve it in polynomial time. However, dynamic programming can be used to find approximate solutions to the problem.
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3. Tree Traversal Algorithms
Tree traversal algorithms are a special type of algorithm that are used to visit all the nodes in a tree. There are two main types of tree traversal: breadth-first search and depth-first search.
Breadth-first search visits the nodes of the tree one level at a time, while depth-first search visits the nodes of the tree one branch at a time. Tree traversal algorithms are used in many different applications, such as searching for a specific element in a tree or finding the shortest path between two nodes in a graph.
Example
Tree traversal algorithms are a common type of question that you’ll encounter in coding interviews. There are three main types of tree traversal algorithms: pre-order, in-order, and post-order. In this section, we’ll go over each one and provide an example of how to solve it.
Pre-order: In a pre-order traversal, the root node is visited first, followed by the left child, and then the right child.
In-order: In an in-order traversal, the left child is visited first, followed by the root node, and then the right child.
Post-order: In a post-order traversal, the left child is visited first, followed by the right child, and then the root node.
Let’s say we have the following tree:
If we were to do a pre-order traversal of this tree, we would visit the nodes in the following order: A, B, D, E, C, F.
If we were to do an in-order traversal of this tree, we would visit the nodes in the following order: D, B, E, A, F, C.
And if we were to do a post-order traversal of this tree, we would visit the nodes in the following order: D, E, B, F, C, A.
4. Hashing Algorithms
Hashing algorithms are used to map arbitrary size data to a fixed size data. The fixed-size data is called a hash, and the mapping is called a hash function.
Hash functions are used in many different applications, such as storing passwords or encrypting data. Hashing algorithms are also used in coding interviews, as they can be used to solve problems such as finding the shortest path between two nodes in a graph.
Example
There are a few different types of hashing algorithms, but the most common ones are MD (Message Digest) and SHA (Secure Hash Algorithm). These algorithms are used to create a “hash” or “digest” of a piece of data. This digest is then used to verify that the data has not been tampered with. For example, when you download a file from the internet, the file is usually “hashed” using one of these algorithms. Then, when you open the file, the computer compares the hash of the file on your computer to the hash of the file on the server. If they match, then you know that the file has not been altered in transit.
There are many different hashing algorithms, but MD and SHA are the most common. Here’s a quick rundown of how each one works:
- MD: The Message Digest algorithm is used to create a hash of data. This hash is then used to verify that the data has not been tampered with.
- SHA: The Secure Hash Algorithm is used to create a hash of data. This hash is then used to verify that the data has not been tampered with.
Both of these algorithms are very important in the world of computer security, and they’re both commonly asked in coding interviews. So if you’re hoping to ace your next interview, make sure you know how both of these algorithms work.
5. Insertion Sort
Insertion sort is a sorting algorithm that works by inserting each element into its correct position in the array. Insertion sort is a stable sorting algorithm, meaning that elements with the same value will retain their original order after the sort.
Insertion sort is typically slower than other sorting algorithms, such as quicksort or heapsort. However, it has the advantage of being an in-place sorting algorithm, meaning that it does not require any additional memory to sort the array.
Example
To see how insertion sort works, let’s take a look at a few examples.
Assuming we have a list of integers that we want to sort from smallest to largest, we can use insertion sort to do so. We start by picking the list’s second element and comparing it to the first element. Since the first element is smaller, we leave it in place and move on to the third element. We compare this element to the second element and find that it is also smaller, so we leave it in place as well. We continue comparing elements in this way until we reach the end of the list. At this point, our list is sorted from smallest to largest.
Insertion sort is a simple algorithm, but it has some important drawbacks.
- First, it is not very efficient for large lists.
- Second, it is not a stable sorting algorithm, which means that it may not preserve the order of elements with equal values.
However, insertion sort is still a useful algorithm to know, and it can be a good choice for small lists or lists with few unique values.
6. Selection Sort
Selection sort is a sorting algorithm that selects the smallest element from the array and swaps it with the first element. Selection sort is an in-place sorting algorithm, meaning that it does not require any additional memory to sort the array.
Selection sort is typically slower than other sorting algorithms, such as quicksort or heapsort. However, it is a stable sorting algorithm, meaning that elements with the same value will retain their original order after the sort.
