Top 7 Data Structures for Interviews Explained SIMPLY

Summary

This video script offers an insightful overview of seven fundamental data structures, essential for coding interviews, computer science studies, and project development. It simplifies concepts like arrays, linked lists, hash maps, stacks, queues, trees, and graphs, highlighting their unique properties and use cases. The script also touches on time complexity and memory allocation, providing a foundational understanding for beginners and a refresher for experienced programmers.

Takeaways

• 🧩 Arrays are ordered collections of data, typically of similar types, and are essential for beginners to learn.
• 📚 Arrays use zero-based indexing, making it easy to read elements but slower for inserting and deleting elements.
• 🔗 Linked lists store ordered data with pointers to the next element, making insertion and deletion faster but reading slower.
• 🔑 Hash maps (or hash tables/dictionaries) allow for custom keys, making searching, inserting, and removing elements very fast.
• 🧁 Stacks are LIFO (last in, first out) structures, similar to a stack of plates, with fast operations for adding, removing, and viewing elements.
• 🛒 Queues are FIFO (first in, first out) structures, like a grocery store line, with efficient operations for adding to the back and removing from the front.
• 🌳 Binary search trees are a type of binary tree where left children are less than the parent and right children are greater, facilitating quick searches.
• 📖 Binary search trees are useful for searching through large ordered datasets efficiently, like looking up words in a dictionary.
• 🔍 Graphs are models of connections with nodes and edges, which can be directed or undirected, weighted, and can include cycles, making them complex but versatile.
• 🚗 Graphs are practical for real-world applications like finding the shortest route for errands or optimizing ride-sharing services like Uber.

Q & A

What are the 7 most important data structures mentioned in the script?

The 7 most important data structures mentioned are Arrays, Linked Lists, HashMap, Stacks, Queues, Trees (specifically Binary Search Trees), and Graphs.

Why are arrays considered the most important data structure to learn first?

Arrays are considered the most important data structure to learn first because they are used all the time for pretty much everything, and they make it easy to read elements with a time complexity of O(1).

What is the main advantage of using an array?

The main advantage of using an array is that it's very easy to find any element due to the index assigned to each element, allowing for constant time complexity (O(1)) when accessing elements.

What is the time complexity for inserting or deleting elements in an array?

The time complexity for inserting or deleting elements in an array is O(n), as it may require shifting elements and potentially reallocating memory.

How does the memory allocation of arrays differ from linked lists?

Arrays are stored in contiguous memory locations, while elements in a linked list are not required to be stored next to each other due to the use of pointers to the next element.

What is the main advantage of linked lists over arrays in terms of element manipulation?

The main advantage of linked lists over arrays is that they are faster at inserting or deleting elements, as they do not require reallocation or shifting of elements.

How does a hash map differ from an array in terms of accessing elements?

A hash map differs from an array in that it uses keys to access elements instead of indexes, and it allows for quick searching with a time complexity of O(1) on average.

What is the time complexity for searching in a hash map?

The time complexity for searching in a hash map is O(1) on average, assuming a good hash function and no excessive collisions.

What is the main characteristic of a stack in terms of element order?

A stack is a LIFO (Last In, First Out) structure, meaning the last element added is the first one to be removed.

What are the three common operations associated with stacks?

The three common operations associated with stacks are push (adding an element), pop (removing the top element), and peek (viewing the top element without removing it).

How does a queue differ from a stack in terms of element order?

A queue is a FIFO (First In, First Out) structure, meaning the first element added is the first one to be removed, which is the opposite of a stack.

What are the three common operations associated with queues?

The three common operations associated with queues are enqueue (adding an element to the back), dequeue (removing the front element), and front (viewing the front element without removing it).

What is the main advantage of using binary search trees for searching through ordered values?

The main advantage of using binary search trees for searching through ordered values is that they allow for efficient searching with a time complexity of O(log n) by eliminating half of the remaining elements with each comparison.

What is a real-life example of a data structure that resembles a graph?

A real-life example of a data structure that resembles a graph is a transportation network, where nodes represent locations and edges represent the roads connecting them, with weights indicating distances or travel times.

What is the main characteristic that distinguishes directed graphs from undirected graphs?

The main characteristic that distinguishes directed graphs from undirected graphs is that in directed graphs, the edges have a direction, indicating that the connection between nodes is one-way, whereas in undirected graphs, the edges have no direction, indicating a two-way connection.

