Neetcode 150 Notout Questions with Solutions

January 2, 2025

Neetcode150Questions

Top 150 Neetcode Liked Questions

Are you preparing for coding interviews or looking to enhance your problem-solving skills? The Top 150 Neetcode Liked Questions list is your go-to resource. These curated problems are loved by developers worldwide and cover a wide range of topics to help you build a strong foundation for technical interviews. Let’s dive into everything you need to know about these questions and how they can accelerate your coding journey.

We will be covering the following things in this course:

  1. Arrays And Hashing
  2. Two Pointers
  3. Sliding Window
  4. Stack
  5. Binary Search
  6. Linked List
  7. Trees
  8. And Many More. 

Neetcode’s Top 150 Liked Questions are handpicked coding challenges widely recognized for their relevance in technical interviews and real-world problem-solving

Arrays And Hashing

  • Arrays in DSA: Arrays are fundamental data structures that provide indexed storage and are crucial for solving problems involving contiguous memory or linear data traversal.
  • Hashing in DSA: Hashing enables efficient data retrieval and storage by mapping keys to values, making it essential for problems like duplicate detection and frequency counting.
Two Pointers In Neetcode

Two Pointers

Two Pointers in DSA: The two-pointer technique involves using two indices to traverse an array or string simultaneously.

  • It is highly effective for solving problems related to sorting, searching, or finding pairs, such as merge operations, finding target sums, and palindromic checks. This approach optimizes time complexity by reducing nested iterations.

Sliding Window

Sliding Window in DSA: The sliding window technique is a powerful method for solving problems involving contiguous subarrays or substrings.

  • It efficiently handles tasks like finding maximum sums, unique characters, or fixed-length subarrays by maintaining a dynamic window of elements, reducing the need for nested loops and improving time complexity.
Sliding Windows In Neetcode

Stack

Stacks in DSA: Stacks are linear data structures that follow the LIFO (Last In, First Out) principle. They are essential for solving problems like balanced parentheses, evaluating expressions, and backtracking.

  • With their ability to handle recursive processes and maintain state, stacks simplify tasks like reversing data or tracking previous operations.

Binary Search

Binary Search in DSA: Binary Search is an efficient algorithm that divides the search space in half at every step, working on sorted data.

  • It is widely used for solving problems like finding target elements, search bounds, and peak elements, with a time complexity of O(log n). This method ensures optimal performance in large datasets.
BinarySearchInNeetcode

Linked List

Linked List in DSA: Linked Lists are dynamic data structures consisting of nodes where each node contains data and a reference to the next node.

  • They are ideal for solving problems involving dynamic memory allocation, insertion, and deletion operations, such as reversing a list, detecting cycles, and merging sorted lists. Their flexibility makes them a key tool in DSA.

Trees

Trees in DSA: Trees are hierarchical data structures that consist of nodes connected by edges, with applications in search operations, decision-making, and hierarchical modeling.

  • They are fundamental for solving problems such as binary search trees (BST), binary trees, and graph traversal (DFS and BFS). Trees help in efficient data retrieval, organizing data, and maintaining relationships.
Backtracking In Neetcode

Math & Geometery

Maths and Geometry in DSA: Mathematics and geometry play a crucial role in solving algorithmic problems involving shapes, distances, and angles.

  • Understanding these principles helps optimize solutions for spatial and numerical challenges in DSA.

Backtracking

Backtracking in DSA: Backtracking is a problem-solving technique that builds solutions incrementally and abandons paths that lead to dead ends.

  • It is particularly useful in problems like N-Queens, Sudoku Solver, and combination/permutation generation. By exploring all possible options and retracting when necessary, backtracking ensures all valid solutions are found while minimizing redundant work.
Graphs In Neetcode

Graphs

Graphs in DSA: Graphs are powerful data structures used to model relationships between entities, with nodes (vertices) and edges.

  • They are essential in solving problems like shortest path finding, network flow, and cycle detection. Graph algorithms like DFS, BFS, and Dijkstra’s algorithm are fundamental for tasks in social networks, routing, and web crawling.

Priority Queues & Heaps

Priority Queue and Heap in DSA: A Priority Queue is a special type of queue where each element is associated with a priority, and elements are served based on priority rather than insertion order.

  • Implemented using a Heap, a Heap is a binary tree-based data structure that ensures the highest or lowest priority element is always at the top. They are crucial in solving problems like task scheduling, Dijkstra’s algorithm, and Huffman encoding.

Tries

Tries in DSA: Tries are tree-like data structures used to efficiently store and search strings, making them ideal for problems like word search, autocomplete, and prefix matching.

By storing characters as nodes, tries optimize search and insertion operations to O(m) time complexity, where m is the length of the string. Tries are especially useful in applications requiring fast prefix queries.

Advance Graph

Advanced Graphs in DSA: Advanced graph algorithms extend basic graph theory to solve more complex problems, such as network flow, strongly connected components, and minimum spanning trees.

Techniques like Floyd-Warshall, Kruskal’s, and Bellman-Ford are crucial for dealing with weighted graphs, negative cycles, and multi-source shortest path problems. These advanced methods are essential in fields like transportation networks, internet routing, and resource allocation.

Advance graph in Neetcode
1-d dynamic Programming

1-D Dynamic Programming

1-D Dynamic Programming in DSA: 1-D Dynamic Programming (DP) involves solving problems by breaking them down into smaller overlapping subproblems and storing their solutions to avoid redundant calculations.

It is commonly used in problems like Fibonacci series, knapsack, and minimum cost path, where the solution depends on the previous state. This approach reduces time complexity from exponential to polynomial, making it efficient for large input sizes.

2-D Dynamic Programming

2-D Dynamic Programming in DSA: 2-D Dynamic Programming is used to solve problems that involve a grid or matrix, where the solution depends on multiple subproblems with overlapping subproblems and optimal substructure.

  • It is commonly applied in problems like longest common subsequence, edit distance, and knapsack problem.
2-D Dynamic Programming
Greedy in Needcode

Greedy

A trie is a tree structure where each node represents a character, and paths from the root to a node form a string. The root node usually represents an empty string.

  • Every node in a trie can have multiple child nodes, each corresponding to a different character.

Intervals

Intervals in DSA: Intervals are used to represent ranges or continuous segments of data, commonly encountered in problems like merging overlapping intervals, interval scheduling, and range queries.

  • Efficient algorithms like sorting and two-pointer techniques help in handling and manipulating intervals, ensuring optimal solutions for tasks such as finding gaps or interval intersection.
Intervals in Neetcode

Bit Manipulation

Bit Manipulation in DSA: Bit manipulation involves directly working with the individual bits of integers, making it a powerful technique for optimizing algorithms.

  • It is commonly used in problems like finding unique elements, counting set bits, and flipping bits. With operations like AND, OR, XOR, and shift, bit manipulation offers efficient solutions to problems with constant-time complexity.