How Many Leetcode Problems Should You Solve?

TL;DR

To effectively prepare for coding interviews, solving around 400 hand-picked Leetcode problems is sufficient for most candidates. Focus on foundational topics first, gradually moving to complex problems, while maintaining a structured practice routine for better retention and confidence. Seeking mentorship can also enhance the learning experience and help avoid common mistakes.

Transcript

I have sold around 1100 questions on lead code and it has helped me crack a lot of interviews but isn't it too much to solve 1100 questions hi everyone I am Faraz and I work at Google as a software engineer in this video I will share my views on how you can practice how many questions you should solve how to revise and the mistakes which you can av... Read More

Key Insights

• 👨‍💻 A structured learning approach is crucial for success in coding interviews; starting with foundational topics helps build confidence.
• ❓ Avoid overwhelming yourself with excessive problem-solving; a limited, focused practice is often more effective.
• 😀 Mentorship can greatly enhance the learning experience, preventing common pitfalls that beginners face.
• ⌛ Revisiting and revising topics is essential to retaining knowledge over time, ensuring that concepts are not forgotten.
• 🤑 Tackling problems in increasing difficulty, starting with easy ones, can provide a confidence boost and smoother progression.
• 👨‍💻 Maintaining consistency in practice is vital for improvement; regular coding practice yields better long-term results.
• 👻 Creating projects outside of coding interview preparation can provide balance, allowing for personal growth while pursuing technical skills.

Questions & Answers

Q: What are the common challenges faced by beginners in coding interviews?

Beginners often struggle with feeling overwhelmed by difficult problems, especially if they lack foundational knowledge. This can lead to frustration and discouragement. It's essential to have a structured learning path that builds confidence through easier problems before advancing to harder ones.

Q: How can someone effectively revise their learned concepts in DSA?

Revising DSA concepts can be done by maintaining a curated list of problems that represent each topic. Faraz suggests selecting two problems from each pattern and revisiting them regularly, especially on weekends, to strengthen memory retention and understanding of algorithms.

Q: What specific topics should one focus on first when preparing for coding interviews?

It's advisable to start with basic topics like arrays and strings, progressing through techniques like the two-pointer approach and sliding window. Once comfortable, one can move to more complex data structures like trees and graphs, ensuring a strong foundation before tackling advanced topics.

Q: What role does mentorship play in preparing for coding interviews?

Mentorship can significantly enhance the learning experience by providing guidance, correcting missteps, and offering moral support. Faraz stresses that having a mentor can prevent wasted time and effort, making the preparation journey smoother and more effective.

Q: How many problems should one ideally solve before advancing to new topics?

Faraz recommends solving around 30 to 40 well-chosen problems to cover essential patterns before moving on. He believes that focusing on quality rather than quantity is key to efficient learning and understanding in DSA.

Q: What is the best mindset to adopt when facing difficult programming problems?

Adopting a mindset that views each difficult problem as a learning opportunity can help. Instead of feeling discouraged, Faraz suggests embracing the challenge as a chance to develop new techniques and strategies that will be beneficial for future problems.

Summary & Key Takeaways

• Faraz discusses his experience with coding interviews, starting from struggling with dynamic programming to solving over 1,100 questions on platforms like LeetCode.

• He emphasizes the importance of a structured approach to learning, recommending that beginners tackle basic data structures and algorithms before progressing to more complex topics.

• The speaker advocates for seeking mentorship and maintaining consistency in practice, suggesting a balanced routine for college students to avoid burnout.