Author: Ritesh Rana
Welcome to the most comprehensive coding interview preparation repository! This collection contains detailed explanations, working code examples, and interview tips for all major coding interview patterns. Whether you're a college student just starting your coding journey or an experienced professional looking to brush up on algorithms, this repository provides in-depth coverage of essential topics.
19 major algorithmic patterns covering 100+ coding problems from basic to advanced level, ensuring complete interview preparation coverage.
Detailed explanations and examples suitable for:
- Beginners: Clear fundamental explanations with step-by-step examples
- Intermediate: Optimization techniques and advanced use cases
- Advanced: Complex variations and cutting-edge optimizations
All examples are:
- Thoroughly tested with multiple test cases
- Ready to download, run, and modify
- Optimized for both time and space complexity
- Documented with clear comments and explanations
- Real interview questions from top tech companies (FAANG+)
- Strategic insights and tips from experienced interviewers
- Common follow-up questions and variations
- Time management and problem-solving strategies
Each problem includes:
- Brute force approach for understanding
- Optimized solutions with detailed complexity analysis
- Alternative methods and trade-offs discussion
- Space optimization techniques when applicable
Complete implementations in:
- Python: Clean, readable syntax ideal for interviews
- Java: Enterprise-grade implementations with proper OOP
- JavaScript: Modern ES6+ syntax for web developers
Every solution includes:
- Time Complexity: Big O analysis with detailed explanation
- Space Complexity: Memory usage breakdown
- Edge Cases: Comprehensive edge case handling
- Optimization Notes: Further improvement possibilities
- Structured progression from basic to advanced concepts
- Cross-pattern relationships and dependencies
- Prerequisite tracking and skill building
- Progress checkpoints and milestones
- Introduction to Two Pointers
- Pair Sum - Sorted
- Triplet Sum
- Is Palindrome Valid
- Largest Container
- Shift Zeros to the End
- Next Lexicographical Sequence
- Introduction to Hash Maps and Sets
- Pair Sum - Unsorted
- Verify Sudoku Board
- Zero Striping
- Longest Chain of Consecutive Numbers
- Geometric Sequence Triplets
- Introduction to Linked Lists
- Linked List Reversal
- Remove the Kth Last Node From a Linked List
- Linked List Intersection
- LRU Cache
- Palindromic Linked List
- Flatten a Multi-Level Linked List
- Introduction to Fast and Slow Pointers
- Linked List Loop
- Linked List Midpoint
- Happy Number
- Introduction to Sliding Windows
- Substring Anagrams
- Longest Substring With Unique Characters
- Longest Uniform Substring After Replacements
- Introduction to Binary Search
- Find the Insertion Index
- First and Last Occurrences of a Number
- Cutting Wood
- Find the Target in a Rotated Sorted Array
- Find the Median From Two Sorted Arrays
- Matrix Search
- Local Maxima in Array
- Weighted Random Selection
- Introduction to Stacks
- Valid Parenthesis Expression
- Next Largest Number to the Right
- Evaluate Expression
- Repeated Removal of Adjacent Duplicates
- Implement a Queue using Stacks
- Maximums of Sliding Window
- Introduction to Heaps
- K Most Frequent Strings
- Combine Sorted Linked Lists
- Median of an Integer Stream
- Sort a K-Sorted Array
- Introduction to Intervals
- Merge Overlapping Intervals
- Identify All Interval Overlaps
- Largest Overlap of Intervals
- Introduction to Prefix Sums
- Sum Between Range
- K-Sum Subarrays
- Product Array Without Current Element
- Introduction to Trees
- Invert Binary Tree
- Balanced Binary Tree Validation
- Rightmost Nodes of a Binary Tree
- Widest Binary Tree Level
- Binary Search Tree Validation
- Lowest Common Ancestor
- Build Binary Tree From Preorder and Inorder Traversals
- Maximum Sum of a Continuous Path in a Binary Tree
- Binary Tree Symmetry
- Binary Tree Columns
- Kth Smallest Number in a Binary Search Tree
- Serialize and Deserialize a Binary Tree
- Introduction to Tries
- Design a Trie
- Insert and Search Words with Wildcards
- Find All Words on a Board
- Introduction to Graphs
- Graph Deep Copy
- Count Islands
- Matrix Infection
- Bipartite Graph Validation
- Longest Increasing Path
- Shortest Transformation Sequence
- Merging Communities
- Prerequisites
- Shortest Path
- Connect the Dots
- Introduction to Backtracking
- Find All Permutations
- Find All Subsets
- N Queens
- Combinations of a Sum
- Phone Keypad Combinations
- Introduction to Dynamic Programming
- Climbing Stairs
- Minimum Coin Combination
- Matrix Pathways
- Neighborhood Burglary
