Fastest Way to Learn DSA | Full Roadmap

[00:00] Most people spend months or even years

[00:02] trying to master data structures and

[00:03] algorithms by reading textbooks,

[00:05] watching endless tutorials, memorizing

[00:07] algorithms, but still struggle to solve

[00:08] le code problems consistently. I was one

[00:10] of them. It took me almost a year of

[00:12] trial and error to figure out the right

[00:14] path. But once I did, I realized you

[00:16] could actually ace DSA interviews in

[00:18] just 10 weeks. In this video, I'll

[00:20] reveal the exact strategy that took me

[00:21] from failing Le Code easy problems to

[00:23] cracking interviews at Google on Amazon.

[00:25] and I'll share a step-by-step road map

[00:27] that you can start using today, as well

[00:28] as the resources that actually matter,

[00:30] the problems to focus on, and the

[00:32] accountability system that makes all the

[00:33] difference. By the end, you'll know

[00:35] exactly how to build the skills and

[00:36] confidence to ace your next interview.

[00:38] Hi friends, I'm Maddie. I'm a senior

[00:40] software engineer who's worked at Google

[00:42] and internet, Amazon, IBM, and

[00:43] Microsoft. I've been on both sides of

[00:45] the interview table. I've solved

[00:46] hundreds of lead code problems to land

[00:48] my roles, and I've interviewed dozens of

[00:50] candidates at Google. Let me start with

[00:52] a story that might sound familiar. You

[00:54] decide that you want to learn how to

[00:55] drive. So, you spend months studying the

[00:57] driver's manual, watching YouTube videos

[00:59] about how the car works, and memorizing

[01:01] traffic laws. Then, when you finally get

[01:03] behind the wheel for the first time, you

[01:05] realize that you actually have no idea

[01:07] how to really drive. This is exactly

[01:09] what most people do with data structures

[01:10] and algorithms. They think the path is

[01:12] to learn theory first, understand

[01:14] everything perfectly, and then apply it.

[01:16] But this is backward. I call this the

[01:18] tutorial trap. You're consuming so much

[01:20] information that you fool yourself into

[01:21] thinking you understand. But you've

[01:22] never actually practiced the fundamental

[01:24] skill. When I was struggling, I had

[01:26] watched more than 50 hours of algorithm

[01:28] tutorials and read cracking the coding

[01:30] interview cover to cover. But put me in

[01:32] front of an actual coding problem. I was

[01:34] completely lost. The reality is that

[01:35] your brain only learns through challenge

[01:37] and struggle. Yes, you need to know the

[01:39] basic theory still, but watching someone

[01:40] else solve problems doesn't teach you to

[01:42] solve problems anymore than watching

[01:44] someone else play basketball teaches you

[01:46] to shoot free throws. So, this brings me

[01:48] to the fundamental shift you need to

[01:49] make. Stop trying to learn all of the

[01:51] data structures and algorithms theory

[01:52] before you start solving problems. Think

[01:54] about learning guitar. You wouldn't

[01:56] start by memorizing music theory for

[01:57] months. You'd pick up the instrument,

[01:59] try to play simple songs, simple chords,

[02:01] and gradually more and more complex

[02:02] things. Great musicians didn't get there

[02:04] by studying theory books. They got there

[02:06] by playing thousands of hours and

[02:07] developing an ear for what sounds good.

[02:09] DSA is all about recognizing patterns

[02:11] and having practiced enough so that you

[02:13] develop a good intuition and can apply

[02:15] the right approach quickly. So, here's

[02:17] what you should do. Pick a problem. Try

[02:19] to solve it for 30 minutes max. And if

[02:21] you get stuck, look up the solution,

[02:22] type it out yourself, and understand why

[02:24] it works. Then move on to the next

[02:26] problem. You're building your pattern

[02:27] recognition muscle, not memorizing every

[02:29] possible algorithm. Now, let me give you

[02:31] the actual road map broken down week by

[02:33] week. This 10-week plan will take you

[02:35] from beginner to interview ready. Weeks

[02:37] 1 to two, foundation building. Start

[02:39] with the basics. If you've never taken a

[02:41] formal DSA course, that's totally fine.

