Tutorial Playlist
58 Lessons1. Data Structure
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2. Types of Linked Lists
3. Array vs Linked Lists in Data Structure
4. Stack vs. Queue Explained
5. Singly Linked List
6. Circular doubly linked list
7. Circular Linked List
8. Stack Implementation Using Array
9. Circular Queue in Data Structure
10. Dequeue in Data Structures
11. Bubble Sort Algorithm
12. Insertion Sort Algorithm
13. Shell Sort Algorithm
14. Radix Sort
15. Counting Sort Algorithm
16. Trees in Data Structure
17. Tree Traversal in Data Structure
18. Inorder Traversal
19. Optimal Binary Search Trees
20. AVL Tree
21. Red-Black Tree
22. B+ Tree in Data Structure
23. Expression Tree
24. Adjacency Matrix
25. Spanning Tree in Data Structure
26. Kruskal Algorithm
27. Prim's Algorithm in Data Structure
28. Bellman Ford Algorithm
29. Ford-Fulkerson Algorithm
30. Trie Data Structure
31. Floyd Warshall Algorithm
32. Rabin Karp Algorithm
33. What Is Dynamic Programming?
34. Longest Common Subsequence
35. Fractional Knapsack Problem
36. Greedy Algorithm
37. Longest Increasing Subsequence
38. Matrix Chain Multiplication
39. Subset Sum Problem
40. Backtracking Algorithm
41. Huffman Coding Algorithm
42. Tower of Hanoi
43. Stack vs Heap
44. Asymptotic Analysis
45. Binomial Distribution
46. Coin Change Problem
47. Fibonacci Heap
48. Skip List in Data Structure
49. Sparse Matrix
50. Splay Tree
51. Queue in Data Structure
52. Stack in Data Structure
53. Time and Space Complexity
54. Linked List in Data Structure
55. Stack And Queue: Roles & Functions
56. Doubly Linked List
57. Strongly Connected Components
58. Bucket Sort Algorithm
Data structures are the backbone of computer science. It helps streamline everything - improving how things are organized and letting us tweak information in IT systems as an expert would. We are going to dive into the heart of data structures, exploring their various types and operations. But we would not stop there - let's look at how they rock in real-world applications too!
So, what is a data structure? Well, think of it as a tidy arrangement that keeps our data in check. It is like having a personal librarian—making sure you can get what you need efficiently. From the simplest arrays and linked lists to more intricate tree and graph structures, data is housed in a variety of ways. Picking the right data structure is like choosing the best tool for a job. It is vital to craft algorithms and boost program performance. So, think of your data structure as an unsung hero in building scalable software solutions.
Data types are the building blocks of programming - they define what kinds of values can be stored and manipulated. Think of numbers, text strings, or boolean values. However, when we start dealing with larger amounts of information, that's where data structures come into play. Consider them as organizing tools for our data—think arrays to store multiple items or trees for hierarchical relationships. The key difference here is that while a data type gives us a single value or piece of information, a data structure helps us manage many pieces at once in an organized way.
Data Type:
Data Structure:
Data structure is an efficient way to organize and keep information. In the computing world, data structure helps arrange information for optimal operations.
There are broadly two types of data structure, linear and non-linear with several subtypes, each with its characteristics and usages.
Let us dive deeper into all the mentioned types.
A linear data structure arranges elements in a sequence, each with a distinct predecessor and successor, barring the first and last elements. The linear aspect allows for clean traversal because factors are obtained linearly. Arrays and linked lists are the main subtypes of linear data structures. Linear data structures are essential for organizing and processing records in a linear manner, making them a key component of many algorithms and applications.
Arrays:
In a basic linear data structure, arrays store the same type of elements in contiguous memory locations. Arrays come in handy where quick retrieval and manipulation of data are required, as they can be accessed through indices. Storage of similar datasets, mathematical operations, and algorithms are typical use cases of arrays.
Example:
Let’s look at a simple example of arrays in Python:
# Example of an array
my_array = [1, 2, 3, 4, 5]
# Accessing elements using indices
print("Element at index 2:", my_array[2]) # Output: 3
# Modifying an element
my_array[1] = 10
# Adding a new element at the end
my_array.append(6)
# Deleting an element
del my_array[3]
# Printing the modified array
print("Modified Array:", my_array)
In the above example:
Linked Lists:
Linked lists have nodes that are linked sequentially, allowing for dynamic memory allocation and architectural adaptability. Each node has two sub-items: a data field and a reference to the following node. The flexible memory allocation of linked lists makes them apt for various applications using intricate data structures.
Example:
The following Python example helps explain the functionalities of a linked list:
# Node class for a linked list
class Node:
def __init__(self, data):
self.data = data
self.next = None
# Creating nodes
node1 = Node(1)
node2 = Node(2)
node3 = Node(3)
# Linking nodes
node1.next = node2
node2.next = node3
# Traversing the linked list
current_node = node1
while current_node:
print(current_node.data, end=" -> ")
current_node = current_node.next
In this example:
Queue:
This type of linear data structure works on the principle of First-in-First-out (FIFO), which means that the first element to be added to the queue is also the first one to be removed.