- Longest Common Subsequence
- Longest Palindrome in a String
- Maximum Subarray Sum
- 0/1 Knapsack
- Largest Square in a Matrix
- Introduction to Greedy Algorithms
- Jump to the End
- Gas Stations
- Candies
- Introduction to Sort and Search
- Sort Linked List
- Sort Array
- Kth Largest Integer
- Dutch National Flag
- Introduction to Bit Manipulation
- Hamming Weights of Integers
- Lonely Integer
- Swap Odd and Even Bits
- Introduction to Math and Geometry
- Spiral Traversal
- Reverse 32-Bit Integer
- Maximum Collinear Points
- The Josephus Problem
- Triangle Numbers
| Pattern | Typical Time | Typical Space | Common Applications |
|---|---|---|---|
| Two Pointers | O(n) | O(1) | Array/String problems |
| Hash Maps | O(n) | O(n) | Fast lookups, counting |
| Linked Lists | O(n) | O(1) | Dynamic data structures |
| Fast/Slow Pointers | O(n) | O(1) | Cycle detection |
| Sliding Window | O(n) | O(k) | Subarray problems |
| Binary Search | O(log n) | O(1) | Sorted array search |
| Stacks | O(n) | O(n) | Expression parsing |
| Heaps | O(n log n) | O(n) | Priority queues |
| Intervals | O(n log n) | O(n) | Scheduling problems |
| Prefix Sums | O(n) | O(n) | Range queries |
| Trees | O(n) | O(h) | Hierarchical data |
| Tries | O(m) | O(ALPHABET × N × M) | String searching |
| Graphs | O(V + E) | O(V) | Relationship problems |
| Backtracking | O(b^d) | O(d) | Combinatorial problems |
| Dynamic Programming | O(n²) or O(n³) | O(n) or O(n²) | Optimization problems |
| Greedy | O(n log n) | O(1) | Local optimization |
| Sort and Search | O(n log n) | O(n) | Ordering problems |
| Bit Manipulation | O(1) to O(n) | O(1) | Low-level operations |
| Math & Geometry | O(n) to O(n²) | O(1) to O(n) | Mathematical problems |
🟢 Beginner-Friendly (Weeks 1-2):
- Two Pointers - Simple array traversal patterns
- Hash Maps and Sets - Basic lookups and counting
- Linked Lists - Fundamental pointer manipulation
- Stacks - LIFO operations and basic applications
🟡 Intermediate Level (Weeks 3-5): 5. Fast and Slow Pointers - Advanced pointer techniques 6. Sliding Window - Optimized subarray problems 7. Binary Search - Efficient searching in sorted data 8. Heaps - Priority-based data structures 9. Intervals - Time-based problem solving 10. Prefix Sums - Cumulative array operations
🟠 Advanced Beginner (Weeks 6-8): 11. Trees - Hierarchical data structure mastery 12. Tries - Specialized string data structures 13. Sort and Search - Algorithm optimization 14. Bit Manipulation - Low-level programming concepts
🔴 Advanced Level (Weeks 9-12): 15. Graphs - Complex relationship modeling 16. Backtracking - Exhaustive solution exploration 17. Dynamic Programming - Complex optimization problems 18. Greedy Algorithms - Strategic decision making 19. Math and Geometry - Mathematical problem solving
Most Common (Asked in 80%+ interviews):
- Array and String manipulation (Two Pointers, Sliding Window)
- Hash Maps for lookups and counting
- Tree traversal and basic operations
- Linked List manipulation
- Basic sorting and searching
Common (Asked in 50-80% interviews):
- Dynamic Programming (easier problems)
- Graphs (BFS/DFS, basic algorithms)
- Heaps for top-K problems
- Binary Search variants
- Stacks for parsing problems
Moderate (Asked in 20-50% interviews):
- Advanced Tree problems
- Backtracking for combinations/permutations
- Interval problems
- Trie-based string problems
- Prefix sums for range queries
Less Common (Asked in 5-20% interviews):
- Advanced Dynamic Programming
- Complex Graph algorithms
- Bit manipulation tricks
- Advanced Math and Geometry
- Greedy algorithm proofs
FAANG Companies:
- Google: Trees, Graphs, Dynamic Programming, Math
- Amazon: Arrays, Strings, Trees, Design (Heaps/Stacks)
- Apple: Trees, Arrays, Dynamic Programming, System Design
- Netflix: Arrays, Strings, Hash Maps, Optimization
- Facebook/Meta: Graphs, Trees, Dynamic Programming, Hash Maps
Other Top Tech:
- Microsoft: Trees, Dynamic Programming, Arrays, Recursion
- Adobe: Arrays, Strings, Trees, Hash Maps
- Salesforce: Arrays, Hash Maps, Trees, Basic Algorithms
- Uber/Lyft: Graphs, Heaps, Hash Maps, Optimization
- Twitter: Hash Maps, Trees, Strings, Scaling Problems
- Start with Fundamentals: Begin with Two Pointers and Hash Maps patterns
- Follow the Learning Path: Progress through patterns in numerical order
- Practice Regularly: Solve at least 2-3 problems daily
- Focus on Understanding: Don't just memorize solutions, understand the patterns
- Target Weak Areas: Use the repository to strengthen specific algorithmic skills
- Time Yourself: Practice problems under interview conditions (45-60 minutes)
- Implement in Multiple Languages: Try solutions in Python, Java, and JavaScript
