LEARNING_PATH.md

The Definitive Learning Path

To master Data Structures and Algorithms, you cannot just solve random problems. You must follow a structured, tiered approach that shifts your focus from memorizing solutions to recognizing patterns.

This learning path organizes the patterns in the repository into four distinct tiers.

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🟢 Tier 1: Foundation (Weeks 1-2)

Goal: Build confidence, master syntax, and become fluent with basic data traversals.

Before tackling complex optimizations, you must be able to iterate through an array or string backwards and forwards, use a Hash Map implicitly, and understand basic pointer arithmetic.

• Patterns to Study:

  • Arrays & Hashing
  • Two Pointers
  • Binary Search
  • Linked List

• Key Milestones:

  • Can you implement Two Sum using a Hash Map from memory?
  • Can you traverse a Linked List without causing a Null Reference Exception?
  • Can you write a standard Binary Search template without off-by-one errors?

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🟡 Tier 2: Core Patterns (Weeks 3-6)

Goal: Master the 80% of patterns used in most technical interviews.

This is where true pattern recognition begins. You will learn to identify when a problem requires expanding/contracting a window, or exploring a graph level-by-level.

• Patterns to Study:

  • Sliding Window
  • Trees & Trie
  • Graphs (BFS & DFS)
  • Backtracking
  • Stack

• Key Milestones:

  • Can you identify if a Sliding Window should have a fixed or variable length?
  • Can you implement BFS using a Queue and track visited nodes?
  • Can you write a basic recursive function for exploring a Tree?

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🔴 Tier 3: Complex Optimization (Weeks 7-9)

Goal: Develop the ability to handle hard, optimization-focused problems usually asked at FAANG/MAANG companies.

These problems require combining multiple patterns or diving deep into specialized algorithms.

• Patterns to Study:

  • Heap, Merge, & Stacks
  • 1-D DP & 2-D DP
  • Advanced Graph Algorithms (Dijkstra, Kruskal)
  • Bit Manipulation

• Key Milestones:

  • Can you define a state and transition for a 1-D DP problem?
  • Can you use a Min-Heap to find the Kth Largest element in a stream?
  • Can you implement Dijkstra's Algorithm for weighted shortest paths?

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🏆 Tier 4: Interview Simulation (Ongoing)

Goal: Bridge the gap from "can solve" to "can solve under pressure."

Once you've studied the patterns, you must simulate the interview environment. Time yourself, speak your thoughts aloud, and practice against company-specific question banks.

• Actions Required:
  • Visit the interview_prep/ folder (coming soon).
  • Complete the 45-minute timed mock interview sets.
  • Review problems tagged with target companies (e.g., Google, Meta).
  • Focus on the Brute Force -> Optimized journey, avoiding premature optimization.