14 must-know algorithms for coding interviews

Dream of working at a MAANG company? Landing a job at one of these top-tier tech giants is the aspiration of many software engineers professionals. However, many fall short due to the incredibly rigorous coding interviews. These interviews are meticulously designed to filter out the best talent by not only testing your coding skills but also evaluating your problem-solving abilities, algorithmic knowledge, and efficiency in finding solutions.

This blog is a one-stop guide for software developers hoping to ace coding interviews. Here, we’ll explore some fundamental algorithms that are the key building blocks for coding interviews. By mastering these algorithms, you’ll showcase your problem-solving prowess, enhance your coding efficiency, and demonstrate your ability to think logically—qualities highly valued by hiring managers at top tech companies.

Today we will cover several important categories of algorithms (including sorting algorithms, searching algorithms, and more), and dive into 14 specific algorithms you'll need to know for your coding interview. These essential coding interview algorithms are:

1. Bubble sort

2. Selection sort

3. Insertion sort

4. Heap sort

5. Linear search

6. Binary search

7. Depth-first search (DFS)

8. Breadth-first search (BFS)

9. Merge sort

10. Quick sort

11. Dijkstra's algorithm

12. Huffman coding

13. Fractional knapsack problem

14. Dynamic programming

Note: We will cover each of these algorithms and use cases in detail below.

If you understand how these algorithms work and when to use them, you will be in great shape for your coding interviews at even the most prestigious tech companies.

Let's dive in!

Importance of algorithms in coding interviews

Understanding why algorithms are crucial in coding interviews is fundamental to your preparation. Algorithms are the backbone of problem-solving in computer science, and understanding them showcases the candidate’s ability to think logically, solve complex problems, and write efficient code.

Why algorithms matter

 Algorithms are important because they:

• Test problem-solving skills: Algorithms require you to break down problems into manageable parts, develop step-by-step solutions, and choose optimal approaches—mirroring real-world challenges in tech.

• Emphasize efficiency: Understanding algorithms helps you write code that not only works but performs well. In the tech industry, efficient code can mean the difference between a product that scales and one that doesn’t. Interviewers look for candidates who can optimize their solutions to save time and resources.

• Demonstrate the breadth of knowledge: Your familiarity with different algorithms shows your breadth of knowledge and ability to apply the right tool for the job. It reflects your readiness to handle a variety of tasks that you might encounter on the job.

Real-world applications

Algorithms aren’t just theoretical concepts; they have practical applications across various tech domains. For instance, search algorithms like binary search are used by digital catalogs to search and retrieve information about particular items in the database. Sorting algorithms like merge sort power recommendation systems that suggest relevant products or content to you based on your preferences. By understanding these algorithms, you gain valuable insight into how tech giants handle data and solve complex problems behind the scenes.

Understanding the significance of algorithms in coding interviews and their real-world applications prepares you for technical challenges and provides insights into how industry leaders solve intricate problems. This foundational knowledge is significant for excelling in coding interviews and pursuing careers at top-tier tech companies.

Must-know algorithms

Now that we understand why algorithms are crucial in coding interviews and how they apply to real-world scenarios, let’s dive into the specific algorithms you need to know to ace your coding interviews. We’ll cover essential algorithms across various categories, including sorting, searching, tree algorithms, dynamic programming, and more. By the end of this section, you’ll have an understanding of the algorithms that are commonly tested in interviews and how to solve problems using them.

Here are some of the fundamental algorithms you’ll likely encounter in your coding interviews:

Sorting algorithms

Sorting algorithms are fundamental for data manipulation. They efficiently arrange data into a specific order, typically ascending or descending. Mastering these algorithms is essential for coding interviews because they test your ability to organize data effectively. Moreover, they form the foundation for many other algorithms and applications, making them essential knowledge for any aspiring software engineer. Here are some commonly used sorting algorithms:

1) Bubble sort

This sorting algorithm might not be the fastest, but it provides a great starting point for understanding more complex sorting techniques.

This algorithm relies on comparisons between adjacent elements in the list. It iterates through the list repeatedly, comparing each element with its neighbor. If the elements are in the wrong order, they are swapped. This process continues until no more swaps are needed, indicating that the list is sorted.

• Use case: It is suitable for small datasets or when simplicity is more important than efficiency.

