# 12 Data Structures and Top Interview Questions Every Developer Should Know

## 1\. [**Arrays**](https://youtu.be/qAEgiVxJs4Y?si=sctGyohlCVCaoNKE)

An **array** is a collection of elements stored at contiguous memory locations. It allows constant-time access to elements if their index is known.

**Analogy**: Imagine a row of lockers in a school, each labeled with a number. To find an item, you go directly to the locker by its number.

### Key Operations:

* Access: (O(1)) if the index is known.
    
* Insertion/Deletion: (O(n)) (shifting elements may be needed).
    

### Interview Question:

**Problem**: Write a function to find the second largest number in an array of integers.

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1732001114973/21129142-894e-4daa-9a31-1a0bffa99d48.png align="center")

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## 2\. [**Linked Lists**](https://hojaleaks.com/introduction-to-linked-lists-and-types-of-linked-lists-data-structures-and-algorithms-day-3)

A **linked list** is a collection of nodes where each node contains data and a reference to the next node in the sequence.

**Analogy**: Consider a treasure hunt where each clue leads to the next location.

### Key Operations:

* Traversal: (O(n))
    
* Insertion/Deletion: (O(1)) (if the pointer to the node is known).
    

### Interview Question:

**Problem**: Reverse a linked list iteratively.

![How To Reverse A Linked List With Animated Examples | by Mike Cronin | Geek  Culture | Medium](https://miro.medium.com/v2/resize:fit:1400/1*XTQtyjLQOkESP5_U9gNbfg.png align="left")

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## 3\. **Stacks**

A **stack** is a collection of elements that follows the Last In, First Out (LIFO) principle. Only the top element can be accessed or modified at any time.

**Analogy**: Imagine a stack of plates where you can only take or add plates from the top.

### Key Operations:

* Push (add): (O(1))
    
* Pop (remove): (O(1))
    

%[https://youtu.be/kyFtUyO170Y?si=N5qyWMqzC_QokSPx] 

### Interview Question:

**Problem**: Implement a stack using two queues.

![Stack Using 2 Queues - InterviewBit](https://www.interviewbit.com/blog/wp-content/uploads/2021/11/Stack-Operations.png align="left")

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## 4\. **Queues**

A **queue** follows the First In, First Out (FIFO) principle. Elements are added at the rear and removed from the front.

**Analogy**: Think of a line at a ticket counter where people are served in the order they arrive.

### Key Operations:

* Enqueue (add): (O(1))
    
* Dequeue (remove): (O(1))
    

### Interview Question:

**Problem**: Design a circular queue.

![DS Circular Queue - javatpoint](https://images.javatpoint.com/ds/images/circular-queue.png align="left")

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## 5\. **Hash Tables**

A **hash table** uses a hash function to map keys to values, allowing for fast data retrieval.

**Analogy**: Imagine a library where each book has a unique code that tells you exactly which shelf it’s on.

### Key Operations:

* Insertion, Deletion, Access: (O(1)) (on average).
    

### Interview Question:

**Problem**: Implement a hash map that supports insert, delete, and get operations in (O(1)).

![Introduction to Hash Tables: A Beginner-Friendly Guide | Day #18](https://cdn.hashnode.com/res/hashnode/image/upload/v1729774855348/8ea3c3c6-0764-4808-b1bd-c3b937498952.png?auto=compress,format&format=webp align="left")

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## 6\. **Binary Trees**

A **binary tree** is a hierarchical structure where each node has at most two children, called the left and right child.

**Analogy**: Picture a family tree where each person has up to two immediate descendants.

### Key Operations:

* Traversal (Inorder, Preorder, Postorder): (O(n))
    

### Interview Question:

**Problem**: Write a function to check if a binary tree is a valid binary search tree (BST).

![Binary Search Tree](https://cdn.programiz.com/sites/tutorial2program/files/bst-vs-not-bst.png align="left")

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## 7\. **Binary Search Trees (BSTs)**

A **binary search tree** is a binary tree where the left child contains values less than the parent, and the right child contains values greater than the parent.

**Analogy**: Think of organizing books on a shelf where smaller books are placed on the left and larger ones on the right.

### Key Operations:

* Search, Insertion, Deletion: (O(h)) (where (h) is the height of the tree).
    

### Interview Question:

**Problem**: Given a BST, find its (k)-th smallest element.

%[https://youtu.be/nOLjdhvfi_A?si=Brd49iUs26pFOYgc] 

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## 8\. **Heaps**

A **heap** is a special tree-based structure where the parent node is always greater than or equal to (max heap) or less than or equal to (min heap) its children.

**Analogy**: Imagine a leaderboard where the highest scorer is always on top.

### Key Operations:

* Insert, Delete: (O(\\log n))
    
* Get Max/Min: (O(1))
    

### Interview Question:

**Problem**: Implement a priority queue using a min heap.

%[https://youtu.be/uMFpDNoYf28?si=2UqwA-y1R2XXDWGN] 

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## 9\. **Graphs**

A **graph** is a collection of nodes (vertices) connected by edges. It can be directed or undirected, weighted or unweighted.

**Analogy**: Think of a city's map where intersections are nodes and roads are edges.

### Key Operations:

* Traversal (DFS, BFS): (O(V + E)), where (V) is vertices and (E) is edges.
    

### Interview Question:

**Problem**: Find the shortest path in an unweighted graph.

%[https://youtu.be/FWxCS-LpwXQ?si=9d63cWyaqECIt_vW] 

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## 10\. **Tries**

A **trie** (pronounced "try") is a tree-like data structure used for efficient retrieval of strings, especially in dictionary-like scenarios.

**Analogy**: Imagine an autocomplete system that narrows suggestions as you type.

### Key Operations:

* Insert, Search: (O(m)), where (m) is the length of the string.
    

### Interview Question:

**Problem**: Implement an autocomplete system using a trie.

![Design Typeahead (Autocomplete) System: A Complete Walkthrough](https://systemdesignschool.io/solutions/3-typeahead/trie-frequency.png align="left")

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## 11\. **Disjoint Sets (Union-Find)**

A **disjoint set** is a data structure that tracks a set of elements partitioned into disjoint subsets. It supports union and find operations.

**Analogy**: Think of different groups of friends where you can check if two people are in the same group.

### Key Operations:

* Union, Find: (O(\\alpha(n))), where (\\alpha) is the inverse Ackermann function.
    

### Interview Question:

**Problem**: Detect a cycle in an undirected graph using the union-find algorithm.

![Detect Cycle in an Undirected Graph using DFS (with code)](https://favtutor.com/resources/images/uploads/Detect_cycle_in_an_undirected_graph.png align="left")

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## 12\. **Segment Trees**

A **segment tree** is a tree used for storing intervals or segments and allows for efficient queries and updates.

**Analogy**: Think of a scoreboard where you can quickly find the highest score in any range.

### Key Operations:

* Query, Update: (O(\\log n))
    

### Interview Question:

**Problem**: Build a segment tree to find the sum of elements in a given range.

![Understanding Range Queries and Updates: Segment Tree, Lazy Propagation and  MO's Algorithm | by Prince Kumar | Nybles | Medium](https://miro.medium.com/v2/resize:fit:1135/1*Y6EDFt8zh5Ss30ckzdJyIQ.png align="left")

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### Conclusion

Mastering these 12 data structures will not only strengthen your problem-solving skills but also prepare you for various scenarios in programming and technical interviews. Each data structure has its unique use cases and trade-offs, making it essential to understand when and where to apply them.