[0:00] Most people spend months or even years
[0:02] trying to master data structures and
[0:03] algorithms by reading textbooks,
[0:05] watching endless tutorials, memorizing
[0:07] algorithms, but still struggle to solve
[0:08] le code problems consistently. I was one
[0:10] of them. It took me almost a year of
[0:12] trial and error to figure out the right
[0:14] path. But once I did, I realized you
[0:16] could actually ace DSA interviews in
[0:18] just 10 weeks. In this video, I'll
[0:20] reveal the exact strategy that took me
[0:21] from failing Le Code easy problems to
[0:23] cracking interviews at Google on Amazon.
[0:25] and I'll share a step-by-step road map
[0:27] that you can start using today, as well
[0:28] as the resources that actually matter,
[0:30] the problems to focus on, and the
[0:32] accountability system that makes all the
[0:33] difference. By the end, you'll know
[0:35] exactly how to build the skills and
[0:36] confidence to ace your next interview.
[0:38] Hi friends, I'm Maddie. I'm a senior
[0:40] software engineer who's worked at Google
[0:42] and internet, Amazon, IBM, and
[0:43] Microsoft. I've been on both sides of
[0:45] the interview table. I've solved
[0:46] hundreds of lead code problems to land
[0:48] my roles, and I've interviewed dozens of
[0:50] candidates at Google. Let me start with
[0:52] a story that might sound familiar. You
[0:54] decide that you want to learn how to
[0:55] drive. So, you spend months studying the
[0:57] driver's manual, watching YouTube videos
[0:59] about how the car works, and memorizing
[1:01] traffic laws. Then, when you finally get
[1:03] behind the wheel for the first time, you
[1:05] realize that you actually have no idea
[1:07] how to really drive. This is exactly
[1:09] what most people do with data structures
[1:10] and algorithms. They think the path is
[1:12] to learn theory first, understand
[1:14] everything perfectly, and then apply it.
[1:16] But this is backward. I call this the
[1:18] tutorial trap. You're consuming so much
[1:20] information that you fool yourself into
[1:21] thinking you understand. But you've
[1:22] never actually practiced the fundamental
[1:24] skill. When I was struggling, I had
[1:26] watched more than 50 hours of algorithm
[1:28] tutorials and read cracking the coding
[1:30] interview cover to cover. But put me in
[1:32] front of an actual coding problem. I was
[1:34] completely lost. The reality is that
[1:35] your brain only learns through challenge
[1:37] and struggle. Yes, you need to know the
[1:39] basic theory still, but watching someone
[1:40] else solve problems doesn't teach you to
[1:42] solve problems anymore than watching
[1:44] someone else play basketball teaches you
[1:46] to shoot free throws. So, this brings me
[1:48] to the fundamental shift you need to
[1:49] make. Stop trying to learn all of the
[1:51] data structures and algorithms theory
[1:52] before you start solving problems. Think
[1:54] about learning guitar. You wouldn't
[1:56] start by memorizing music theory for
[1:57] months. You'd pick up the instrument,
[1:59] try to play simple songs, simple chords,
[2:01] and gradually more and more complex
[2:02] things. Great musicians didn't get there
[2:04] by studying theory books. They got there
[2:06] by playing thousands of hours and
[2:07] developing an ear for what sounds good.
[2:09] DSA is all about recognizing patterns
[2:11] and having practiced enough so that you
[2:13] develop a good intuition and can apply
[2:15] the right approach quickly. So, here's
[2:17] what you should do. Pick a problem. Try
[2:19] to solve it for 30 minutes max. And if
[2:21] you get stuck, look up the solution,
[2:22] type it out yourself, and understand why
[2:24] it works. Then move on to the next
[2:26] problem. You're building your pattern
[2:27] recognition muscle, not memorizing every
[2:29] possible algorithm. Now, let me give you
[2:31] the actual road map broken down week by
[2:33] week. This 10-week plan will take you
[2:35] from beginner to interview ready. Weeks
[2:37] 1 to two, foundation building. Start
[2:39] with the basics. If you've never taken a
[2:41] formal DSA course, that's totally fine.