Outlines

00:00

📚 Introduction to Essential Data Structures

This paragraph introduces the topic of the video, which is an overview of the seven most important data structures. The speaker aims to simplify these concepts for viewers, regardless of their background, and plans to cover them in order of complexity. The paragraph emphasizes the importance of understanding data structures for coding interviews, computer science classes, and project development. It also mentions that time complexity for common operations will be displayed, but a deep understanding of it is not required to follow the content.

05:00

🔍 Arrays and Linked Lists: Basic Data Structures

The first data structure discussed is the array, an ordered collection of similar data types, with easy access to elements via indexing but less efficient for insertion or deletion. The paragraph explains the concept of zero-based indexing and the contiguous memory allocation of arrays, which can lead to inefficiencies when elements are added or removed. The second data structure is the linked list, which allows for faster insertion and deletion due to its non-contiguous memory allocation and pointer system, but is slower for accessing elements as it lacks direct indexing.

10:03

🗺️ HashMaps: Efficient Key-Value Pair Storage

The paragraph introduces hash maps, which are similar to arrays in that they store values with associated keys, but differ in that the keys can be of any type and the storage is unordered. Hash maps provide fast insertion, removal, and search capabilities, which is ideal for scenarios where quick lookup is necessary. The speaker also mentions that hash maps are known by various names, such as hash tables or dictionaries in Python, and that they will be covered in more depth in future videos.

🍽️ Stacks and Queues: LIFO and FIFO Data Structures

This paragraph explains two additional data structures: stacks and queues. Stacks are LIFO (last in, first out) structures, where elements are added and removed from the top, and have operations like push, pop, and peek. The paragraph uses the analogy of a stack of plates to illustrate this concept. Queues, on the other hand, are FIFO (first in, first out) structures, similar to a line of people waiting for service, with operations like enqueue, dequeue, and front. The paragraph also provides real-world examples of when these data structures might be used.

🌳 Trees: Hierarchical Data Structures

The paragraph delves into trees, a category of data structures that includes nodes connected by edges, starting with a root node. It focuses on binary trees and binary search trees, where each node has up to two children, and the tree is ordered such that all left children are less than the parent, and all right children are greater. This ordering makes binary search trees efficient for searching through ordered values, with the paragraph providing the examples of a number guessing game and a digital dictionary for context.

🚦 Graphs: Complex Networks of Nodes and Edges

Graphs are introduced as complex data structures that model a set of connections, with nodes and edges similar to trees but with fewer restrictions. Graphs can be directed or undirected, may contain cycles, and can have weighted edges. The paragraph explains that graphs are considered one of the most challenging data structures due to their complexity and provides an example of using graphs to find the shortest route between multiple locations, like running errands or the routing algorithms used by services like Uber.

🎉 Conclusion and Appreciation for Subscribers

The final paragraph concludes the video with a thank you to the viewers, expressing gratitude for the support received since the channel's inception. The speaker reflects on the rapid growth to 1,000 subscribers and promises to continue improving the quality and quantity of content. There's an invitation for viewers to engage by commenting and liking the video, and a hopeful look towards reaching the next milestone of 10,000 subscribers.

Keywords

💡Data Structures

Data structures are specialized formats for organizing, storing, and manipulating data in a way that facilitates certain operations. In the context of this video, they are the central theme, with the script focusing on explaining various types of data structures, their uses, and their complexities. The video aims to educate viewers on the importance of learning data structures for coding interviews, computer science classes, and project development.

💡Arrays

An array is an ordered collection of data elements, typically of a similar type. In the script, arrays are introduced as the most fundamental data structure, with the benefit of easy element access through indexing. The concept of zero-based indexing is highlighted, where the first element is at index 0, which can be confusing for beginners. Arrays are used to illustrate the trade-off between efficient element retrieval (O(1) time complexity) and less efficient insertion or deletion (O(n) time complexity).

💡Linked Lists

A linked list is a linear data structure where each element, called a node, contains a reference to the next node in the sequence. Unlike arrays, linked lists allow for efficient insertions and deletions as they do not require contiguous memory allocation. The script points out that linked lists are slower in reading elements due to the lack of direct indexing, requiring traversal from the beginning of the list to find a specific element.

💡HashMaps

A hashmap, also known as a hash table or dictionary in some programming languages, is a data structure that stores data in key-value pairs. It allows for fast insertion, removal, and search operations (O(1) time complexity) due to its use of a hash function to compute an index into an array of buckets. The video script emphasizes hashmaps' unordered nature and the flexibility of using custom keys for quick data retrieval.