2) Selection sort

Selection sort works by repeatedly finding the minimum element from the unsorted part of the array and swapping it with the first unsorted element. It gradually builds the sorted segment of the array.

• Use case: It is suitable for small datasets.

3) Insertion sort

Insertion sort builds the sorted array one element at a time by repeatedly taking the next element and inserting it into its correct position among the already sorted elements.

• Use case: It is efficient for small datasets or when the array is almost sorted.

4) Heap sort

Heap sort is a comparison-based algorithm that uses a binary heap data structure. It first builds a max heap and then repeatedly extracts the maximum element to build the sorted list.

• Use case: It is suitable for applications with large datasets requiring in-place sorting.

Searching algorithms

Searching algorithms are fundamental in computer science for retrieving information stored within data structures. Mastering searching algorithms is crucial for coding interviews because they test your ability to handle and navigate data effectively.

Here, we’ll focus on two fundamental and widely used search algorithms.

5) Linear search

Linear search sequentially checks each dataset element until the target element is found or all elements have been checked.

• Use case: It is suitable for unsorted or small datasets where elements are not in any specific order.

6) Binary search

Binary Search is a divide-and-conquer algorithm that requires data to be sorted. It repeatedly divides the dataset in half and compares the target value with the middle element. It continues narrowing down the search space until the target element is found or the search space is traversed.

• Use case: It is ideal for large sorted datasets, such as arrays or lists, where quick retrieval is required.

Tree algorithms 

Tree algorithms are fundamental for managing hierarchical data structures efficiently. Trees are hierarchical data structures consisting of nodes, where each node can have zero or more child nodes. The node without a parent is called the root, and nodes without children are called leaf nodes. Tree algorithms are designed to perform operations such as traversal, insertion, deletion, and searching within these structures. Here, we will look at the two popular tree traversal algorithms.

7) Depth-first search (DFS)

In this algorithm, we explore as far as possible along each branch before backtracking. To understand this better, you can think of exploring a maze by following a single path as deep as possible before backtracking and trying other paths.

The DFS algorithm works as follows:

1. Start at the root node.

2. Choose a child node (usually the leftmost one) and explore it as far as possible.

3. If you reach a dead end (no child nodes), backtrack to the parent node.

4. Explore another child node of the parent (if available) by following the same approach.

5. Continue until all nodes have been visited.

There are three main methods to traverse a tree with the tree depth-first search pattern—preorder, inorder, and postorder. The following illustration shows an example of preorder traversal:

8) Breadth-first search (BFS)

Explore all nodes at the present depth level before moving on to nodes at the next depth level. For this, you can imagine exploring a neighborhood by methodically going down each street, visiting every house on that street before moving on to the next street.

The BFS algorithm works as follows:

1. Start at the root node.

2. Visit all the child nodes of the root level by level.

3. For each child node, explore all its child nodes at the same level before moving down to the next level.

4. Continue until all nodes have been visited.

Here’s the table summarizing the difference between the two:

The following illustration shows an example of how breadth-first search traverses the nodes of a tree:

Now, let’s explore some more algorithms using popular algorithmic techniques that are frequently tested in the interviews:

Divide and conquer

This is a powerful algorithmic strategy that breaks down a complex problem into smaller, more manageable subproblems, solves each subproblem independently, and then combines their solutions to solve the original problem. 

It typically involves three steps:

1. Divide: Break the original problem into smaller subproblems similar to the original problem but simpler to solve.

2. Conquer: Solve each subproblem recursively unless the subproblem size is small enough to be solved directly (base case). 

3. Combine: Merge the solutions of the subproblems to get the solution to the original problem.

Let’s look at some notable divide-and-conquer algorithms.

9) Merge sort

It follows the divide and conquer algorithm by recursively dividing an unsorted list into halves until each sublist has only one element (sorted by itself). Then, it merges these sorted sublists together in the correct order to create the final sorted list.

• Use case: Merge sort is efficient for large datasets.

10) Quick sort

It is another popular divide-and-conquer sorting algorithm. It works by choosing a pivot element from the list and partitioning the list around it. Elements less than the pivot are shifted to the left side, and elements greater than the pivot move to the right side. These partitions become the new subproblems, which are then sorted recursively and finally combined.

• Use case: It is efficient for large datasets and widely used for in-place sorting.