[2:43] Spend these two weeks cramming intro to
[2:45] algorithms the CLS textbook. Focus on
[2:47] understanding things like big O
[2:49] notation, basic data structures like
[2:50] arrays and linked lists, and simple
[2:52] algorithms. Don't worry about any
[2:54] implementation yet. Just understand the
[2:55] concepts and the basic syntax. Weeks 3
[2:58] to four, core data structures. Now, dive
[3:00] into the essential data structures, for
[3:02] example stacks cues hashmaps and
[3:04] binary trees. Learn how they work
[3:06] internally and their time complexities.
[3:07] Start with le code easy problems that
[3:09] manipulate these data structures. This
[3:11] hands-on practice is critical. Weeks
[3:13] five to six algorithms patterns. Focus
[3:15] on the most important algorithms
[3:17] patterns. For example, two-pointer,
[3:18] sliding window, binary search, and basic
[3:20] recursion. These patterns will show up
[3:22] everywhere in interviews. Continue doing
[3:24] le code easy problems in parallel and
[3:26] start going through the le code 75,
[3:27] which is a problem list designed to
[3:29] cover the most important data structures
[3:31] and algorithms you'll need for coding
[3:32] interviews. Weeks 7 through 8 advanced
[3:34] topics. Tackle graphs, trees, and
[3:36] dynamic programming. Be sure to master
[3:38] breath for search and desperate search.
[3:40] If you're consistently solving le code
[3:42] mediums, consider getting le code
[3:43] premium for company specific practice.
[3:45] And finally, weeks 9 through 10,
[3:47] interview simulation. This is crunch
[3:50] time. Work through cracking the coding
[3:51] interview practice problems and do mock
[3:53] interviews three to four times per week
[3:55] and practice writing code on whiteboards
[3:57] or in simple text editors. This because
[3:59] some companies like Meta give you just a
[4:01] notepad instead of an actual IDE. So get
[4:03] comfortable tracing through your code by
[4:04] hand. Throughout all these 10 weeks,
[4:06] remember the 30 minute rule for mediums
[4:08] and 45minut rule for hards. If you're
[4:10] stuck, look up the solution after those
[4:12] time limits. Remember that you're
[4:13] building pattern recognition, not
[4:15] reinventing and implementing algorithms
[4:16] from scratch. Also, be sure to block out
[4:18] specific times in your calendar for each
[4:20] week's focus. Treat this like any other
[4:22] important commitment because it is. And
[4:24] let me be specific about what core data
[4:27] structures and algorithms you actually
[4:28] need to master. For data structures,
[4:30] focus on these core ones first. Arrays,
[4:32] linked lists, stacks, cues, hashts, and
[4:34] binary trees. These are your foundation
[4:36] for arrays. Master techniques like two
[4:38] pointer and sliding window. For example,
[4:40] two pointer is perfect for problems like
[4:42] finding pairs of sum to a target or
[4:44] removing duplicates from sorted arrays.
[4:45] Sliding window helps with substring
[4:47] problems or finding maximum sums and
[4:49] subarrays. For trees, you need to be
[4:51] comfortable with all three traversals in
[4:53] order, pre-order, and post-order. Know
[4:55] when to use each one. In order gives you
[4:57] sorted order BST. Pre-order is great for
[5:00] copying trees and post-order works well
[5:01] for bottom-up calculations. For graphs,
[5:04] master both BFS and DFS. BFS is perfect
[5:07] for shortest path problems and
[5:08] unweighted graphs, while DFS works well
[5:10] for detecting cycles or exploring all
[5:12] possible paths. Don't worry about
[5:13] advanced structures like trees or
[5:15] segment trees until you've nailed these
[5:16] basics. To be honest, most interview
[5:18] problems can be solved with these
[5:19] fundamental building blocks. And even
[5:21] with the right approach and problems,
[5:23] most people still fail. Why? because
[5:25] they try to do it alone. Learning DSA is
[5:27] like going to the gym. You can have the
[5:29] perfect workout plan, but if you're
[5:30] doing it alone without workout buddies,
[5:32] you more likely than not might quit when
[5:34] it gets hard, and there's no one else to
[5:36] shame you if you stop going. What
[5:37] finally worked for me was creating
[5:38] external accountability through a study
[5:40] group. Find two to four other people who
[5:42] are serious about getting good at these
[5:44] algorithms. Bonus points if you want to
[5:45] apply to similar companies. Meet several
[5:48] times a week for 2 to three hours each
[5:50] session and come prepared with three to
[5:51] five problems to work through together.