💡Stacks

A stack is a data structure that follows the Last In, First Out (LIFO) principle. It is compared to a stack of plates in the script, where the last plate placed on top is the first to be removed. The script mentions common stack operations: push (adding an element), pop (removing the top element), and peek (viewing the top element). Stacks are highlighted for their use in scenarios where the most recent item needs to be accessed first.

💡Queues

Queues are data structures that operate on a First In, First Out (FIFO) basis, analogous to a line of people waiting for service. The script explains that queues are used when the order of processing matters, with operations such as enqueue (adding an element to the back), dequeue (removing the front element), and front (viewing the front element). Queues are noted to be more frequently used than stacks in real-world programming scenarios.

💡Trees

Trees are hierarchical data structures consisting of nodes connected by edges, with one node designated as the root. In the script, binary trees and binary search trees are specifically discussed. Binary search trees are highlighted for their ability to efficiently organize and search through ordered data, using the concept of eliminating half of the remaining elements with each comparison, similar to a number-guessing game.

💡Graphs

Graphs are complex data structures used to represent a set of objects (nodes) and the relationships or connections between them (edges). The script introduces graphs as potentially having directed or undirected edges, cycles, and weighted paths. Graphs are noted for their wide range of applications, such as finding the shortest route in a network, which is exemplified by the Uber application optimizing driver and rider connections.

💡Time Complexity

Time complexity is a measure of the efficiency of an algorithm, expressed in terms of the time it takes to complete in relation to the size of the input. In the script, time complexity is used to describe the efficiency of operations on different data structures, such as constant time (O(1)) for accessing elements in an array or logarithmic time (O(log n)) for searching in a binary search tree.

💡Zero-based Indexing

Zero-based indexing is a method of numbering elements in an array, where the index of the first element is zero. The script uses this concept to explain how arrays allow for direct access to elements using their index, but also points out the potential confusion for beginners who might expect the first element to be at index 1.

💡Memory Allocation

Memory allocation refers to the process of reserving and assigning portions of memory for use by a program. In the context of the script, it is mentioned in relation to arrays, where adding an element in the middle requires shifting elements and potentially reallocating the entire array to a new memory space, which can be inefficient.

Highlights

Introduction to 7 essential data structures for coding interviews, computer science classes, and project building.

Arrays are ordered collections with easy element access via zero-based indexing but harder insertions or deletions.

Arrays are stored in contiguous memory, requiring reallocation for size changes.

Linked Lists allow faster insertions and deletions with non-contiguous memory storage but slower element access.

HashMaps offer O(1) time complexity for insertion, removal, and searching with key-value pairs.

HashMaps are unordered and can be referred to as hash tables or dictionaries in different programming languages.

Stacks are LIFO data structures with push, pop, and peek operations for efficient last-in element access.

Queues are FIFO structures with enqueue, dequeue, and front operations, useful for managing sequences.

Trees, specifically binary search trees, enable efficient searching, insertion, and deletion in ordered data.

Binary search trees allow for middle-element guessing strategies for optimal searching.

Graphs model connections with nodes and edges, suitable for complex networks and pathfinding algorithms.

Graphs can be directed or undirected and may contain cycles or weighted edges.

Graphs are used in real-life applications like route optimization for services like Uber.

Explanation of time complexity for common operations on each data structure.

The importance of choosing the right data structure based on the required operations for efficiency.

A celebration of the channel reaching 1,000 subscribers and a commitment to improving content quality.

Invitation for viewers to request more data structure content and an expression of gratitude for support.

Transcripts

We’re going to go through the 7 most important data structures today and explain them as

simply as possible.

These are extremely important to learn whether its for coding interviews, computer science

class, or building projects.

We’ll be going through the list from easiest to hardest, so beginners have a better idea

of where to begin.

We’ll go over a simplified explanation for what each data structure is and talk about

common uses for that data structure.

I’ll also be putting the time complexity for common operations on screen, but if you

don’t know what that is, don’t worry; I’m just including it on the screen for

people who want to see.

Let’s not waste any time and get right into it.

Arrays Arrays are ordered collections of data.

Typically the data is all of a similar type, like integers or strings, but some languages

also allow for differing data types.

An example of a real-life use for an array would be if you had temperatures for the next

5 days, and wanted to store them so your program could access them.

