how I would learn Data Structures & Algorithms if I had to start over

Summary

In this video, Alvin shares his advice on how to effectively learn data structures and algorithms (DSA) for tech interviews. Instead of diving into problem lists like the Blind 75 or NeetCode 150, he recommends starting with a solid foundation in core concepts through beginner-friendly courses. Alvin stresses the importance of mastering high-leverage patterns such as hashmaps and DFS/BFS, which are frequently tested in interviews. He also advises against getting bogged down by specialized algorithms and encourages a holistic approach to job preparation, emphasizing efficiency and strategy in learning DSA.

Takeaways

• 😀 Don't focus on lists like the Blind 75 or LeetCode 150 when starting out. These are not suitable for beginners and may lead to frustration.
• 😀 Understand that the Blind 75 or similar lists are advanced goals. These problems require foundational knowledge of data structures and algorithms.
• 😀 Learning DSA is not about memorizing solutions to specific problems, but about understanding the core concepts that can be applied across problems.
• 😀 Skip the grind mentality. Grinding through problems without understanding the underlying principles is inefficient and unproductive.
• 😀 Start with foundational concepts and build your way up. Use beginner-friendly courses that cover the basics of each data structure before attempting more complex problems.
• 😀 Don't mistake 'easy' labels on LeetCode problems as truly easy. Difficulty depends on the background knowledge you already have.
• 😀 LeetCode problems were not designed for structured learning. Instead, use well-structured courses that introduce concepts gradually.
• 😀 Data structure and algorithm learning should not just be about solving problems, but about building a strong conceptual foundation.
• 😀 Focusing on high-leverage patterns (like hashmaps, DFS, BFS) with low learning time but high frequency of appearance in interviews will provide the most value for job preparation.
• 😀 Don't aim for solving an arbitrary number of problems. Instead, focus on mastering patterns that will give you the highest return for your time investment.
• 😀 Be selective with what you study. Avoid low-frequency, high-time investment algorithms that won't often appear in interviews, and focus on versatile tools like hashmaps.

Q & A

Why does the speaker advise against focusing on LeetCode problem lists like Blind 75 or NeetCode 150 for beginners?

The speaker advises against these lists because they are designed for those already proficient in data structures and algorithms. For beginners, attempting these lists without a strong foundation in the underlying concepts will make learning harder. It's better to first understand the basics before tackling these complex problems.

What does the speaker believe is the biggest mistake beginners make when learning data structures and algorithms?

The biggest mistake is jumping straight into solving LeetCode problems without having a solid understanding of fundamental data structures and algorithms. This approach can lead to confusion and slow progress.

How does the speaker suggest beginners should approach learning data structures and algorithms?

Beginners should start by focusing on learning individual data structures and algorithms through structured courses. These courses should introduce concepts gradually, starting from simple problems to more complex ones, ensuring foundational understanding before moving to advanced topics.

What is the difference between using a course and a list of LeetCode problems for learning?

A course is designed to be self-contained, providing both the foundational knowledge and related problems in a structured way. It starts with basic concepts and gradually increases in difficulty, ensuring that learners build their understanding progressively. In contrast, LeetCode problem lists can lack this structure and may overwhelm beginners.

What is the significance of focusing on high-leverage patterns in algorithm preparation?

Focusing on high-leverage patterns allows learners to solve a variety of common interview problems with minimal learning time investment. These patterns are frequent in interviews and serve as building blocks for more complex problems, making them crucial for effective and efficient preparation.

What does the speaker mean by 'low learning time' and 'high frequency' patterns?

'Low learning time' refers to concepts that are easy to grasp and require less effort to master. 'High frequency' refers to concepts that appear frequently in interviews. The speaker recommends focusing on patterns that are both quick to learn and commonly tested to maximize interview preparation efficiency.

What are some examples of 'low time, high frequency' patterns mentioned in the script?

Examples include hashmaps, depth-first search, and breadth-first search. These patterns are common in interviews and are often used as components in solving more advanced problems.

Why does the speaker recommend avoiding 'low time, low frequency' topics like bit manipulation?

Low time, low frequency topics, such as bit manipulation, are highly specific and rarely appear in interviews. While they may have niche applications, they are not as versatile as other algorithms like hashmaps, which can solve a broader range of problems. Therefore, studying them is less efficient.

How does the speaker suggest learners prioritize topics in their study of algorithms?

Learners should prioritize topics based on their frequency in interviews and the time required to master them. The speaker suggests focusing on high-frequency, low-learning time patterns first and gradually progressing to more complex topics that may take more time to learn.

What does the speaker mean by 'focusing on nothing' when learning algorithms?

The speaker warns against trying to learn everything at once. If you try to master every possible algorithm or solution, you'll end up being ineffective because you're not prioritizing the most important concepts. A focused approach, where you intentionally choose what not to study, is more effective.