Data Structures and Algorithms: In-Depth DSA using Python

Name: Data Structures and Algorithms: In-Depth DSA using Python
Rating: 4.5 (4080 reviews)

Learn Python Data Structures, Algorithms (DSA) with Animated Examples, includes Dynamic Programming, Backtracking etc...

Role Play

Created bySyed Mohiuddin

Last updated 11/2025

English

What you'll learn

Learn Data Structures, Abstract Data Types and their implementation in Python
Implementation of Searching Algorithms in Python
Implementation of Stacks, Queues, Linked List, Binary Trees, Heaps and Graphs in Python
Implementation of Binary Tree Traversal Techniques in Python
Graph traversals techniques ie Depth First Search and Breadth-First Search in Python
Implementation of Sorting Algorithms in Python
Enhance Analytical Skill and efficiently use searching and sorting algorithms in real applications

Course content

52 sections • 433 lectures • 54h 24m total length

Course Introduction3:12
Get the most out of this course2:40
Why we need Data Structure ?2:35
Why Learn Algorithms ?5:12
Abstract Data Type (ADT)9:17
Python Installation on Windows5:39
PyCharm (IDE) Installation on Windows8:50

First Python Program, Data Types and Variables26:00
Reading Input from User5:16
Numbers - Integers & Floating Point6:22
Arithmetic Operations and Expressions23:42
Boolean & None Data Types3:39
Strings8:29
Comparison or Relational Operators5:58
if, if-else and elif Statements33:22
Logical Operators and Comparison Statements17:35
Chaining Comparison Operators3:54
Membership Operators and If Statement4:35
For loop and the range() Function23:11
For Loops with Break, Continue and Pass Statements17:04
For Loop with else Clause5:42
While Loops8:13
While Loops with Break, Continue and Pass Statements22:11
While Loop with else Clause7:01
What are Lists?8:00
Using Lists and List Indexing5:08
What are Tuples ?4:24
Tuple Indexing4:45
Membership & Identity Operators2:19
What are Dictionaries?4:26
Using Dictionaries8:13
What are Functions?8:10
Writing Functions in Python?4:48
Importing Modules in Python7:32
Creating Your Own Modules6:37
Fundamentals of Object Oriented Programming2:49
Defining Classes & Creating Objects12:44
More on __init__ Method (Constructor)3:06
Understanding self Parameter3:11
Static and Local Variables6:49

Sorting Introduction2:45
Stable and Unstable Sorting3:20
Selection Sort - Explanation, Algorithm and Analysis11:32
Selection Sort - Implementation6:05
Insertion Sort - Explanation, Algorithm and Analysis14:04
Insertion Sort - Implementation5:14
Bubble Sort - Explanation, Algorithm and Analysis16:49
Bubble Sort - Implementation5:42
Shell Sort - Explanation, Algorithm and Analysis13:01
Shell Sort - Implementation6:15
Merge Sort8:28
Merge Sort - Algorithm5:40
Merging - Algorithm11:36
Merge Sort - Complexity Analysis3:38
Merge Sort - Implementation10:13
Quick Sort28:31
Quick Sort - Algorithm5:36
Quick Sort - Complexity Analysis6:43
Quick Sort - Implementation10:39
Count Sort - Explanation, Algorithm and Analysis16:21
Count Sort - Implementation7:37
Radix Sort - Explanation, Algorithm and Analysis17:57
Radix Sort - Implementation9:32
Python's Built-in Sorting Functions5:16
Sorting Algorithms - Summary of Complexities7:58

Why do we use Linked List17:09
Creating Node of Linked List12:28
Playing with the links of Linked List6:31
How to Create Linked List5:09
Displaying or Traversing Linked List8:25
Lets Implement Creating and Displaying Linked List20:48
Searching Element in Linked List7:09
Lets Implement Search in Linked List5:24
Insert Element at the Beginning of Linked List4:59
Lets Implement Insert Element at the Beginning of Linked List5:50
Insert Element Anywhere in between the Linked List9:45
Lets Implement Insert Element Anywhere in Between the Linked List6:11
Delete Element at Beginning of Linked List6:09
Lets Implement Delete Element at Beginning of the Linked List5:09
Delete Element at End of Linked List8:15
Lets Implement Delete Element at End of Linked List5:55
Delete Element Anywhere in between Linked List7:22
Lets Implement Delete Element Anywhere in between Linked List5:38
Circular Linked List5:02
Creating Circular Linked List5:54
Traversing Circular Linked List8:50
Lets Implement Creating and Displaying Circular Linked List10:54
Insert Element at the Beginning of Circular Linked List4:41
Lets Implement Insert Element at the Beginning of Circular Linked List5:02
Insert Element Anywhere in between the Circular Linked List7:53
Lets Implement Insert Element Anywhere in between the Circular Linked List5:19
Delete Element at Beginning of Circular Linked List5:13
Lets Implement Delete Element at Beginning of Circular Linked List6:37
Delete Element at End of Circular Linked List7:15
Lets Implement Delete Element at End of Circular Linked List6:23
Delete Element Anywhere in between Circular Linked List7:14
Lets Implement Delete Element Anywhere in between Circular Linked List5:10
Double Linked List1:22
Creating Node of Doubly Linked List12:53
Playing with links of Doubly Linked List8:05
Creating Doubly Linked List5:33
Traversing Doubly Linked List4:44
Lets Implement Creating and Displaying Doubly Linked List17:37
Insert Element at the Beginning of Doubly Linked List4:53
Lets Implement Insert Element at the Beginning of Doubly Linked List5:15
Insert Element Anywhere in between the Doubly Linked List8:20
Lets Implement Insert Element Anywhere in between the Doubly Linked List6:16
Delete Element at Beginning of Doubly Linked List5:31
Lets Implement Delete Element at Beginning of Doubly Linked List5:18
Delete Element at End of Doubly Linked List4:30
Lets Implement Delete Element at End of Doubly Linked List4:51
Delete Element Anywhere in between Doubly Linked List7:16
Lets Implement Delete Element Anywhere in between Doubly Linked List6:14