Greedy algorithms

Greedy algorithms are straightforward, iterative algorithms used to make optimal choices at each step in the hope of finding a global optimum. They are especially useful for problems where a locally optimal choice leads to a globally optimal solution. While this might not always guarantee finding the global optimum, it often yields a satisfactory solution in a reasonable amount of time.

Now, let’s look at some popular greedy algorithms.

11) Dijkstra’s algorithm

This algorithm finds the shortest path between two nodes in a weighted graph. At each step, it chooses the unvisited node with the minimum distance from the starting node, prioritizing the closest unexplored option.

• Use case: It is used in routing algorithms to find the shortest network path, such as GPS navigation systems.

12) Huffman coding

Huffman coding is a data compression technique that assigns variable-length codes to characters based on their frequencies in the text, with shorter codes assigned to more frequent characters.

• Use case: File compression algorithms, such as those used in ZIP files and multimedia applications, rely on these techniques to reduce file size efficiently.

13) Fractional knapsack problem

Given the weights and values of items, the fractional knapsack problem maximizes the total value in the knapsack without exceeding its capacity. Unlike 0/1 knapsack, here fractions of items can be taken.

• Use case: It is used in resource allocation problems, such as selecting investments to maximize returns given limited resources.

Dynamic programming problems

Dynamic programming (DP) is a method for solving complex problems by breaking them down into simpler subproblems and storing their solutions to avoid redundant computations. It’s particularly useful for optimization problems where solutions to subproblems can be reused.

14) Dynamic programming

It’s useful to formally define the characteristics of problems that can be addressed using DP approaches:

• Optimal substructure: If it is possible to break down a given problem into smaller subproblems and an optimal solution to these subproblems exists and contributes to the solution of the given problem, that means an optimal substructure exists for such a problem.

• Overlapping subproblems: If the solution to the overall problem requires solving some of the subproblems repeatedly, the problem is said to feature overlapping subproblems and is a good candidate for optimization using DP.

It is only if the solution to a problem has these properties that we may use dynamic programming to optimize it.

How it works

1. Define the problem: Clearly define the problem and identify the optimal substructure.

2. Formulate a recurrence relation: Express the solution to the problem in terms of solutions to smaller subproblems.

3. Select an appropriate technique: Implement the solution using either of the following:

   1. Memoization: The memoization approach, also known as top-down, is usually implemented as an enhancement of the naive solution. It uses recursion to break down larger subproblems into smaller ones. The smallest one is solved, and the result is stored in a lookup table for use in computing larger subproblems.

   2. Tabulation: The tabulation approach, also known as a bottom-up approach, solves the smallest problem first, saves the results, and computes larger subproblems based on the evaluated results. The bottom-up approach starts by solving the smallest subproblem and then iterates progressively through larger subproblems to reach the overall solution.

4. Optimize the solution: Analyze time and space complexity to optimize the solution further if needed.

Dynamic programming can initially seem daunting, but with practice, it transforms into a powerful tool in your problem-solving arsenal. At Educative, we provide a comprehensive course dedicated to mastering DP problems. Our course intelligently categorizes problems into patterns, simplifying the understanding of core DP concepts and enabling you to effectively tackle any problem, even those you haven’t encountered before.

Explore the dynamic programming courses available in Python, C++, Java, and JavaScript languages to start mastering these essential skills today.

Conclusion

Mastering essential algorithms is crucial for acing coding interviews, especially when aiming for positions at top-tier tech companies like MAANG. This journey isn’t just about memorizing algorithms. Rather, it’s about understanding how these algorithms tackle problems efficiently.
From sorting and searching algorithms to tree traversals, divide and conquer, dynamic programming, and greedy algorithms, each algorithm offers unique benefits and demonstrates your ability to solve complex problems efficiently. Understanding these algorithms not only helps you perform well in interviews but also equips you with the skills necessary for real-world applications, making you a valuable asset to any technical team.

What’s next?

This is just the beginning of your algorithmic adventure! As you explore further, you’ll encounter more advanced algorithms and data structures that will take your problem-solving skills to the next level. However, understanding these algorithms isn’t enough—you must practice implementing them before you’re ready for interviews. Platforms like Educative and LeetCode provide a wide range of problems for practice.

For those looking to deepen their understanding, Educative offers a variety of courses designed to increase your knowledge of algorithms and data structures.

Remember, the world of algorithms is vast and ever-evolving. By practicing regularly, you’ll pave the way for a successful tech career.

Happy coding!