Example
Now that we’ve gone over the basics of selection sort, let’s take a look at some examples. Example one is an unsorted list:
54, 26, 93, 17, 77, 31, 44, 55, 20
We start by finding the smallest element in the list and swapping it with the first element. The list now looks like this:
17, 26, 93, 54, 77, 31, 44, 55, 20
Now we find the second smallest element in the list and swap it with the second element. The list now looks like this:
17, 20, 93, 54, 77, 31, 44, 55, 26
We continue this process until the list is sorted. The final sorted list looks like this:
17, 20, 26, 31, 44, 54, 55, 77, 93
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7. Search Algorithms
Search algorithms are used to find a specific element in an array. There are two main types of search algorithms: linear search and binary search. Linear search visits each element of the array in order, while binary search only visits the elements that are necessary to find the target element. Binary search is typically faster than linear search, but it requires sorting the array.
Example
There are a few different types of search algorithms that you should be familiar with. The first is the linear search, which is the simplest type of search algorithm. To do a linear search, you simply go through each element in the list one by one until you find the target element. This type of search is not very efficient, but it’s easy to understand and implement.
The next type of search algorithm is the binary search. Binary search is a more efficient way to search through a list, but it requires that the list be sorted in order for it to work. To do a binary search, you start by finding the middle element in the list. If the target element is less than the middle element, then you search the left half of the list. If the target element is greater than the middle element, then you search the right half of the list. You continue doing this until you find the target element or you reach an empty list.
The last type of search algorithm is the hashtable search. Hashtable search is a very efficient way to search through a list, but it requires that you have a hashtable data structure to use.
To do a hashtable search, you simply go through each element in the list and look up its corresponding value in the hashtable. If the value is not in the hashtable, you know the element is not in the list. Hashtable search is a very efficient way to search, but it can be difficult to implement if you’re not familiar with hashtables.
8. Bubble Sort
Bubble sort is a sorting algorithm that repeatedly swaps the adjacent elements if they are in the wrong order. Bubble sort is a stable sorting algorithm, meaning that elements with the same value will retain their original order after the sort.
Bubble sort is typically slower than other sorting algorithms, such as quicksort or heapsort. However, it has the advantage of being an in-place sorting algorithm, meaning that it does not require any additional memory to sort the array.
Example
Let’s take a look at an example. Suppose we have an array of integers that looks like this:
[23, 42, 17, 16, 25]
We can use bubble sort to sort this array in ascending order. First, we compare the first two elements and swap them if they are out of order. In this case, 23 is greater than 17, so we swap them:
[17, 42, 23, 16, 25]
Then, we compare the next two elements and swap them if they are out of order. This process is repeated until the entire array is sorted. In the end, our sorted array looks like this:
[17, 16, 23, 25, 42]
Bubble sort is a simple algorithm, but it is not very efficient. It has a time complexity of O(n^), which means it takes a long time to sort large arrays. However, it is a good algorithm to use for small arrays or if you need to sort an array quickly.
9. Linear Search
Linear search is a search algorithm that works by visiting each element of the array in order. Linear search is a simple algorithm, but it is not very efficient. Binary search is typically faster than linear search, but it requires sorting the array.
Example
Let’s take a look at an example. Suppose we have an array of integers that looks like this:
[11,23,58,31,15,63,27,93,49]
And you’re asked to find the number 58. To do this, you would start at the first number in the array (11) and then compare it to the number you’re looking for (58). If they are not equal, you move on to the next number in the array (23) and compare it again. You continue this until you find the number you’re looking for or until you reach the end of the array.
Linear search is a very simple algorithm, but it’s also one of the most common ones that you’ll be asked in an interview. So it’s important to know how to do it.
Here’s a step-by-step breakdown of how to do a linear search:
- First, you need to have an array of numbers. This can be any size and can contain any type of number (positive, negative, decimal, etc.).
- Next, you need to specify the number that you’re looking for in the array.
- Then, you need to start at the first number in the array and compare it to the number you’re looking for. If they are equal, then you’ve found your number and you can stop there. If they are not equal, then you move on to the next number in the array and compare it again.
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Common Coding Algorithm Questions in Interviews
Now that we’ve gone over the different types of search algorithms, let’s discuss how to solve some common coding interview questions using these algorithms.
Question 1: Given a list of numbers, find the target number.
Solution: We can use a linear search to solve this problem. To do a linear search, we simply go through each element in the list one by one until we find the target number.
Question 2: Given a sorted list of numbers, find the target number.
Solution: We can use a binary search to solve this problem. To do a binary search, we start by finding the middle element in the list. If the target number is less than the middle element, then we search the left half of the list. If the target number is greater than the middle element, then we search the right half of the list. We continue doing this until we find the target number or we reach an empty list.
Question 3: Given a list of numbers, find all the pairs that add up to the target number.
