The Ninety DSA Patterns That Cover 99% Coding Interviews
You might have solved over 200 LeetCode questions, yet your confidence drops the moment the interview starts.
The truth is, interviewers rarely invent new problems; they adapt known logical structures.
These organizations rely on pattern-based questions to assess how fast you adapt familiar logic to new contexts.
By learning 90 carefully chosen DSA patterns, you’ll unlock solutions to 99% of interview problems instantly.
What You’ll Learn
You’ll explore 15 foundational categories containing 90 powerful coding patterns.
You’ll also discover how to practice these patterns interactively with AI feedback using Thita.ai.
Why Random LeetCode Grinding Doesn’t Work
Without pattern-based learning, random LeetCode practice fails to build adaptability.
Once recognized, a pattern turns complex problems into familiar exercises.
Example mappings include:
– Sorted Array + Target Sum ? Two Pointers (Converging)
– Longest Substring Without Repeats ? Sliding Window (Variable Size)
– Cycle in Linked List ? Fast & Slow Pointers.
Success in interviews comes from recognizing underlying DSA themes, not recalling exact problems.
The 15 Core DSA Pattern Families
These pattern families cover the foundational structures behind most coding interview challenges.
1. Two Pointer Patterns (7 Patterns)
Applied in problems where two indices move strategically across data structures.
Key Patterns: Converging pointers, Fast & Slow pointers, Fixed separation, In-place modification, Expand from center, String reversal, and Backspace comparison.
? Pro Tip: Check if the data is sorted or relationships exist between index pairs.
2. Sliding Window Patterns (4 Patterns)
Used to handle range-based optimizations in arrays and strings.
Examples include fixed or variable windows, character tracking, and monotonic operations.
? Hint: Balance expansion and contraction logic to optimize results.
3. Tree Traversal Patterns (7 Patterns)
Used for recursive and iterative traversals across hierarchical structures.
4. Graph Traversal Patterns (8 Patterns)
Applied in DFS, BFS, shortest paths, and union-find logic.
5. Dynamic Programming Patterns (11 Patterns)
Covers problems like Knapsack, LIS, Edit Distance, and Interval DP.
6. Heap (Priority Queue) Patterns (4 Patterns)
Ideal for top-K computations and real-time priority adjustments.
7. Backtracking Patterns (7 Patterns)
Includes subsets, permutations, N-Queens, Sudoku, and combination problems.
8. Greedy Patterns (6 Patterns)
Use Case: Achieving global optima through local choices.
9. Binary Search Patterns (5 Patterns)
Use Case: Efficient searching over sorted data or answer ranges.
10. Stack Patterns (6 Patterns)
Enables structured data management through stack logic.
11. Bit Manipulation Patterns (5 Patterns)
Crucial for low-level data operations.
12. Linked List Patterns (5 Patterns)
Focuses on optimizing node traversal and transformation.
13. Array & Matrix Patterns (8 Patterns)
Applied in image processing, pathfinding, and transformation tasks.
14. String Manipulation Patterns (7 Patterns)
Used for matching, substring searches, and string reconstruction.
15. Design Patterns (Meta Category)
Use Case: Data structure and system design logic.
How to Practice Effectively on Thita.ai
The real edge lies in applying these patterns effectively through guided AI coaching.
Access the DSA 90 framework sheet to visualize all pattern families.
Choose one category (e.g., Sliding Window) to practice related LeetCode-style problems.
Engage Thita.ai’s AI tutor for instant suggestions and solution breakdowns.
Get personalized progress tracking and adaptive recommendations.
The Smart Way to Prepare
Stop random practice; focus on mastering logic templates instead.
Use Mock interviews Thita.ai’s roadmap to learn, practice, and refine through intelligent feedback.
Why Choose Thita.ai?
Thita.ai empowers learners to:
– Master 90 reusable DSA patterns
– Practice interactively with AI feedback
– Experience realistic mock interviews
– Prepare for FAANG and top-tier interviews
– Build a personalized, AI-guided learning path.