In the world of software engineering interviews, one phrase dominates the preparation process: DSA. Short for Data Structures and Algorithms, DSA is the backbone of most coding interviews, especially at top-tier companies like Google, Amazon, Meta, and Microsoft. If you’re serious about landing a job in tech, having a rock-solid grasp of DSA is non-negotiable.
But let’s be honest—studying DSA can feel overwhelming. There are so many topics, so many problems, and so little time. That’s where this cheat sheet comes in. Whether you’re preparing for your first interview or brushing up before your next big tech round, this quick reference guide will give you everything you need to remember and master the most important DSA concepts.
Why You Need a DSA Cheat Sheet
DSA isn’t just academic. Interviewers use it to evaluate how well you solve problems, understand computational trade-offs, and write efficient code. A cheat sheet acts like your battle-tested arsenal, giving you just the essentials without drowning in details.
Think of this guide as your go-to playbook. Bookmark it. Revisit it. Make it your daily prep companion.
Big-O Complexity: Know Before You Code
Before jumping into data structures or algorithms, internalize the time and space complexity of operations.
| Operation Type | Array | Linked List | Stack/Queue | Hash Table | BST (Balanced) |
|---|---|---|---|---|---|
| Access | O(1) | O(n) | O(n) | O(1) | O(log n) |
| Search | O(n) | O(n) | O(n) | O(1) | O(log n) |
| Insert | O(n) | O(1) | O(1) | O(1) | O(log n) |
| Delete | O(n) | O(1) | O(1) | O(1) | O(log n) |
Pro Tip: Practice recognizing when trade-offs (e.g., faster insert vs. slower search) are worth it.
Must-Know Data Structures
1. Arrays
- Fixed size, contiguous memory
- Random access in O(1)
- Best for static datasets
- Important topics: Sliding window, two pointers, prefix sum
2. Linked Lists
- Dynamic memory allocation
- Ideal for insert/delete operations
- Types: singly, doubly, circular
- Common interview patterns: reverse list, detect cycle, merge sorted lists
3. Stacks
- LIFO (Last In First Out)
- Used for: recursion simulation, expression parsing, undo mechanisms
- Problems: valid parentheses, min stack, evaluate reverse Polish notation
4. Queues
- FIFO (First In First Out)
- Variants: circular queue, deque, priority queue
- Applications: task scheduling, BFS
5. Hash Tables / Hash Maps
- Key-value pairs
- O(1) average lookup and insert
- Watch out for collisions and load factor
- Use cases: frequency counting, anagram grouping, LRU cache
6. Trees
- Hierarchical structure
- Binary Tree, BST, AVL, Segment Tree, Trie
- Must-practice problems: lowest common ancestor, level-order traversal, serialization/deserialization
7. Heaps (Priority Queues)
- Complete binary trees
- Max-heap or Min-heap
- Great for top-k problems, Dijkstra’s algorithm, median in stream
8. Graphs
- Represented using adjacency list/matrix
- Directed vs. undirected, cyclic vs. acyclic
- Traversal: DFS, BFS
- Critical concepts: Dijkstra, Bellman-Ford, Floyd-Warshall, topological sort, union-find
Must-Know Algorithms
1. Sorting Algorithms
- Quick Sort – O(n log n) average, in-place
- Merge Sort – O(n log n), stable
- Heap Sort – O(n log n), in-place
- Counting Sort / Radix Sort – Linear time for bounded integers
2. Searching Algorithms
- Binary Search – O(log n), only on sorted data
- Variants: first occurrence, last occurrence, search in rotated array
3. Recursion and Backtracking
- Foundation for DFS, permutations, combinations
- Classic problems: N-Queens, Sudoku Solver, Subset Sum
4. Dynamic Programming (DP)
- Break problem into overlapping subproblems
- Bottom-up (tabulation) vs. top-down (memoization)
- Must-know patterns:
- 0/1 Knapsack
- Longest Common Subsequence
- Longest Increasing Subsequence
- Matrix path problems
5. Greedy Algorithms
- Make the locally optimal choice
- Works when greedy = optimal globally
- Examples: Activity Selection, Huffman Encoding, Coin Change (greedy variant)
6. Divide and Conquer
- Divide problem -> Solve subproblems -> Combine solutions
- Examples: Merge Sort, Quick Sort, Binary Search
7. Sliding Window Technique
- Ideal for contiguous subarray problems
- Examples: max sum subarray, longest substring without repeating characters
8. Two Pointer Technique
- Efficient for sorted arrays and linked lists
- Problems: pair sum, reverse array in-place, merge two sorted arrays
9. Bit Manipulation
- Use bits for optimization
- XOR tricks, checking even/odd, counting set bits, swapping values
Common Interview Problem Patterns
Interviewers don’t just test knowledge; they test how well you apply patterns.
Pattern 1: Fast and Slow Pointers
- Detect cycle in linked list
- Find middle node
Pattern 2: Merge Intervals
- Insert interval
- Merge overlapping intervals
Pattern 3: Top-K Elements
- Use min-heap or quick select
Pattern 4: Subsets and Permutations
- Backtracking techniques
Pattern 5: Graph Traversals
- BFS for shortest path
- DFS for components or cycle detection
10 Must-Practice DSA Problems Before Your Interview
- Two Sum (Hash Map)
- Reverse Linked List (Linked List Basics)
- Valid Parentheses (Stack)
- Merge Intervals (Greedy / Sorting)
- LRU Cache (HashMap + Doubly Linked List)
- Binary Tree Level Order Traversal (BFS)
- Word Ladder (BFS + Graph)
- Longest Substring Without Repeating Characters (Sliding Window)
- Kth Largest Element in an Array (Heap or Quickselect)
- Edit Distance (Dynamic Programming)
DSA Study Strategy That Works
- Start Simple: Learn basics through visual aids and real-life analogies.
- Solve by Categories: Focus on one data structure at a time.
- Practice Patterns: Recognize reusable logic.
- Do Mock Interviews: Use platforms like Pramp or Interviewing.io.
- Stay Consistent: Even 1-2 problems/day make a difference over time.
Tools and Platforms for DSA Mastery
- LeetCode: Most relevant to interviews
- HackerRank: Great for beginners
- GeeksforGeeks: Detailed explanations
- Codeforces: For competitive programming
- InterviewBit: Curated path for interviews
Final Thoughts
Cracking coding interviews isn’t just about solving problems—it’s about solving them efficiently, consistently, and confidently. This cheat sheet is your shortcut to success. Keep it close, update it with your own notes, and use it regularly.
Remember, DSA mastery isn’t about memorizing solutions. It’s about recognizing patterns, understanding trade-offs, and staying calm under pressure. With this quick reference guide, you’re already ahead of 90% of the competition.
So roll up your sleeves, fire up your coding platform, and let the preparation begin. Your dream job is just one well-structured solution away.
If this cheat sheet helped you, share it with a fellow coder or bookmark it for daily practice. You’ve got this!
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