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Coding Interview Preparation - Incremental Learning Guide

Welcome to structured, incremental learning for algorithms and data structures. This documentation is organized into 7 phases that build upon each other.

🎯 Learning Philosophy

Master fundamentals first, then build advanced concepts.

Each phase is self-contained yet builds on previous knowledge. Digestible chunks mean you understand deeply before moving forward.

📚 The 7 Learning Phases

Phase 1: Fundamentals (Week 1)

Goal: Understand complexity analysis and basic concepts

  • Big O notation
  • Time & Space complexity
  • Basic data structure operations

Key Question: "Why do we care about efficiency?"

Phase 2: Core Data Structures (Weeks 2-3)

Goal: Master the 4 essential structures

  1. Arrays - Foundation of all structures
  2. Linked Lists - Dynamic allocation and pointers
  3. Stacks & Queues - LIFO/FIFO patterns
  4. Hash Tables - O(1) lookup magic

Key Question: "Which structure solves this problem best?"

Phase 3: Trees (Foundation) (Weeks 4-5)

Goal: Understand hierarchical data organization

  1. Binary Search Trees - Foundation of tree concepts
  2. Heaps - Priority queue implementation
  3. Tries & TSTs - String-based structures

Key Question: "How do trees improve on linear structures?"

Phase 4: Searching & Sorting (Weeks 6-7)

Goal: Master the fundamental algorithms

  1. Sorting:
  2. Simple: Bubble, Insertion
  3. Efficient: Merge, Quick
  4. String Searching:
  5. Boyer-Moore
  6. Rabin-Karp

Key Question: "How fast can we sort/search?"

Phase 5: Advanced Trees (Weeks 8-9)

Goal: Understand self-balancing and specialized trees

  1. AVL Trees - Height-balanced
  2. Red-Black Trees - Color-based balancing
  3. Splay Trees - Self-optimizing
  4. B-Trees - Disk-friendly

Key Question: "How do we guarantee O(log n) performance?"

Phase 6: Graph Basics (Weeks 10-11)

Goal: Understand graph fundamentals

  1. Graph Representations - Adjacency list/matrix
  2. Traversal:
  3. BFS (Breadth-First)
  4. DFS (Depth-First)
  5. Basic Properties - Connected components, cycles

Key Question: "How do we explore networks?"

Phase 7: Advanced Algorithms (Coming Soon)

Goal: Shortest paths, MST, DP, and more

Will be added once Phase 1-6 mastery is achieved.

  • Graph algorithms (Dijkstra, Bellman-Ford)
  • Minimum Spanning Trees
  • Dynamic Programming
  • Advanced techniques

🚀 How to Use This Documentation

Start Here

  1. Pick Phase 1
  2. Read through each topic
  3. Understand the "Key Question"
  4. Move to next phase only when confident

Search Tips

  • Use search (🔍 top-right) to find specific topics
  • Each topic has real slide content extracted from lessons
  • Code examples are highlighted

Progression

Phase 1 → Phase 2 → Phase 3 → Phase 4 → Phase 5 → Phase 6 → Phase 7 (1 wk) (2 wks) (2 wks) (2 wks) (2 wks) (2 wks) (TBD) └─────────────── ~13 weeks to mastery ──────────────┘

💡 Key Principles

✅ Incremental - Small, digestible pieces
✅ Sequential - Each phase builds on the last
✅ Deep - Master fundamentals before advanced
✅ Practical - Real code and examples
✅ Complete - Full slide content extracted

📊 What's Included

  • 283 PDF practice problems with solutions (Phase 2+)
  • Real PowerPoint slide content for every topic
  • Code examples and complexity analysis
  • Interview tips and patterns

🎓 Expected Timeline

Phase Duration Concepts Status
1 1 week Fundamentals ✅ Ready
2 2 weeks Core DS ✅ Ready
3 2 weeks Trees ✅ Ready
4 2 weeks Sorting/Search ✅ Ready
5 2 weeks Adv. Trees ✅ Ready
6 2 weeks Graphs ✅ Ready
7 TBD Adv. Algos 📋 Coming

Total: ~13 weeks to interview-ready mastery

🤔 How to Approach Each Phase

  1. Read the overview
  2. Understand the key question
  3. Study each topic (slides contain detailed content)
  4. Code - implement from the src/ directory
  5. Practice - solve problems in Phase 7 once ready

✨ Remember

"The goal is not to know everything about algorithms. The goal is to understand fundamental concepts so deeply that you can solve any problem."

Start with Phase 1. Take your time. Master each concept before moving forward.

Let's build your interviewing skills! 🚀

All content extracted from comprehensive PowerPoint slides. Last updated: April 27, 2026