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   Algorithms can make or break software. Take your algorithms from good to great and ace your coding interview today. 

Algorithmic paradigms, asymptotic complexity, and more.


Algorithms are the basis for any great program. Tech companies work to develop the best, most efficient algorithms to reduce computational stress and improve load times. Generally, they save time and money all while creating a product that is fast and easy to use. Needless to say, if you want to advance your career, mastering algorithms is a must. Technical interviews are notorious for asking questions about algorithms.

Algorithms play such an important role in your interview process that your answers to certain questions may filter you to different levels of seniority. If you apply for a senior developer position and end up giving an unsatisfactory answer to a technical question, the hiring team may downlevel your application to a mid-level developer role. If you're prepared, however, just the opposite may happen. An interviewer may be so impressed with your responses that they consider you for a higher position. 

If you have an interview coming up or you're just dusting off some skills in preparation, you may have found that there's a lot more to cover than you remembered. And, you may have noted that the amount of material out there aiming to cover this topic is a bit daunting. 

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want a comprehensive interview prep guide? you've come to the right place.

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Technical interviews are often the most stressful part of the interview process. You must prepare effectively to perform to the best of your ability. If you're feeling overwhelmed, keep these steps in mind instead of trying to face the bombardment of information head-on.


  1. Know the basics.

  2. Understand the use cases for the common algorithmic paradigms.

  3. Be prepared to optimize your program with asymptotic analysis.


If you refer to these goals, you can fight information overload by starting to break down and compartmentalize what you learn. On this page, we'll give you an overview for the majority of topics that you should be familiar with before a technical interview. We won't get too detailed on any one area, but you should form an idea of where you'll need to drill deeper based on your individual knowledge.

The basics of algorithms

Algorithms are essentially a set of deliberate instructions that aim to solve one particular problem. Learning to identify the type of algorithm your problem calls for will help you lay the mental groundwork for fully understanding them in the long run. 


You can start by familiarizing yourself with three of the most common types:

  • Sorting/searching: Examples of sorting algorithms are: insertion sort, bubble sort, selection sort, mergesort, quicksort. Search algorithms are linear search and binary search.

  • Graph: Graph algorithms like breadth-first search (BFS) and depth-first search (DFS) are used to traverse graphs. Graphs represent objects as nodes (vertices), and the relationships between those objects as edges (lines), often forming a network.

  • Shortest path: Algorithms like Djikstra's and Bellman-Ford's are used to find the shortest possible path in a graph.


You can start preparing by studying popular algorithms that every dev should know. One of the first things to refresh is recursion. Recursion algorithms are often a core problem solving approach for some of the most common interview problems.

algorithmic paradigms

An algorithmic paradigm is essentially a design class of algorithms. Paradigms inform what the algorithm is capable of as well as a general structure for how it works.


  • Brute force: One of the simplest but least efficient algorithms, brute force involves checking every single possible outcome. 

  • Divide and conquer: Recursively solves problems by segmenting a larger task into smaller components and reassembles them upon completion.

  • Dynamic programming: Similar to divide and conquer except the subtasks overlap. Completed overlapping sections can be retrieved from memory.

  • Greedy: Solves subtasks using the best possible solution available, or the local optima. 

Other algorithmic frameworks are: approximation, randomized, and linear.

Asymptotic complexity

Asymptotic time complexity refers to the analysis of the exact run time of an algorithm. The calculation is independent of the platform or inputs. Asymptotic space complexity refers to the amount of memory an algorithm consumes. Both complexities are a measure of the efficiency of an algorithm and something all software developers need to be aware of.


There are three asymptotes that need to be calculated to develop an appropriate picture of an algorithm.

  • Big Omega (Ω): Represents the best case.

  • Big Theta (Θ): Represents the average case.

  • Big O Notation (O): Represents the worst case.

Typically, Big O notation is the most common metric used for measuring efficiency. When benchmarking programs, the worst case scenario is what will make or break the functionality. You may well be asked to calculate the Big O complexity of an algorithm in an interview. 


A couple of general best practices for Big O notation are:

  • Ignore the lower order terms

  • Drop the leading constants


For a more in-depth glossary of concepts you should be familiar with, refer to this guide to computer science fundamentals for developers.

