Your Ultimate Cheat Sheet for Data Structures and Algorithms on LeetCode in 2025.
Introduction
Whether you're a college student prepping for a technical interview, a recent grad gearing up for your first job, or an experienced dev polishing up your skills, mastering data structures and algorithms is a must. Platforms like LeetCode have become essential for practicing coding problems and acing tech interviews, where understanding these concepts can make or break your success.In this cheat sheet, we’ll break down the essential data structures, algorithms, and problem-solving strategies you need to tackle LeetCode with confidence. From Big-O notation to tree traversals, we've got you covered!
Why Data Structures and Algorithms Matter
Data structures and algorithms are the backbone of efficient programming. They allow you to solve problems optimally, manage data effectively, and ultimately write code that scales. Corporations such as Google, Facebook, Amazon, and Microsoft highlight a candidates' DSA (Data Structures and Algorithms) background during interviews because DSA highlights a candidates' problem solving approach and their ability to manage complexity.Key Concepts to Master
Let’s dive into the core topics you’ll encounter on LeetCode. All of these data structures and algorithms have common forms, and by learning these you will be able to solve many problems.1. Big-O Notation: The Basics of Complexity Analysis
Big-O notation describes the efficiency of algorithms in terms of time and space complexity. In coding interviews, you’ll be expected to analyze your code’s performance and explain why it’s efficient (or not).
- O(1) – Constant time: The best time complexity; this algorithm does not depend on size of the input.
- O(log N) – Logarithmic time: Algorithms that partition the solution space in half at each iteration (e.g., binary search).
- O(N) – Linear time: Algorithms that iterate through each element once.
Common in sorting algorithms like Merge Sort.
- O(N^2) – Quadratic time: Nested loops; common in brute-force solutions.
- O(2^N) – Exponential time: Common in recursive solutions that explore all possibilities, like the subset problem.
Helpful resource: Big-O Cheatsheet
2. Arrays and Strings
They are the simplest data structures, but they are the basis of very complicated problems.- Array Basics: Reading, writing, and deleting elements with O(1)/O(N) time complexity.
- Common Patterns: Sliding window, two pointers, sorting.
- Common Problems on LeetCode:
Two Sum - Calculate pairs with hash maps in O(N) time.
Sliding Window Maximum - Learn how to extract maximum values from fixed-size subarrays.
Tips for Array/ String Problems:
- Use hash maps for quick lookups.
- For subarray problems, consider the sliding window technique.
Example LeetCode Problems: Two Sum, Longest Substring Without Repeating Characters
3. Linked Lists
Linked lists are a linked arrangement of nodes, each containing a value and a pointer to the next node.- Types of Linked Lists: Singly linked, doubly linked, circular linked.
- Common Operations: Traversing, reversing, merging.
- Common Problems:
Reverse Linked List - Implement a linked list reverse using pointers in O(N) time complexity.
Concatenate Two Sorted Lists - Learn how to concatenate in a sorted manner.
Tips for Linked Lists:
Persistently record your previous and current nodes.
- Use slow and fast pointers to detect cycles.
Example LeetCode Problems: Reverse Linked List, Merge Two Sorted Lists
4. Stacks and Queues
Stacks and queues are also fundamental for problem such as order of operations and last-in, first-out (LIFO) or first-in-first-out (FIFO) nature.
- Stack Uses: Backtracking, evaluating expressions, tracking recursive calls.
- Queue Uses: BFS traversal, task scheduling.
- Common Patterns: Using stacks for balancing parentheses, using queues for level-order tree traversal.
Common Problems:
Correct Parentheses - Stack-based validation of bracket matching.
Queue implementation using stacks - Get an idea how to simulate a queue on stack.
Example LeetCode Problems: Valid Parentheses, Implement Queue using Stacks
5. Trees and Graphs
Trees and graphs are the foundation from which a variety of high-level data structures are built and are used extensively in the world through networking, databases, and pathfinding, etc.
Trees
- Binary Trees: Each node has at most two children.
- Binary Search Trees (BSTs): Ordered binary trees where left < root < right.
- Traversal Techniques:
- In-order, pre-order, post-order (DFS)
- Level-order (BFS)
- Common Problems:
Lowest Common Ancestor - Explore trees to identify common ancestors.
Binary Tree Level Order Traversal (L O T) - BFS to traverse nodes level by level.
Example LeetCode Problems: Lowest Common Ancestor, Binary Tree Level Order Traversal
Graphs
Graphs are collections of nodes (vertices) and edges.
- Types of Graphs: Directed, undirected, weighted, unweighted.
- Traversal Techniques:
- BFS (Breadth-First Search)
- DFS (Depth-First Search)
- Common Problems:
Directed Cycle Detection - Apply DFS to check for cycles in a directed graph.
- Shortest Path - Dijkstra’s or Bellman-Ford algorithms.
Directed Cycle Detection - Apply DFS to check for cycles in a directed graph.
- Shortest Path - Dijkstra’s or Bellman-Ford algorithms.
Tips for Graphs:
Adjacency lists for sparse graphs and adjacency matrices for dense graphs.
BFS is useful for computing shortest path in nonweighted graphs.
Example LeetCode Problems: Graph Valid Tree, Number of Islands.
6. Heaps and Priority Queues
Heaps are a specific type of tree structure that always stores the smallest or the largest item in the root. They are employed as priority queues where elements are processed according to their priority.
- Common Use Cases: Dijkstra’s algorithm, finding the Kth largest/smallest element.
- Common Problems:
Find the Kth Largest Number in an Array - Use a min-heap to store the largest number.
Concatenate K Sorted Arrays - Implement the functionality of a heap efficiently merging sorted arrays.
Example LeetCode Problems: Kth Largest Element in an Array, Merge K Sorted Lists.
7. Dynamic Programming (DP)
Dynamic programming is a useful method employed for optimization problems, i.e., you can decompose a problem into overlapping subproblems.
- Common Patterns: Memoization, tabulation, recursion.
- Common Problems:
The Fibonacci Sequence - A classic example of dynamic programming, where the accumulated solution helps generate the final answer.
Knapsack Problem - selection of items with respect to a weight constraint.
Tips for DP Problems:
- Start by solving the problem recursively.
- Look for subproblems and try storing results to avoid redundant calculations.
Example LeetCode Problems: Climbing Stairs, House Robber
8. Bit Manipulation
Bit manipulation can be a lifesaver in problems involving binary operations, where you need to manipulate individual bits for efficiency.
- Common Operations: AND, OR, XOR, left shift, right shift.
- Common Problems:
Single Value - Perform bitwise XOR to determine a unique n-ary number from a pair.
Bit Counting - Count occurrences of the bit set in a binary format.
Example LeetCode Problems: Single Number, Counting Bits
9. Tips for LeetCode Success
1. Practice Patterns: Focus on solving problems by pattern, like “two-pointer problems” or “DFS problems. This will help you see connections across different questions.
2. Optimize Incrementally: Start with a brute-force solution, then work to improve it. Sometimes brute force can help you understand the problem.
3. Explain Your Solution: Pretend you’re explaining your approach to a friend. This helps clarify your thinking and identify areas that need work.
LeetCode’s discussion section is a goldmine. If you’re stuck, see how others approach the problem, but make sure you understand the solution instead of just copying it.
Conclusion
Data structures and algorithm mastery is the result of time, patience, and lots of practice. With a solid understanding of Big-O,
arrays, linked lists, trees, graphs, and other fundamental structures, you’ll be well-prepared to tackle anything LeetCode or a technical interview can throw at you.