[02:43] Spend these two weeks cramming intro to

[02:45] algorithms the CLS textbook. Focus on

[02:47] understanding things like big O

[02:49] notation, basic data structures like

[02:50] arrays and linked lists, and simple

[02:52] algorithms. Don't worry about any

[02:54] implementation yet. Just understand the

[02:55] concepts and the basic syntax. Weeks 3

[02:58] to four, core data structures. Now, dive

[03:00] into the essential data structures, for

[03:02] example stacks cues hashmaps and

[03:04] binary trees. Learn how they work

[03:06] internally and their time complexities.

[03:07] Start with le code easy problems that

[03:09] manipulate these data structures. This

[03:11] hands-on practice is critical. Weeks

[03:13] five to six algorithms patterns. Focus

[03:15] on the most important algorithms

[03:17] patterns. For example, two-pointer,

[03:18] sliding window, binary search, and basic

[03:20] recursion. These patterns will show up

[03:22] everywhere in interviews. Continue doing

[03:24] le code easy problems in parallel and

[03:26] start going through the le code 75,

[03:27] which is a problem list designed to

[03:29] cover the most important data structures

[03:31] and algorithms you'll need for coding

[03:32] interviews. Weeks 7 through 8 advanced

[03:34] topics. Tackle graphs, trees, and

[03:36] dynamic programming. Be sure to master

[03:38] breath for search and desperate search.

[03:40] If you're consistently solving le code

[03:42] mediums, consider getting le code

[03:43] premium for company specific practice.

[03:45] And finally, weeks 9 through 10,

[03:47] interview simulation. This is crunch

[03:50] time. Work through cracking the coding

[03:51] interview practice problems and do mock

[03:53] interviews three to four times per week

[03:55] and practice writing code on whiteboards

[03:57] or in simple text editors. This because

[03:59] some companies like Meta give you just a

[04:01] notepad instead of an actual IDE. So get

[04:03] comfortable tracing through your code by

[04:04] hand. Throughout all these 10 weeks,

[04:06] remember the 30 minute rule for mediums

[04:08] and 45minut rule for hards. If you're

[04:10] stuck, look up the solution after those

[04:12] time limits. Remember that you're

[04:13] building pattern recognition, not

[04:15] reinventing and implementing algorithms

[04:16] from scratch. Also, be sure to block out

[04:18] specific times in your calendar for each

[04:20] week's focus. Treat this like any other

[04:22] important commitment because it is. And

[04:24] let me be specific about what core data

[04:27] structures and algorithms you actually

[04:28] need to master. For data structures,

[04:30] focus on these core ones first. Arrays,

[04:32] linked lists, stacks, cues, hashts, and

[04:34] binary trees. These are your foundation

[04:36] for arrays. Master techniques like two

[04:38] pointer and sliding window. For example,

[04:40] two pointer is perfect for problems like

[04:42] finding pairs of sum to a target or

[04:44] removing duplicates from sorted arrays.

[04:45] Sliding window helps with substring

[04:47] problems or finding maximum sums and

[04:49] subarrays. For trees, you need to be

[04:51] comfortable with all three traversals in

[04:53] order, pre-order, and post-order. Know

[04:55] when to use each one. In order gives you

[04:57] sorted order BST. Pre-order is great for

[05:00] copying trees and post-order works well

[05:01] for bottom-up calculations. For graphs,

[05:04] master both BFS and DFS. BFS is perfect

[05:07] for shortest path problems and

[05:08] unweighted graphs, while DFS works well

[05:10] for detecting cycles or exploring all

[05:12] possible paths. Don't worry about

[05:13] advanced structures like trees or

[05:15] segment trees until you've nailed these

[05:16] basics. To be honest, most interview

[05:18] problems can be solved with these

[05:19] fundamental building blocks. And even

[05:21] with the right approach and problems,

[05:23] most people still fail. Why? because

[05:25] they try to do it alone. Learning DSA is

[05:27] like going to the gym. You can have the

[05:29] perfect workout plan, but if you're

[05:30] doing it alone without workout buddies,

[05:32] you more likely than not might quit when

[05:34] it gets hard, and there's no one else to

[05:36] shame you if you stop going. What

[05:37] finally worked for me was creating

[05:38] external accountability through a study

[05:40] group. Find two to four other people who

[05:42] are serious about getting good at these

[05:44] algorithms. Bonus points if you want to

[05:45] apply to similar companies. Meet several

[05:48] times a week for 2 to three hours each

[05:50] session and come prepared with three to

[05:51] five problems to work through together.

