Transcription
If coding problems feel overwhelming or confused, you are not alone. And you've probably asked yourself which data structure algorithm topics are actually worth studying. Most tech companies ask the same eight data structure algorithms patterns over and over again in their interviews. And if you understand those concepts deeply, you're already ahead of 90% of candidates.
Hi friends, I'm Maddie. I'm a senior softer who previously worked at Google and internet other big tech companies like Amazon, IBM, and Microsoft. I've been down the internet gauntlet at multiple companies. And today, I'm breaking down the eight essential data structures algorithms concepts you should master with tangible example problems. By the end of this video, you'll understand exactly which data structures matter most, how they're tested in technical interviews, and the specific problems you need to practice.
[music] Let's get right into it. Before we even touch data structures, you need to understand big O notation because interviewers will ask you to analyze your solutions efficiency every single time. Big O measures how your algorithm scales as input size grows. Think of it like searching time. If I already know my phone's charging from the wall outlet and need to grab it from there, that's O of one, constant time. It doesn't matter how many items are in my room. But if I'm searching for a specific book on my bookshelf by checking each one, that's O of N, linear time, where N is the number of books. In terms of interviews, O of one is constant and ideal, but doesn't appear very often. O of log N is very efficient like binary search. O of N is acceptable for most problems, and O of N log N is what you get with efficient sorting. and O of N^ squ or worse usually means you probably need to optimize [music] more.
Interviewers love asking what is the time and space complexity of your solution after you solve a problem. For example, if you're solving the classic two problem, finding two numbers that add up to a target, the naive approach, checking each pair is O of N squ, but using a hashmap gets you O of N. You should always be thinking about time and space trade-offs. Sometimes using extra memory with something like a hashmap gives you faster runtime, and interviewers want to hear you articulate those decisions.
Now that we understand time and space complexity, let's talk about our first data structure, arrays. An array stores elements at contiguous memory locations with O of one access by index, but inserting or deleting in the middle takes O of N because you need to shift elements. Interviewers might ask you to do array operations in place to test space complexity awareness. When they say things like reverse a string in place or rotate an array, they're checking if you understand how to manipulate arrays without allocating new memory. In order to master arrays, get comfortable with array traversal patterns, forward and backward iteration, 2.0 technique, and sliding window. These patterns appear everywhere in coding interviews.
Next, let's talk about hashmaps. A hashmap stores key value pairs with an average of one insertion, deletion, and lookup by using a hash function to compute where the item should go. Think of a hashmap like a code check where instead of searching through a massive pile of codes to find yours, you instead present a unique ticket, the key, and the attendant instantly goes to the specific hook, the index where your item is stored. For example, let's rehash the classic twosome problem we talked about earlier. Again, you're given an array and a target sum, and you need to find two numbers in the array that add up to the target. The hashmap solution iterates through the array once, storing each number, and checks if the target minus the current number exists in your map. that is O of N time versus the O of N squed for the brute force approach.
Here's another classic interview scenario. Design a file system with create path and [music] get methods. This appears in some actual tech interviews and the optimal solution. Use a hashmap to store paths as keys with their values. The interviewer might then ask you to extend it with additional operations like listing directories or adding content to files, testing your ability to build on top of your data structure choices. Something else you should keep in mind is how hash collisions work and how best to resolve them. When two keys hash to the same location, most implementations use chaining storing multiple values in a linked list at that index. [music] This is why hashmap operations are of one on average, but can degrade to O of N in the worst case with many collisions. So the bottom line is that whenever you need fast lookups or need to track frequency or existing elements, think of a hashmap first.
Now let's talk about sets. Sets are like hashmaps but only store keys without values, maintaining uniqueness with O1 average case operations. Interviewers use sets to test whether you recognize patterns around uniqueness and membership. Common interview questions that you can use sets to answer are ones like remove duplicates from an array. A set gives you automatic dduplication. Find intersection of two arrays. Add one array to a set and iterate the other checking membership. Detect if a linked list has a cycle. Track visited nodes in a set. The trick with sets is that they're often not the main solution but a supporting data structure. Interviewers might have you use a set alongside another structure like a graph for marking visited nodes during BFS or DFS traversal.
Now let's talk about linked lists. Linked lists store data as nodes where each node contains a value and a pointer to the next node. Unlike arrays, link lists don't give you one access by index. You have to traverse from the head. But inserting or deleting nodes is of one if you have a pointer to the right location. Classic linked list interview questions include ones like reverse a linked list. This tests pointer manipulation and whether you can think iteratively or recursively. Detect a cycle in a linked list. This checks if you know the two-pointer technique using slow and fast pointers. Merge two sorted linked lists, testing your ability to manipulate pointers while maintaining sorted orders. You might even encounter linked lists [music] in your classic design and LRU or least recently used cache system design question. This uses both a hashmap like we talked about previously and a doubly linked list. The hashmap gives you o1 lookup while the linked list maintains the order of recently used items. When you access an item, you move it to the front of the list. When the cache is full and you need to add a new item, you remove it from the tail.
The next important data structures to cover are stacks and cues. Stacks are last in first out. Think of a stack of plates where you can only add or remove the top. Q's on the other hand are first in first out like a line or a Q at a coffee shop. Both give you 01 insertion and deletion. A classic interview question that you'll need a stack for is the valid parenthesis question. Checking if brackets are balanced is a perfect stack problem. Every opening bracket gets pushed. Every closing bracket should then match the top of your stack. Stacks appear constantly in problems involving parsing, matching or tracking previous states. Anytime you see a problem where you need to process elements in reverse order or track the most recent item, think stack.
Q's on the other hand show up in breath first search, which is huge for interviews involving trees and graphs. when you're doing level order traversal of a tree or finding the shortest path in an unweighted graph using a queue to process nodes level by level.
So speaking of trees, what exactly are they? Trees are data structures that organize data hierarchically with nodes and parent child relationships. Binary search trees are trees with a special property. The left child is always smaller than the parent and the right child is always larger. This gives you of log and search, insertion and deletion for balanced trees. Common tree interview questions are ones like validate if a tree is a valid binary search tree. This tests whether you can think recursively and maintain constraints as you traverse down. Find the lowest common ancestor of two nodes is another classic. And finally, in order pre-order and postorder tree traversals. Interviewers want to know if you can traverse those trees iteratively or recursively.
And finally, let's talk about heaps and another type of Q, [music] priority cues. Heaps are complete binary trees where parent nodes are always greater in a max heap or smaller in a min heap than their children. Priority cues are typically implemented with heaps [music] and give you o of log and insertion and deletion with O of one access to the min or max element. Think of a priority Q like a hospital emergency room. Instead of treating patients in the same order they arrive, the most critical case is always seen [music] first. New patients are assigned a triage score and the ER continuously maintains the ordering so that the most urgent case always rises to the top of the queue. Classic heap patterns include questions like find the kth largest element in array. The naive approach sorts the array with O of N log N time complexity. But using a min heap of size K gives you O of N log K, which is fast for large arrays of small K.
And there you have it. These are the top eight data structures and algorithms concepts you should know to ace your interviews. GGO notation, arrays, hashmaps, [music] sets, linked lists, stacks and cues, trees, and heaps and priority cues. The trick isn't just knowing what each structure is. It's recognizing which one solves a problem effectively and being able to articulate why. Hope this video is helpful and if you enjoyed it, please hit that like button and subscribe to my channel for more tech walkthroughs. Thanks for watching and I'll see you in the next one.