I’ve seen a lot of people take the wrong approach to learning data structures and algorithms (DSA). One student from last year stands out. He solved over 500 LeetCode problems but still got stuck when a variant of a problem came up in an interview. The issue was that he focused on solving a high number of problems without truly understanding the underlying concepts. To me, learning DSA is like building a house — if the foundation is weak, no amount of problem-solving will help.
I suggest starting with the basics: arrays and linked lists. Make sure you understand their core properties. For example, random access in an array is O(1), but insertion and deletion are O(n), whereas linked lists are the opposite. These characteristics determine how you use them in practice. One of my students had to frequently insert data in the middle of a structure for a project. At first, he used arrays, which performed poorly. After switching to linked lists, his program sped up significantly. This kind of hands-on experience is much more valuable than memorizing definitions.
Dynamic programming (DP) is a challenge for many. One student tackled it by first looking at the Fibonacci sequence’s recursive solution. After identifying the issue of repeated calculations, he optimized it with memoization and eventually moved to the standard DP approach. By doing this, he not only mastered DP but also understood the principle of algorithm optimization. Now, when he faces DP questions in interviews, he can quickly spot the state transition equation.
Choosing the right resources is also key. There are many tutorials online, but some are too shallow, while others are overly theoretical. I recommend looking for practical courses, preferably with real-world examples. One of my students joined an algorithm bootcamp by DrillInsight, where each algorithm was tied to real-world use cases, such as using Dijkstra’s algorithm for navigation or optimizing database indexes with red-black trees. After completing the course, he could not only solve problems but also understand how these algorithms are applied in industry.
Finally, don’t forget the power of review. One student I guided had a great habit: he kept a notebook of all the problems he got wrong, writing down why he got them wrong and how to solve them correctly. After three months, his problem-solving accuracy improved by 40%. When an interviewer gave him a variation of a problem he had previously struggled with, he immediately recognized it. Deliberate practice like this is far more effective than mindlessly solving problems.
Remember, learning algorithms is not just about preparing for interviews but about developing problem-solving skills. The best engineers I know can often abstract patterns from algorithm problems and apply them to real-world situations. For example, a Facebook engineer I know applied backtracking techniques to debug a distributed system and quickly found a tough bug. This kind of ability is the ultimate goal of learning DSA.
Release time:2025-04-25