[05:53] The key is that you're competing with

[05:54] each other in a friendly way. I can't

[05:56] overstate how much this changes the

[05:58] game. When my friend would solve a

[05:59] medium problem before me, I go home and

[06:01] study extra hard just so I could come

[06:03] back and show what I learned and not

[06:04] feel like I was falling behind. That

[06:06] healthy competition was the missing

[06:07] piece for me. And if you can't find

[06:09] people IRL to do this with, that's

[06:11] totally fine. Consider tracking your

[06:13] progress publicly. For example, you

[06:15] could start a blog, tell your

[06:17] non-technical friends your goals, and

[06:18] make it embarrassing for yourself to

[06:19] quit. As you're practicing problems,

[06:21] keep in mind there's a critical

[06:22] difference between memorizing solutions

[06:24] and actually understand them. A lot of

[06:26] people get trapped in solution

[06:27] collection. For example, they see a

[06:29] problem, look up the answer, think, "Oh,

[06:30] that makes sense." And then move on. But

[06:32] when they see a similar problem later,

[06:34] they're stuck again. Here's how to avoid

[06:35] this. When you look up a solution, don't

[06:37] just understand the step-by-step

[06:38] process. Actually understand why it

[06:40] works. Ask yourself, why did they choose

[06:42] this data structure? What makes this

[06:43] approach efficient? How would I modify

[06:45] this if the problem changed? For

[06:46] example, with binary search, don't just

[06:48] memorize the template. Understand why we

[06:50] can eliminate half the search space each

[06:52] time. Understand what conditions must be

[06:54] true for binary search to work. That

[06:55] way, when you see a problem that's not

[06:57] obviously binary search, you might be

[06:59] able to still recognize the pattern. I

[07:01] use the teaching test. After solving a

[07:03] problem, can you explain the solution to

[07:04] someone else without looking at your

[07:05] notes? If not, then you don't really

[07:07] understand it yet. Here are some more

[07:08] practical tips. First, a quick note on

[07:11] language choice. I recommend Python for

[07:13] coding interviews unless you have a

[07:14] strong reason for not using it. It is

[07:16] minimum boilerplate, reads almost like

[07:18] English and lets you implement solutions

[07:19] quickly. Don't get hung up on whether

[07:21] interviewers will judge you for not

[07:22] using more difficult language like C++.

[07:24] You want to spend your mental energy on

[07:26] problem solving, not syntax. Secondly,

[07:28] consistency beats perfection every

[07:30] single time. It's better to spend 30

[07:31] minutes every day practicing than 8

[07:33] hours once a week. Your brain really

[07:35] needs time to process and make

[07:36] connections. Set a realistic goal and

[07:38] pick something you can actually stick

[07:40] to. And finally, don't beat yourself up

[07:42] when you can't solve a problem. I still

[07:44] remember not being able to solve twosome

[07:45] when I started. Literally one of the

[07:47] easiest problems on le code. We all have

[07:49] to start somewhere. And finally, I know

[07:51] I've been telling you to avoid studying

[07:52] theory upfront, but that doesn't mean

[07:54] theory isn't important. Once you

[07:56] struggle with problems and started

[07:57] recognizing patterns, that's when you

[07:58] should dive deeper into things like time

[08:00] complexity and master's theorem, space

[08:02] complexity, and theoretical foundations.

[08:04] The theory will stick much better

[08:05] because you now have practical context.

[08:07] And look, I'm not going to lie, learning

[08:08] DSA is hard and frustrating. There will

[08:11] be days where you feel like you're not

[08:12] making progress or you're actually going

[08:14] backward. But here's what I want you to

[08:16] remember. Every single engineer at those

[08:18] tech companies like Google, Amazon, and

[08:19] Meta has been exactly where you are

[08:21] right now. The difference between people

[08:23] who make it and the people who don't

[08:24] isn't natural talent. It's persistence

[08:26] and using an approach that actually

[08:27] works. And that's all I have for you in

[08:29] this video. If you found it helpful,

[08:31] please hit that like button. And if you

[08:32] want more content on software

[08:34] engineering, careers, interview prep,

[08:35] and breaking into tech, make sure to

[08:37] subscribe. I've got more videos planned

[08:38] on system design, behavioral interviews,

[08:40] and navigating your first sweet job.

[08:42] Thanks for watching, and I'll see you in

[08:43] the next one.