Coding Patterns

This comprehensive guide to coding patterns that will help you ace your technical interviews at top tech companies. This section covers the most frequently asked algorithmic patterns in coding interviews, with detailed explanations, examples, and practice problems.

Why Learn Coding Patterns?

Coding patterns are reusable solutions to common algorithmic problems. Understanding these patterns will help you:

• Solve problems faster - Recognize familiar problem structures
• Write cleaner code - Use proven, efficient approaches
• Communicate better - Explain your thought process clearly
• Ace interviews - Handle 70%+ of coding interview problems

Pattern Categories

Data Structure Foundations

• Array Manipulation - Core operations, sorting, and manipulation techniques
• Linked List - Pointer-based data structures and traversals
• Stack - Last-in-First-out data structure operations
• Queue - First-in-First-out data structure operations
• Heap/Priority Queue - Priority-based data structures
• Trie - Prefix tree data structures for strings
• Hash Table - Hashing techniques for fast lookups
• Matrix - Two-dimensional array operations and traversals

Array & String Algorithms

• Two Pointers - Efficient technique for searching and manipulating arrays with two moving pointers
• Sliding Window - Dynamic window technique for subarray and substring problems
• Binary Search - Divide and conquer approach for sorted array problems
• String Manipulation - String processing, pattern matching, and transformations
• Kadane Algorithm - Maximum subarray sum problems
• Prefix Sum - Cumulative sum techniques for range queries
• Monotonic Stack - Stack-based solutions maintaining monotonic properties

Graph & Tree Algorithms

• Graph Traversal - BFS and DFS for graph exploration
• Tree Traversal - In-order, pre-order, and post-order traversals
• Topological Sort - Ordering vertices based on dependencies
• Shortest Path - Finding optimal paths in graphs
• Union Find - Disjoint set union operations
• Cycle Detection - Detecting cycles in graphs and trees

Dynamic Programming

• Dynamic Programming - Breaking down complex problems into simpler subproblems
• Knapsack Problems - Optimization problems with constraints

Advanced Algorithms

• Bit Manipulation - Bitwise operations and optimizations
• Greedy Algorithms - Making locally optimal choices
• Backtracking - Systematic search through solution spaces
• Divide and Conquer - Breaking problems into subproblems
• Recursion - Recursive problem-solving techniques
• Interval Scheduling - Scheduling problems with intervals
• Math Algorithms - Mathematical computations and algorithms

Learning Roadmap

Follow this structured approach to master coding patterns:

1. Foundation - Arrays, Strings, Two Pointers
2. Search & Sort - Binary Search, Sliding Window
3. Dynamic Programming - Memoization, Tabulation
4. Graphs & Trees - Traversal, Shortest Paths
5. Advanced Topics - Backtracking, Greedy, System Design

Practice Strategy

• Start with Easy - Build confidence with simpler problems
• Focus on Patterns - Don’t memorize solutions, understand approaches
• Time Yourself - Practice under interview conditions
• Review Solutions - Learn from optimal solutions
• Track Progress - Mark patterns as mastered

Getting Started

Begin with the Two Pointers, Sliding Window, or Binary Search patterns - fundamental techniques frequently asked in interviews. Each pattern includes:

• 📖 Detailed Guide - Step-by-step explanations
• 💻 Code Templates - Reusable implementations
• 🎯 Practice Problems - Curated LeetCode problems
• 📊 Complexity Analysis - Time and space considerations

Ready to start your journey? Let’s dive into Two Pointers, Sliding Window, or Binary Search!