Start preparing for algorithm interview questions today

If you're ready to start your algorithm interview prep, don't waste time wading through the glut of information a web search turns up, trying to cobble together bits of information from blogs and YouTube tutorials. You're better suited using a comprehensive approach. The Educative platform is more interactive than passive learning through videos. You engage with the material through in-browser coding environments and address gaps in your knowledge to help actively home in on what you need to learn to code. The Algorithms and Data Structures Interview Crash Course will do more than just prepare you for an interview. It will allow you to retain what you learn so you can further your career even after you get the job.

The 10 Most Common Algorithm Interview Questions in 2023

Tech companies seek people who can think critically instead of reproducing textbook answers to solve problems. You need algorithms to write clean code, so you must do your homework before the interview. Don't get disheartened if you have forgotten some fundamental data structures and algorithm concepts — practicing this list of the top 10 most-asked algorithm interview questions will help you prepare thoroughly.


Algorithm questions can range in difficulty, so we have compiled a holistic list to make your preparation easier. So, when you have to solve a problem in a limited amount of time during the interview, you can quickly recall the essentials from this list.

Top 10 Algorithm Interview Questions

1. What is an algorithm?

Algorithms provide a systematic approach to solving a problem by defining the step-by-step solution in a well-defined formal language. This includes information about calculations, data processing, automated decision-making, and other tasks. Simply put, it defines the computational procedure to take an input value and generate an output value. When you begin to learn machine learning, it is essential to study algorithms as it heavily relies on algorithmic approaches for data analysis and pattern recognition.


2. How can you determine the complexity of an algorithm?

The computational complexity of an algorithm can help you determine the efficiency of one algorithm compared to another. You can measure it using two parameters — time complexity and space complexity:

1. Time complexity:

It measures the algorithm's efficiency by the time it takes to run an algorithm as a function of the input size. Time complexity is measured by asymptotic analysis and represented by the asymptotic notation, which is as follows:

a. Big O Notation (O): This provides an upper bound for the growth rate of the algorithm by bounding the function from above. 

b. Omega notation (Ω): This provides the lower bound on an algorithm's time complexity.

c. Theta notation (Θ): This combines the Big O and Omega notations and gives an upper as well as a lower bound for the algorithm's growth.

2. Space complexity:

Space complexity measures the algorithm's performance based on the amount of memory (space) it needs to run depending on the size of the input value. It is affected by the variables, data structures, and additional memory used during the algorithm's execution. Like time complexity, you can express it in asymptotic notation.


Using asymptotic analysis, you can express the performance of an algorithm in the following terms:


a. Best-case scenario: In the best-case scenario, data arrangement is such that the algorithm performs the best. For example, the most favorable input for a binary search algorithm would be if the target value lies in the center of the data.

b. Worst-case scenario: This represents the worst possible performance of an algorithm, as it takes the most time and space. You can test the scalability and efficiency of the algorithm with this parameter.

c. Average-case scenario: The average-case scenario represents the most realistic expected performance of the algorithm, as it is calculated by the average number of steps taken on any input size.

3. Describe the linear search algorithm

The linear search algorithm is a sequential search method that looks for a target value within a collection of elements. It traverses the entire collection and checks each element one by one until the specific target value is found. It can be described with the following steps:


  1. Define a loop to traverse all the given elements

  2. Compare the target value with the current element value

  3. If it matches the target value, print the index of that element

  4. If it does not match the target value, move to the next element

  5. Repeat steps 2 through 4 until the target value is found

  6. If the entire collection is traversed but the target value is not found, print ‘target value not found’

4. What is a sorting algorithm?

Sorting is an important function in programming as it helps in storing and retrieving data. A sorting algorithm arranges elements in a specific order — such as ascending or descending — depending on the comparison criterion. Here are some of the most well-known sorting algorithms:


  1. Bubble sort: The bubble sort algorithm works by comparing adjacent elements and swapping them if they are in the wrong order. The sorting process finishes when there is no further need for swapping.

  2. Insertion sort: Insertion sort builds a sorted array by inserting one element at a time. First, the array is separated into sorted and unsorted lists. Then, each element from the unsorted list is inserted into the sorted list until all the elements are in order.

  3. Quick sort: The quick sort algorithm is a divide-and-conquer algorithm that picks an element from an array as its pivot. Next, the array is partitioned depending on whether the element is greater or lesser than the pivot. The two partitions are then recursively sorted.