Solution: We can use a hashtable search to solve this problem. To do a hashtable search, we simply go through each element in the list and look up its corresponding value in the hashtable. If the value is not in the hashtable, then we know that the element is not in the list. Hashtable search is a very efficient way to search, but it can be difficult to implement if you’re not familiar with hashtables.
Frequently Asked Questions (FAQs): Top Coding Algorithms
What are coding algorithms?
In the most basic sense, algorithms are a set of instructions for solving a problem. They can be as simple as a recipe for making a cake or as complex as your computer’s steps to calculate pi to the billionth decimal place. When it comes to coding interviews, you can expect to be asked questions about both simple and complex algorithms. The key is to be able to understand the question and then apply the appropriate algorithm to solve it.
There are a few different types of questions that you might be asked about algorithms. The first type is a straight-up coding question, where you’ll be given a problem and then asked to write a function that solves it.
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Which programming language is best for algorithms?
While there are some languages that are better suited for algorithm development, such as C++ and Java, the reality is that any language can be used for algorithms.
The key is to choose a language that you’re comfortable with, allowing you to express your ideas clearly. For some people, that means choosing a language like Python or Ruby, while for others, it may mean using a more traditional language like C++. No matter what language you choose, though, the important thing is to be able to express your ideas clearly and concisely.
Once you’ve chosen a language, the next step is to start practicing. If you’re not sure where to start, there are a number of resources available online that can help you. The website TopCoder, for example, offers a variety of algorithm challenges that you can solve in any language. And once you’ve gotten some practice under your belt, you’ll be well on your way to acing your next coding interview.
How many algorithms are in Python?
There are four main types of algorithms in Python: search, sort, merge, and mathematical. Search algorithms help you find an element in a data structure, sort algorithms help you organize elements in a particular order, merge algorithms help you combine two data structures into one. Mathematical algorithms perform various operations on numbers. To ace your next coding interview, you should be familiar with all of these types of algorithms and learn how to implement them in Python.
Which coding platform is best for interview preparation?
There are a few different options when it comes to choosing a coding platform for interview preparation. The most popular ones are Udemy, LeetCode, and HackerRank. These platforms offer a variety of questions that cover different topics and difficulty levels. If you’re just starting out, we recommend beginning with some of the easier questions on either platform. As you get more comfortable, you can start to tackle the more difficult questions.
Another great way to prepare for your coding interview is by doing practice interviews. This is where you can find a friend or family member who is willing to sit down with you and ask you some of the same types of questions that you might be asked in an actual interview. Practice interviews are a great way to get comfortable with the format and to help you build your confidence.
No matter which platform you choose or how you prepare, the most important thing is that you practice as much as possible. The more questions you see and the more times you go through the interview process, the better prepared you’ll be for your actual interview. And remember, even if you don’t get the job you’re interviewing for, each interview is an opportunity to practice and learn. So don’t get discouraged. Keep at it, and eventually, you’ll be acing those coding interviews in no time.
Is Python better for coding interviews?
Well, some companies prefer to use Python in their interviews, while others prefer Java or C++. There is no one right answer, so it’s important to be prepared for any language that might be thrown your way.
That being said, Python is a great language to know for coding interviews. It’s relatively easy to learn, and it’s becoming increasingly popular in the tech industry. So if you’re looking to get ahead of the competition, learning Python is a good place to start.
Should I use LeetCode or HackerRank?
This is a common question that we hear from students who are preparing for their coding interviews. And the answer is: it depends. If you’re just getting started with algorithm practice, then we recommend using LeetCode. It’s a great resource that offers a variety of problems to solve, and it also includes helpful hints and solutions for each problem.
On the other hand, if you’re looking for a more challenging experience, then HackerRank is a good option. The questions on HackerRank are typically more difficult than those on LeetCode, so it’s a good way to push yourself and see how far you can really go.
Either way, we recommend that you solve as many problems as you can before your interview. The more practice you have, the better prepared you’ll be for whatever questions come your way.
Is HackerRank free for developers?
HackerRank is a free online coding platform that offers developers a chance to hone their skills and compete in challenges. The site also provides users with resources to prepare for interviews, including articles on common interview questions and tips on how to present oneself during an interview best. In order to use HackerRank, developers simply need to create an account and start practicing.
Final Thoughts
In conclusion, we’ve gone over the top coding algorithms that are commonly asked in coding interviews. We’ve provided step-by-step instructions on how to solve them, and we’ve also given you some tips on how to prepare yourself for the best big day. So whether you’re just starting out your career or gearing up for your next interview, we hope that this blog post has given you the information you need to know about acing the tech interview!
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Resources
Conducting The Technical Interview
Coding Interview Prep – Free Resources to Help You Ace Your Interviews
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