Is 100 LeetCode enough?

The question "Is 100 LeetCode problems enough?" is a common and important one for individuals preparing for coding interviews, especially for highly competitive companies like Google. The short answer is it depends, but with the right approach, 100 well-chosen and thoroughly understood LeetCode problems can form a solid foundation for your interview preparation. However, to maximize effectiveness, it's essential to consider several factors beyond just the number of problems you solve.

Here's a detailed breakdown to help you assess whether 100 LeetCode problems are sufficient for your interview preparation and how to optimize your study strategy:

1. Quality Over Quantity

a. Problem Selection

• Focus on Key Topics: Ensure that your 100 problems cover the essential data structures and algorithms commonly tested in interviews. These include:
  • Data Structures: Arrays, Strings, Linked Lists, Stacks, Queues, Trees (Binary, Binary Search Trees), Graphs, Hash Tables, Heaps.
  • Algorithms: Sorting and Searching, Dynamic Programming, Recursion, Backtracking, Graph Traversal (BFS, DFS), Greedy Algorithms, Divide and Conquer.
• Difficulty Levels: Include a mix of Easy, Medium, and Hard problems. While solving Hard problems can be challenging, they often encapsulate multiple concepts and problem-solving patterns.

b. Depth of Understanding

• Conceptual Clarity: It's not just about solving the problem but understanding the underlying concepts and patterns. For each problem:
  • Analyze Time and Space Complexity: Be able to discuss and optimize your solution.
  • Identify Patterns: Recognize recurring patterns like sliding windows, two pointers, or dynamic programming paradigms.
• Multiple Approaches: Try to solve the same problem using different methods to deepen your understanding and flexibility in problem-solving.

2. Comprehensive Coverage of Topics

a. Balanced Problem Distribution

• Core Areas: Ensure that your 100 problems are distributed across all major topics. For example:
  • Arrays and Strings: ~20%
  • Linked Lists and Trees: ~20%
  • Dynamic Programming: ~15%
  • Graphs and Backtracking: ~15%
  • Sorting, Searching, and Greedy Algorithms: ~15%
  • Others (e.g., Bit Manipulation, Math Problems): ~15%

b. Variety of Problem Types

• Different Constraints and Scenarios: Solve problems with varying constraints, such as large inputs, multiple outputs, or unique edge cases.
• Real-World Applications: Engage with problems that simulate real-world scenarios to enhance practical problem-solving skills.

3. Supplementary Preparation Strategies

a. Mock Interviews

• Simulate Real Interview Conditions: Participate in mock interviews to practice coding under time constraints and receive feedback.
• Platforms to Use:
  • DsignGurus.io

b. Study Other Resources

• Books:
  • Cracking the Coding Interview by Gayle Laakmann McDowell
  • Elements of Programming Interviews by Adnan Aziz, Tsung-Hsien Lee, and Amit Prakash
  • Grokking the Coding Interview by Design Gurus
• Online Courses:
  • Coursera’s Data Structures and Algorithms Specialization
  • Udemy’s Master the Coding Interview: Data Structures + Algorithms

c. System Design Preparation (For Mid to Senior Roles)

• Understand Core Concepts: Scalability, load balancing, database design, microservices, caching.
• Resources:
  • Designing Data-Intensive Applications by Martin Kleppmann
  • DsignGurus.io's Grokking the System Design Interview

d. Behavioral Interview Preparation

• Use the STAR Method: Structure your responses to behavioral questions by outlining the Situation, Task, Action, and Result.
• Reflect on Experiences: Prepare stories that highlight your teamwork, leadership, problem-solving, and adaptability.

4. Time Management and Consistency

a. Structured Study Plan

• Daily Goals: Allocate specific time blocks each day for solving problems, reviewing concepts, and engaging in mock interviews.
• Progress Tracking: Keep track of the problems you've solved, areas you've covered, and topics that need more attention.

b. Avoid Burnout

• Balanced Schedule: Mix problem-solving with breaks and other activities to maintain mental well-being.
• Regular Reviews: Periodically revisit previously solved problems to reinforce learning and ensure retention.

5. Leveraging Feedback and Iteration

a. Analyze Mistakes

• Understand Errors: For each problem you struggle with or get wrong, spend time understanding why and how to correct it.
• Iterative Improvement: Continuously refine your approaches based on past mistakes and feedback.

b. Peer Discussions

• Join Study Groups: Engage with peers to discuss different problem-solving approaches and gain new perspectives.
• Online Communities: Participate in forums like LeetCode Discuss or Reddit’s r/cscareerquestions for additional support and insights.

6. Personalized Assessment

a. Evaluate Your Starting Point

• Skill Level: If you have a strong background in computer science and algorithmic thinking, 100 problems might be sufficient with in-depth practice.
• Experience: Candidates with industry experience might find that fewer problems are needed compared to those new to the field.

b. Identify Gaps

• Self-Assessment: Regularly assess which areas you’re strong in and which require more focus.
• Targeted Practice: Allocate more time to topics where you’re less confident to ensure comprehensive preparation.

7. Additional Considerations

a. Problem-Solving Speed

• Efficiency: Practice solving problems quickly without compromising on accuracy. This is crucial for timed interviews.
• Shortcut Techniques: Learn and apply techniques that can reduce the time taken to arrive at solutions, such as recognizing patterns and using optimal algorithms.

b. Language Proficiency

• Choose the Right Language: Use a programming language you’re most comfortable with to express solutions clearly and efficiently.
• Master Syntax and Libraries: Ensure you are well-versed with the syntax, standard libraries, and common functions of your chosen language.

Conclusion

Solving 100 LeetCode problems can be a substantial and effective component of your interview preparation, provided that you approach them strategically. Here are the key takeaways:

• Focus on Quality and Depth: Ensure each problem you solve enhances your understanding of fundamental concepts and problem-solving techniques.
• Comprehensive Coverage: Diversify the types and topics of problems to build a well-rounded skill set.
• Supplement with Other Resources: Use books, courses, and mock interviews to reinforce and expand your knowledge.
• Maintain Consistency and Manage Time: Stick to a structured study plan and avoid burnout by balancing practice with rest.
• Seek Feedback and Iterate: Continuously learn from your mistakes and refine your strategies based on feedback.

Ultimately, the number of problems is less important than how well you understand and can apply the concepts. Whether 100 is enough depends on your starting point, the quality of your practice, and how effectively you use additional resources to prepare.

Recommended Resources

Books:

• Cracking the Coding Interview by Gayle Laakmann McDowell
• Elements of Programming Interviews by Adnan Aziz, Tsung-Hsien Lee, and Amit Prakash
• Grokking the Coding Interview by Design Gurus

Online Platforms:

• LeetCode
• HackerRank
• DesignGurus
• Coderbyte

Courses:

• DsignGurus.io's Grokking the Coding Interview
• Coursera’s Data Structures and Algorithms Specialization
• Udemy’s Master the Coding Interview: Data Structures + Algorithms

Mock Interview Services:

• DsignGurus.io