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The Ninety DSA Patterns That Cover Almost All Coding Interviews
Many candidates solve 200+ LeetCode challenges but still blank out during actual technical rounds.
Most companies reuse recurring data structure and algorithm (DSA) templates to evaluate problem-solving skills.
Big tech interviews at companies like Google, Amazon, and Microsoft revolve around consistent logic frameworks.
Understanding these 90 DSA blueprints equips you to decode almost any interview challenge with ease.
What You’ll Learn
Inside this guide, we organize 90 DSA templates into 15 essential families used by elite programmers.
You’ll be guided on how to practice these patterns dynamically through AI-driven hints and feedback on Thita.ai.
Why Random LeetCode Grinding Doesn’t Work
Solving problems at random doesn’t build intuition for recurring logic patterns.
Each DSA pattern functions as a reusable design you can apply to multiple situations.
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.
Those who excel identify the pattern first and adapt instantly.
The 15 Core DSA Pattern Families
Each category groups related concepts that repeatedly surface in coding interviews.
1. Two Pointer Patterns (7 Patterns)
Applied in problems where two indices move strategically across data structures.
Includes logic for in-place edits, fixed gaps, and center-based expansion techniques.
? Tip: Sorted inputs often signal a two-pointer approach.
2. Sliding Window Patterns (4 Patterns)
Best for problems requiring flexible range adjustments.
Key Patterns: Fixed-size window, Variable-size window, Monotonic queue, Character frequency matching.
? Pro Tip: Learn when to expand or contract your window efficiently.
3. Tree Traversal Patterns (7 Patterns)
Use Case: Binary and N-ary tree exploration including BFS, DFS, LCA, and AI interviews serialization.
4. Graph Traversal Patterns (8 Patterns)
Focuses on efficient exploration and connection validation in networks.
5. Dynamic Programming Patterns (11 Patterns)
Central to solving resource allocation and sequence-based problems efficiently.
6. Heap (Priority Queue) Patterns (4 Patterns)
Helps in scheduling and optimization tasks.
7. Backtracking Patterns (7 Patterns)
Powerful for constraint satisfaction and combinatorial logic.
8. Greedy Patterns (6 Patterns)
Relies on sorted order or prioritization strategies.
9. Binary Search Patterns (5 Patterns)
Used in range partitioning and target detection.
10. Stack Patterns (6 Patterns)
Involves handling nested structures and validation problems.
11. Bit Manipulation Patterns (5 Patterns)
Use Case: XOR-based logic, bit counting, and power checks.
12. Linked List Patterns (5 Patterns)
Use Case: Efficient pointer-based data manipulation.
13. Array & Matrix Patterns (8 Patterns)
Focuses on efficient iteration over structured grids.
14. String Manipulation Patterns (7 Patterns)
Includes palindrome checking, encoding/decoding, and pattern validation.
15. Design Patterns (Meta Category)
Includes LRU Cache, LFU Cache, Min Stack, Trie, and Design Twitter.
How to Practice Effectively on Thita.ai
Learning the 90 DSA patterns is only the beginning — mastering their application is the key.
Begin by opening the full Thita.ai DSA pattern catalog.
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.
Step 4: Track Progress ? Analyze performance and identify weak zones.
The Smart Way to Prepare
Success in coding interviews is built on pattern familiarity, not repetition.
Use Thita.ai’s roadmap to learn, practice, and refine through intelligent feedback.
Why Choose Thita.ai?
Thita.ai helps you achieve interview mastery by offering:
– Comprehensive 90 DSA pattern training
– Real-time AI insights
– Mock interview simulations
– Tailored progress analytics
– Structured growth tracking.