- Study Optimizations: Focus on advanced techniques and space/time optimizations
- Focus on Hard Problems: Concentrate on Dynamic Programming, Graphs, and Advanced Trees
- Teach Others: Use the explanations to mentor junior developers
- Contribute: Share your optimizations and alternative approaches
- Interview Preparation: Use as a comprehensive review before technical interviews
4-Week Intensive Plan:
- Week 1: Two Pointers, Hash Maps, Linked Lists, Fast & Slow Pointers
- Week 2: Sliding Windows, Binary Search, Stacks, Heaps
- Week 3: Intervals, Prefix Sums, Trees, Tries
- Week 4: Graphs, Backtracking, Dynamic Programming
8-Week Comprehensive Plan:
- Weeks 1-2: Master basic patterns (Two Pointers through Binary Search)
- Weeks 3-4: Learn intermediate patterns (Stacks through Trees)
- Weeks 5-6: Advanced patterns (Tries through Dynamic Programming)
- Weeks 7-8: Complex patterns (Greedy through Math & Geometry) + Review
Prerequisites:
- Basic programming knowledge in at least one language
- Understanding of basic data structures (arrays, strings, basic math)
- Code editor or IDE of your choice
Recommended Tools:
- Python: PyCharm, VS Code, or Jupyter Notebooks
- Java: IntelliJ IDEA, Eclipse, or VS Code with Java extensions
- JavaScript: VS Code, WebStorm, or browser console for quick testing
Testing Your Solutions:
# For Python examples
cd pattern-directory/code/python
python3 problem_name.py
# For Java examples
cd pattern-directory/code/java
javac ProblemName.java
java ProblemName
# For JavaScript examples
cd pattern-directory/code/javascript
node problem_name.jsFoundation Patterns (Start Here):
- Hash Maps and Sets → Used in many other patterns
- Two Pointers → Foundation for many array problems
Building Block Patterns:
- Linked Lists → Required for Trees and Graphs
- Binary Search → Used in many optimization problems
- Stacks → Foundation for tree traversals and expression parsing
Intermediate Patterns:
- Sliding Windows → Builds on Two Pointers
- Trees → Foundation for advanced tree problems and graphs
- Heaps → Used in graph algorithms and optimization
Advanced Patterns:
- Dynamic Programming → Uses concepts from multiple patterns
- Graphs → Combines trees, BFS, DFS, and other techniques
- Backtracking → Advanced recursion building on tree concepts
Track your progress with this checklist:
Basic Level (Can solve Easy problems):
- Two Pointers
- Hash Maps and Sets
- Linked Lists
- Binary Search (basic)
- Stacks (basic)
Intermediate Level (Can solve Medium problems):
- Fast and Slow Pointers
- Sliding Windows
- Binary Search (advanced)
- Heaps
- Trees (basic)
- Intervals
Advanced Level (Can solve Hard problems):
- Trees (advanced)
- Graphs
- Dynamic Programming
- Backtracking
- Advanced patterns (Tries, Greedy, etc.)
Target Solving Times (Per Problem):
- Easy: 15-25 minutes
- Medium: 25-45 minutes
- Hard: 45-75 minutes
Code Quality Checklist:
- Handles edge cases
- Optimal time complexity
- Reasonable space complexity
- Clean, readable code
- Proper variable naming
- Comments for complex logic
This repository is authored and maintained by Ritesh Rana. Contributions are welcome!
- Report Issues: Found a bug or typo? Open an issue
- Suggest Improvements: Have ideas for better explanations or optimizations?
- Add Test Cases: Help expand the test coverage
- Language Support: Add implementations in additional programming languages
- Alternative Solutions: Share different approaches to existing problems
- Follow the existing code style and documentation format
- Add comprehensive comments and explanations
- Include time and space complexity analysis
- Add test cases for new implementations
- Update README files when adding new content
- Review fundamental concepts for each pattern
- Practice coding without IDE assistance
- Prepare to explain your thought process clearly
- Study common follow-up questions
- Ask clarifying questions about requirements and constraints
- Start with brute force, then optimize
- Think out loud and explain your approach
- Test your solution with examples
- Discuss trade-offs between different approaches
- Jumping to code without understanding the problem
- Not considering edge cases
- Focusing only on time complexity, ignoring space complexity
- Not testing the solution thoroughly
- Being unable to explain the algorithm clearly
This repository is created for educational purposes. All content is authored by Ritesh Rana.
- ✅ Personal study and practice
- ✅ Educational use in classrooms
- ✅ Reference during interviews (mention the source)
- ✅ Sharing with friends and colleagues
- ❌ Commercial redistribution without permission
- ❌ Claiming authorship of the content
Happy Coding and Good Luck with Your Interviews!