  4. Heap sort: The heap sort algorithm uses max-heap to build a heap from the given array. The maximum or minimum element is then repeatedly extracted and placed in its correct position until the sorting is completed.


5. What is the difference between Breadth-first search (BFS) and Depth-first search (DFS) algorithms?

Breadth-first search and Depth-first search are both algorithms for graph traversal, but there are fundamental differences in their search strategies: 


  • Breadth-first search: Breadth-first search uses a breadth-ward motion to explore the graph. It starts at a specific node and visits all its neighboring nodes before going to the next level. This process is repeated on each level until the target is found. BFS uses a queue data structure to store the node values.

  • Depth-first search: Depth-first search algorithms use a depthward motion to explore the graph. They start at a node and then explore all neighboring nodes to check all branches in depth before backtracking. They then use the stack data structure to store the node values.


6. Write down a string reversal algorithm

Here is a step-by-step algorithm to reverse a string of characters. For example, if you want to reverse 'Jake' to write 'ekaJ', you would follow these steps:


Step 1: Start

Step 2: Initiate two pointers — variables 'l' and 'r'

Step 3: Set the value of 'l' as 0 and the value of 'r' as (length of the string -1)

Step 4: Interchange the values of 'l' and 'r' in the string

Step 5: Increase the value of 'l' by 1

Step 6: Decrease the value of 'r' by 1

Step 7: Step 4 is repeated until the value of 'r' is less than the value of 'l'

Step 8: Stop


7. What is a greedy algorithm? Where can they be used?

A greedy algorithm aims to make the optimal decision at each sub-step of the process, which leads to a globally optimal solution. Using this approach, a greedy algorithm chooses the best answer available at the time without much regard for the consequences. You can use greedy algorithms to find solutions for the following:


  1. Job Scheduling Problem

  2. Knapsack Problem (Fractional Knapsack)

  3. Coin Change

  4. Prim's Minimal Spanning Tree Algorithm

  5. Kruskal's Minimum Spanning Tree Algorithm

  6. Travelling Salesman Problem

8. How can you swap two integers in Java without swapping the temporary variable?

This is a very common trick question asked in coding interviews. Without using a temporary variable, there are two algorithms for Java to solve this problem:


  1. Using mathematical procedures: If a=2 and b=3 and we want to switch the values of 'a' and 'b', we can apply this algorithm

    a= a + b;
    b= a - b;
    a= a - b;

    This works as long as the addition and subtraction do not result in integer overflow. (Extra points if you mention this in your interview!)

  2. Using the XOR trick: This is the best approach as it does not risk integer overflow. The XOR bitwise operator is denoted by the symbol '^'

    x = x ^ y; 
    y = x ^ y; 
    x = x ^ y;

9. What is Dynamic Programming? List some of its applications

Dynamic Programming is an approach to recursion optimization that works by simply saving the results of subproblems in a table or memoization cache so that they don’t need to be recalculated. You can reduce the time complexity of an algorithm from exponential to polynomial with dynamic programming. Some of its applications include the following: 


  • Finding the nth Fibonacci number

  • Solving the discrete (or 0-1) Knapsack Problem

  • Calculating the shortest path between any two nodes in a graph (Floyd Warshall Algorithm)

  • Finding the longest palindromic substring in a string

10. What is the use of a Hash algorithm?

A hashing algorithm is a hash function that takes a string of any length and transforms it into a unique fixed-size string of characters called the hash value. Due to its irreversibility and deterministic properties, you can use the hash function for the following purposes: 


  • Password storage

  • Data retrieval

  • Cryptographic systems


Understanding the underlying principles of the algorithms discussed in this list will help you get a quick overview of algorithms before your interview.


If you are looking for an easy-to-follow course that simulates a real coding interview, check out:


1. Algorithms for Coding Interviews in Java

2. Algorithms for Coding Interviews in Python

3. Algorithms for Coding Interviews in C++ 


Practice with the coding challenges in these courses and work to find solutions so that no technical questions can surprise you during your interview. You can also practice the most common interview questions with our collection of Blind 75 problems

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Cut through technical interviews with ease and land your dream job.

Start your coding interview prep today.

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