Example:
Let’s look at the following example for clarity:
class Queue:
def __init__(self):
self.items = []
def enqueue(self, item):
self.items.insert(0, item)
def dequeue(self):
if not self.is_empty():
return self.items.pop()
def is_empty(self):
return len(self.items) == 0
def size(self):
return len(self.items)
# Creating a queue
my_queue = Queue()
# Enqueueing elements
my_queue.enqueue(1)
my_queue.enqueue(2)
my_queue.enqueue(3)
# Dequeueing elements
removed_item = my_queue.dequeue()
# Checking the size of the queue
queue_size = my_queue.size()
print("Removed Item:", removed_item)
print("Queue Size:", queue_size)
In the above example:
Stack:
This dynamic data structure works on the opposite principle of queues. Here, the first element is the last to be removed, i.e., the operating framework follows a Last-in-First-out (LIFO) method. The endpoints from which elements are added and removed are typically called the ‘top’ of the stack.
Example:
We will explain the concept with the following illustration:
class Stack:
def __init__(self):
self.items = []
def push(self, item):
self.items.append(item)
def pop(self):
if not self.is_empty():
return self.items.pop()
def is_empty(self):
return len(self.items) == 0
def size(self):
return len(self.items)
# Creating a stack
my_stack = Stack()
# Pushing elements onto the stack
my_stack.push(1)
my_stack.push(2)
my_stack.push(3)
# Popping elements from the stack
popped_item = my_stack.pop()
# Checking the size of the stack
stack_size = my_stack.size()
print("Popped Item:", popped_item)
print("Stack Size:", stack_size)
Non-linear data structures increase element organization complexity, which enables more complicated interactions. Sequential structures place elements, but non-linear structures prefer graphs and tree branches. Trees have hierarchical nodes that branch into subtrees. Vertices joined by edges show various connections in graphs. Non-linear structures help describe complicated interactions in hierarchical or interconnected data organizations, such as decision-making systems, organizational hierarchies, and network modeling.
Tree:
A tree is a non-linear, hierarchical data structure that consists of nodes connected by edges. It has a parent node and at least one child node. Each child node can have its children, creating a branch structure. The depth of a node is equal to the length of its path from the root to other nodes in the tree.
Example:
This simple example of a binary tree with an organizational hierarchy lends clarity to the above concept:
CEO
/ \
CTO CFO
/ \ / \
Dev QA Finance HR
As we see:
Graph:
A non-linear data structure, known as a graph, is comprised of a set of nodes (vertices) that are interconnected via edges. Graphs permit more complex relationships than trees, such as interconnected structures and cycles. Graphs may be classified as undirected (edges without direction) or directed (edges with a specific direction).
Example:
Let’s take an example of a social network graph:
A -- B
| |
C -- D
This short example shows that:
Data structure operations refer to a collection of essential actions that can be executed on different data structures, determining how data is changed, inserted, deleted, and traversed.
Typical operations in data structures include insertion, which adds elements; deletion, which removes elements; and traversal, the methodical process of viewing each element.
The effectiveness and suitability of these procedures depend on the specific data structure utilized. Arrays are particularly efficient for fast insertion and retrieval due to their constant-time random access.
On the other hand, linked lists are productive at allocating dynamic memory. Comprehending and excelling in these activities is crucial for creating efficient algorithms and enhancing the performance of software applications.
Arrays vs. Linked Lists:
Comparing Stacks and Queues:
Binary Trees vs. Binary Search Trees (BSTs):
In conclusion, the fundamental principles, types, and operations of data structures have been revealed in this all-encompassing examination of data structures. This DSA tutorial has shed light on their relevance in algorithmic design and practical applications, from the complexities of linear and non-linear structures to the efficiency and adaptability of these systems. The subtleties of data structures will help aspiring programmers build the language of efficient coding and the foundation for building robust and scalable software solutions. Acquiring expertise in data structures goes beyond being a technical pursuit, as it delves into the realm of computational thinking as an art and a science.
1) How many days are required to learn data structures?
While it depends on your focus and dedication, you would typically need several weeks to master the concepts and practical implementations.
2) Which data structure is easy to learn?
Arrays are often regarded as one of the easiest data structures to understand due to their simplicity and direct representation of elements.
3) How can I learn data structures effectively?
A combination of theoretical knowledge and practical implementation through coded exercises provides an effective learning foundation.
4) Which data structures are used most?
Due to their versatility and ease of use, arrays, linked lists, and trees are the most widely used data structures.
Mukesh Kumar
Working with upGrad as a Senior Engineering Manager with more than 10+ years of experience in Software Development and Product Management.
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upGrad does not grant credit; credits are granted, accepted or transferred at the sole discretion of the relevant educational institution offering the diploma or degree. We advise you to enquire further regarding the suitability of this program for your academic, professional requirements and job prospects before enr...