Arrays are used all the time and for pretty much everything, so they’re definitely the

most important data structure to learn first.

One of the amazing benefits of using an array is that it’s very easy to find any element,

as each element in an array is assigned a number called an index that you can use to

find it.

This form of numbering is often called “zero-based indexing”, which just means the first element

in the array is at an index of 0.

This can often confuse new programmers, because if you want to retrieve the second element,

you have to use its index, which is 1, not 2.

While the advantage of arrays is that it makes it easy to read elements (O(1)), the disadvantage

is that they have a slightly harder time inserting or deleting elements (O(n)).

Now, just a quick note.

We won’t talk about memory much in this video, but for the first two data structures

in this video, the memory component is very simple, and important so you can tell the

difference between them.

We’ll use our temperature array from earlier as an example.

Arrays are stored in contiguous memory, which means each of the elements in the array are

next to one another in memory.

If a new element is added in the middle of the array, the entire array must shift down.

But what would’ve happened if there was already something in the next memory address?

To fit this new element in now, the array’s memory will have to be reallocated to an entirely

new space where all of the elements fit.

While computers are incredibly quick, this is not very efficient.

Arrays are very good for reading elements, but can a bit less efficient when it comes

to insertion or deletion.

Now we’ll take a look at a data structure that’s the opposite.

Linked Lists While arrays were fast at reading elements,

and a bit slower at inserting or deleting, linked lists are a bit slower at reading elements,

but fast at inserting or deleting.

Linked lists are similar to arrays, in that they also store ordered lists of data elements.

However, a huge difference is in the way they are stored in memory.

Each element of a linked list has what we call a pointer, which is basically the address

of the next element of the list.

As a result, elements in a linked list do not have to be stored beside each other.

You can store the next element in any location, and the previous element will point to it

if you want to find it.

The advantage of this is that it solves our problem with array insertions and deletions.

To add a new element, you find a free spot in memory for it, and have the previous element

point to it.

To remove an element, you just delete the element and have the previous point to the

one ahead of the deleted element.

The disadvantage of this is that linked lists do not have indexes, as the elements are not

stored right beside each other.

This means that to find an element, we have to go through the list starting from the beginning.

If we want the third element, we have to first look at the first element, see where it points,

go to the second, see where it points, and then we find the third.

If you think of a huge linked list, you can start to understand why it’s not the fastest

at reading.

So, to recap.

Arrays are faster when it comes to reading, linked lists are faster when it comes to inserting

and deleting.

HashMap Remember how arrays had values stored, and

for each value, there was an index that numbered them?

Well, hash maps are essentially the same thing, except that you can choose what the “index”

is, which hash maps call a key.

The key and it’s value are commonly known as key-value pairs.

The other major difference between arrays and hash maps is that hash maps are unordered.

Hash maps are fast (O(1)) for both inserting and removing elements.

But the real benefit to using hash maps is their ability to search quickly (O(1)).

Let’s say you wanted to store a list of capital cities.

If we stored these in an array, we would have to know the index for each one to read it.

But for a hash map, if we make the keys countries, we can just look up the country to find the

capital city.

Hash maps go by a few different names.

They are sometimes called hash tables, or, if you know Python, they’re called dictionaries.

For the purposes of this video, you can assume they’re all the same thing.

So, if you know how to use dictionaries in Python, congratulations!

You’ve actually been using hash maps.

The way hash maps work underneath the hood is really interesting, but a bit out of the

scope of this video.

I’ll be making specific videos for all of the data structures, so we’ll cover hash

maps more in-depth there.

For now, just understand that they’re unordered, and their custom keys allow for very quick

searching.

Stacks & Queues The most simple way to describe stacks is

to think of a stack of plates or pancakes.

The first plate goes on the bottom, the last plate goes the top.

Stacks are LIFO structures, which stands for last in, first out, because the last element

in is like the last plate that goes on top the stack.

When you go to grab a plate, this last plate on top will be the first you take off.

Stacks have three common operations, which are push, pop, and peek.

Pushing is when you add a new element to the top of the stack.

Popping is when you remove the top-most element from the stack.

And peeking is when you’re just taking a look at what the element at the top of the

stack is.

All of these are very fast, which is why stacks are optimal for certain problems.

If you’re wondering when stacks might be used, think of the pancake and plate examples.

For any scenario that has a similar structure where the last element in is the first element

out, stacks are likely a good data structure to use.