[5:53] The key is that you're competing with
[5:54] each other in a friendly way. I can't
[5:56] overstate how much this changes the
[5:58] game. When my friend would solve a
[5:59] medium problem before me, I go home and
[6:01] study extra hard just so I could come
[6:03] back and show what I learned and not
[6:04] feel like I was falling behind. That
[6:06] healthy competition was the missing
[6:07] piece for me. And if you can't find
[6:09] people IRL to do this with, that's
[6:11] totally fine. Consider tracking your
[6:13] progress publicly. For example, you
[6:15] could start a blog, tell your
[6:17] non-technical friends your goals, and
[6:18] make it embarrassing for yourself to
[6:19] quit. As you're practicing problems,
[6:21] keep in mind there's a critical
[6:22] difference between memorizing solutions
[6:24] and actually understand them. A lot of
[6:26] people get trapped in solution
[6:27] collection. For example, they see a
[6:29] problem, look up the answer, think, "Oh,
[6:30] that makes sense." And then move on. But
[6:32] when they see a similar problem later,
[6:34] they're stuck again. Here's how to avoid
[6:35] this. When you look up a solution, don't
[6:37] just understand the step-by-step
[6:38] process. Actually understand why it
[6:40] works. Ask yourself, why did they choose
[6:42] this data structure? What makes this
[6:43] approach efficient? How would I modify
[6:45] this if the problem changed? For
[6:46] example, with binary search, don't just
[6:48] memorize the template. Understand why we
[6:50] can eliminate half the search space each
[6:52] time. Understand what conditions must be
[6:54] true for binary search to work. That
[6:55] way, when you see a problem that's not
[6:57] obviously binary search, you might be
[6:59] able to still recognize the pattern. I
[7:01] use the teaching test. After solving a
[7:03] problem, can you explain the solution to
[7:04] someone else without looking at your
[7:05] notes? If not, then you don't really
[7:07] understand it yet. Here are some more
[7:08] practical tips. First, a quick note on
[7:11] language choice. I recommend Python for
[7:13] coding interviews unless you have a
[7:14] strong reason for not using it. It is
[7:16] minimum boilerplate, reads almost like
[7:18] English and lets you implement solutions
[7:19] quickly. Don't get hung up on whether
[7:21] interviewers will judge you for not
[7:22] using more difficult language like C++.
[7:24] You want to spend your mental energy on
[7:26] problem solving, not syntax. Secondly,
[7:28] consistency beats perfection every
[7:30] single time. It's better to spend 30
[7:31] minutes every day practicing than 8
[7:33] hours once a week. Your brain really
[7:35] needs time to process and make
[7:36] connections. Set a realistic goal and
[7:38] pick something you can actually stick
[7:40] to. And finally, don't beat yourself up
[7:42] when you can't solve a problem. I still
[7:44] remember not being able to solve twosome
[7:45] when I started. Literally one of the
[7:47] easiest problems on le code. We all have
[7:49] to start somewhere. And finally, I know
[7:51] I've been telling you to avoid studying
[7:52] theory upfront, but that doesn't mean
[7:54] theory isn't important. Once you
[7:56] struggle with problems and started
[7:57] recognizing patterns, that's when you
[7:58] should dive deeper into things like time
[8:00] complexity and master's theorem, space
[8:02] complexity, and theoretical foundations.
[8:04] The theory will stick much better
[8:05] because you now have practical context.
[8:07] And look, I'm not going to lie, learning
[8:08] DSA is hard and frustrating. There will
[8:11] be days where you feel like you're not
[8:12] making progress or you're actually going
[8:14] backward. But here's what I want you to
[8:16] remember. Every single engineer at those
[8:18] tech companies like Google, Amazon, and
[8:19] Meta has been exactly where you are
[8:21] right now. The difference between people
[8:23] who make it and the people who don't
[8:24] isn't natural talent. It's persistence
[8:26] and using an approach that actually
[8:27] works. And that's all I have for you in
[8:29] this video. If you found it helpful,
[8:31] please hit that like button. And if you
[8:32] want more content on software
[8:34] engineering, careers, interview prep,
[8:35] and breaking into tech, make sure to
[8:37] subscribe. I've got more videos planned
[8:38] on system design, behavioral interviews,
[8:40] and navigating your first sweet job.
[8:42] Thanks for watching, and I'll see you in
[8:43] the next one.