Top data structures and algorithms every developer must know

Many shudder at the thought of a coding interview. It can be stressful, grueling, and challenging. Oftentimes, it can be a struggle simply to know what topics to prepare.

Today, you will be introduced to the primary data structures and algorithms that are tested in a coding interview. After reading this article, you should have a good idea of what you need to prepare for to land your dream job.

We will cover the following:

Why should you learn data structures and algorithms?
Understanding Big O notation
Important data structures to learn
Important algorithms to learn
What to learn next

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Why should you learn data structures and algorithms?#

The coding interview tests for your problem-solving abilities and understanding of computer science concepts. Usually, you are given about 30 - 45 minutes to solve one complex problem.

This is where data structures and algorithms come in. These interviews will test you on topics such as linked lists, queues, sorting, searching, and much more, so it’s crucial to prepare.

Not only do companies want to test your technical knowledge, but they also want to evaluate your problem-solving skills.

If you get the job, you will often face bugs that you need to fix, and companies want to make sure that you can overcome these obstacles.

Furthermore, you will often use specific data structures and algorithms to optimize your code so that it runs as efficiently as possible.

Understanding Big O notation#

Big O notation is an asymptotic analysis that describes how long an algorithm takes to perform. In other words, it’s used to talk about how efficient or complex an algorithm is.

Big O describes the execution time, or run time, of an algorithm relative to its input $N$ as the input increases. Though we can analyze an algorithm’s efficiency using average-case and best-case, we typically use worst-case with Big O notation.

Today, you will learn about time complexity, but take note that space complexity is also an important concept to understand. Runtime complexity can be a difficult concept to grasp at first, so let’s look at some examples.

Some rules to remember#

Ignore constants: When using Big O notation, you always drop the constants. So, even if the runtime complexity is $O(2N)$ , we call it $O(N)$ .
Drop less dominant terms: You only keep the most dominant term when talking Big O. For example, $O(N^3 + 50N +1 7)$ is simply $O(N^3)$ . Here’s the rule of thumb: $O(1)$ < $O(logN)$ < $O(N)$ < $O(NlogN)$ < $O(N^2)$ < $O(2^N)$ < $O(N!)$ .

Since each popular algorithm or structure has consistent complexity, its helpful to keep a Big-O Cheat Sheet for ease of access.


import java.io.*; 
  
// Java program to implement 
// a Singly Linked List 
public class LinkedList { 
  
    Node head; // head of list 
  
    // Linked list Node. 
    // This inner class is made static 
    // so that main() can access it 
    static class Node { 
  
        int data; 
        Node next; 
  
        // Constructor 
        Node(int d) 
        { 
            data = d; 
            next = null; 
        } 
    } 
  
    // Method to insert a new node 
    public static LinkedList insert(LinkedList list, int data) 
    { 
        // Create a new node with given data 
        Node new_node = new Node(data); 
        new_node.next = null; 
  
        // If the Linked List is empty, 
        // then make the new node as head 
        if (list.head == null) { 
            list.head = new_node; 
        } 
        else { 
            // Else traverse till the last node 
            // and insert the new_node there 
            Node last = list.head; 
            while (last.next != null) { 
                last = last.next; 
            } 
  
            // Insert the new_node at last node 
            last.next = new_node; 
        } 
  
        // Return the list by head 
        return list; 
    } 
  
    // Method to print the LinkedList. 
    public static void printList(LinkedList list) 
    { 
        Node currNode = list.head; 
   
        System.out.print("LinkedList: "); 
   
        // Traverse through the LinkedList 
        while (currNode != null) { 
            // Print the data at current node 
            System.out.print(currNode.data + " "); 
   
            // Go to next node 
            currNode = currNode.next; 
        } 
    } 
   
    // Driver code 
    public static void main(String[] args) 
    { 
        /* Start with the empty list. */
        LinkedList list = new LinkedList(); 
  
        // 
        // ******INSERTION****** 
        // 
  
        // Insert the values 
        list = insert(list, 1); 
        list = insert(list, 2); 
        list = insert(list, 3); 
        list = insert(list, 4); 
        list = insert(list, 5); 
        list = insert(list, 6); 
        list = insert(list, 7); 
        list = insert(list, 8); 
  
        // Print the LinkedList 
        printList(list); 
    } 
}

Stack.java

Main.java

class Stack { 
    static final int MAX = 1000; 
    int top; 
    int a[] = new int[MAX]; // Maximum size of Stack 
  
    boolean isEmpty() 
    { 
        return (top < 0); 
    } 
    Stack() 
    { 
        top = -1; 
    } 
  
    boolean push(int x) 
    { 
        if (top >= (MAX - 1)) { 
            System.out.println("Stack Overflow"); 
            return false; 
        } 
        else { 
            a[++top] = x; 
            System.out.println(x + " pushed into stack"); 
            return true; 
        } 
    } 
  
    int pop() 
    { 
        if (top < 0) { 
            System.out.println("Stack Underflow"); 
            return 0; 
        } 
        else { 
            int x = a[top--]; 
            return x; 
        } 
    } 
  
    int peek() 
    { 
        if (top < 0) { 
            System.out.println("Stack Underflow"); 
            return 0; 
        } 
        else { 
            int x = a[top]; 
            return x; 
        } 
    } 
}

Queue.java

Main.java

class Queue 
{ 
    int front, rear, size; 
    int  capacity; 
    int array[]; 
       
    public Queue(int capacity) { 
         this.capacity = capacity; 
         front = this.size = 0;  
         rear = capacity - 1; 
         array = new int[this.capacity]; 
            
    } 
       
    // Queue is full when size becomes equal to  
    // the capacity  
    boolean isFull(Queue queue) 
    {  return (queue.size == queue.capacity); 
    } 
       
    // Queue is empty when size is 0 
    boolean isEmpty(Queue queue) 
    {  return (queue.size == 0); } 
       
    // Method to add an item to the queue.  
    // It changes rear and size 
    void enqueue( int item) 
    { 
        if (isFull(this)) 
            return; 
        this.rear = (this.rear + 1)%this.capacity; 
        this.array[this.rear] = item; 
        this.size = this.size + 1; 
        System.out.println(item+ " enqueued to queue"); 
    } 
       
    // Method to remove an item from queue.   
    // It changes front and size 
    int dequeue() 
    { 
        if (isEmpty(this)) 
            return Integer.MIN_VALUE; 
           
        int item = this.array[this.front]; 
        this.front = (this.front + 1)%this.capacity; 
        this.size = this.size - 1; 
        return item; 
    } 
       
    // Method to get front of queue 
    int front() 
    { 
        if (isEmpty(this)) 
            return Integer.MIN_VALUE; 
           
        return this.array[this.front]; 
    } 
        
    // Method to get rear of queue 
    int rear() 
    { 
        if (isEmpty(this)) 
            return Integer.MIN_VALUE; 
           
        return this.array[this.rear]; 
    } 
}

Top data structures and algorithms every developer must know

Why should you learn data structures and algorithms?#

Understanding Big O notation#

O(1)O(1)O(1)#

O(N)O(N)O(N)#

O(N2)O(N^2)O(N2)#

Some rules to remember#

Important data structures to learn#

Arrays#

Linked lists#

Stacks#

Queues#

Graphs#