Queues are the opposites of stacks.

The simplest way to think of a queue is like a lineup at a grocery store.

The first person in the line will get serviced first, and every additional person who joins

the line goes at the end.

Queues are FIFO structures, which stands for first in, first out, because the first element

in is the first element to come out.

Queues have very similar operations to stacks, which are enqueue, dequeue, and front.

Enqueue is like push for a stack, and is when a new element is added to the back of the

queue.

Dequeue is like pop for a stack, and is when the element on the front of the queue is removed.

Front is like peek for a stack, and is when you take a look at the front-most element

in the queue.

Queues are more frequently used than stacks, especially in real-world programming.

Think of YouTube playlists.

When you start watching a playlist, you’ll start with the video that was added first,

and the last video you watch will be the final one that was added.

Trees Trees are a category of data structure that,

as you might have guessed by the name, resemble trees.

Trees have nodes, which are connected to each other by edges.

The first node in a tree is called the root node.

Nodes have a parent-child direction, where one is a parent node that leads to another

node, which is a child node.

Sometimes, the parent nodes are sometimes just called nodes, and the child nodes are

called leaves.

There are tons of tree-based data structures, but in this video, we’ll be talking about

binary trees, and in particular, binary search trees.

A binary tree is a tree where each parent node has up to two children nodes.

A binary search tree is a type of binary tree, where all left children nodes are less than

the parent node, and all right children nodes are greater than the parent node.

These binary search trees make it very easy to search through large amounts of ordered

values.

The classic example is to think of a number guessing game, where one person thinks of

a number between 1 and 100, and the other person has to guess.

With each guess, they get told whether the correct number is higher or lower than their

guess.

The strategy is to always guess the middle number, because then you’re eliminating

the most amount possible each time.

This is what a binary search tree does.

We can eliminate a parent node and everything below it or above it, and continue that process

until we get to our correct number.

This is not only useful for this game though.

A more practical example is to think of a digital dictionary.

Dictionaries have over 100,000 words.

If you give a computer a word, and want it to give you the definition, it would be incredibly

slow for it to start at the beginning and look through each word until it finds the

correct one (O(n)).

Instead, because the dictionary is sorted alphabetically, the computer is able to go

right to the word in the middle of the dictionary, and check if the target word comes before

or after.

It continues sorting like this until it reaches the word (O(logn)).

There are tons of other tree-structures like heaps and tries, but we’ll leave those for

another video.

Graphs Lastly we have graphs.

Graphs are basically models for a set of connections.

Like trees, graphs are made up of nodes and edges.

In fact, trees, and even linked lists to an extent, are technically types of graphs.

But graphs can get a lot more complicated.

In a graph, there are less restrictions than with trees.

Nodes can be connected to any amount of neighbours.

Graphs can be directed, where nodes point to other nodes, but they can also be undirected.

Some graphs have cycles, where two nodes both point to each other.

The edges between nodes can also be weighted, where the path has a value associated with

it.

As you can see, graphs are very complicated, which is why many people consider them to

be one of the hardest data structures to learn.

I’m going to make an entirely separate video dedicated to graphs, so we’ll tackle the

complexities in a little bit.

For now, let’s see an example where graphs are useful data structures.

Imagine you’re running errands, and you have five different stores to visit.

We can represent this as a graph, where each store is a node, and each edge has a distance.

Using this data structure, we can develop an algorithm that allows us to calculate what

the shortest route between all five places is.

For a real-life example, think of Uber.

Every Uber driver and user could be seen as nodes, and the application is constantly trying

to optimize so that the waiting time for each rider is as short as possible.

There are endless applications for graphs, which is why they’re such an important data

structure to understand.

Conclusion & Thank You!

Thanks so much for watching!

If you enjoyed this content on data structures and want to see more data structure videos,

comment down below and leave a like on the video.

I want to take a quick moment and express my gratitude to everyone for being so incredibly

supportive so far, and to celebrate hitting 1,000 subscribers.

I started this channel just over a month ago, and I’ve been super fortunate to have found

1,000 amazing people who have been willing to take a chance on my small YouTube channel.

I never thought I’d be able to hit 100 subscribers in this time, let alone 1,000, so I’m just

so thankful that all of you have been so supportive.

I promise I’ll do my absolute best to continue increasing the quality and quantity of content

as much as I can.

Thank you, and 10,000, here we come.