Trees Definition and Properties5:54
Trees - Terminology6:33
Trees - Height and Levels3:21
Degree of Node and Tree3:43
Binary Trees and it's Properties12:46
Proper Binary Tree5:20
Full Binary Tree6:30
Complete Binary Tree7:17
Full Vs Complete Vs Proper Binary Tree7:55
Binary Tree Representation - Array Based14:43
Binary Tree Representation - Linked Based7:19
Traversing Binary Trees1:34
Binary Trees Traversal - Preorder5:33
Binary Trees Traversal - Inorder5:23
Binary Trees Traversal - Postorder5:26
Binary Trees Traversal - Level Order3:48
Easy way of remembering Binary Trees Traversals3:04
Creating Binary Trees15:37
Function for Preorder Traversal9:51
Function for Inorder Traversal9:29
Function for Postorder Traversal10:07
Lets Implement Creating Binary Trees6:00
Lets Implement Traversing Binary Trees6:02
Lets Create Binary Trees6:37
Lets Create Binary Trees - Another Example5:04
Function for Level Order Traversal8:40
Lets Implement Level Order Traversal6:49
Count Number of Nodes in Binary Tree14:10
Lets Implement Count Operations of Binary Tree3:44
Find Height of Binary Tree13:44
Lets Implement Height Operations of Binary Tree4:19

Requirements

Prior knowledge of Programming any high level language
Basic knowledge of Python Programming

Description

This course will help you in better understanding of the basics of Data Structures and how algorithms are implemented in Python. This course consists of Videos which covers the theory concepts + implementation in python.

There’s tons of concepts and content in this course:

Basics of data structures & Algorithms
Analysis of Algorithms (Big O, Time and Space complexity)
Recursion & Analysis of Recursive Algorithms
Searching Algorithms
Sorting Algorithms
Linked List
Stacks
Queues
Binary Trees
Binary Search Trees
Balanced Binary Search Trees
Priority Queues and Heaps
Hashing
Graphs
Graph Traversal Algorithms

Followed by Advanced Topics of Algorithms:

Sets and Disjoint Sets
Divide and Conquer Approach - Introduction
Divide and Conquer - Binary Search
Divide and Conquer - Finding Maximum and Mininum
Divide and Conquer - Merge Sort
Divide and Conquer - Quick Sort
Divide and Conquer - Selection Algorithm
Divide and Conquer - Strassens Matrix Multiplication
Divide and Conquer - Closest Pair
Divide and Conquer - Convex Hull
Greedy Method - Introduction
Greedy Method - Knapsack Problem
Greedy Method - Job Sequencing with Deadlines
Greedy Method - Mininum Cost Spanning Tree (Prim's & Kruskal's Algorithms)
Greedy Method - Optimal Storage on Trees
Greedy Method - Optimal Merge Pattern
Greedy Method - Single Source Shortest Path (Dijkstra's Algorithm)
Dynamic Programming - Introduction
Dynamic Programming - Multistage Graphs
Dynamic Programming - All Pairs Shortest Path
Dynamic Programming - Single Source Shortest Path
Dynamic Programming - Optimal Binary Search Trees
Dynamic Programming - 0/1 Knapsack Problem
Dynamic Programming - Reliability Design
Dynamic Programming - Travelling Salespersons Problem
Backtracking - Introduction
Backtracking - n-Queesn Problem
Backtracking - Sum of Subsets Problem
Backtracking - Graph Coloring Problem
Backtracking - Hamiltonian Cycles Problem
Backtracking - 0/1 Knapsack Problem
Branch & Bound - Introduction
Branch & Bound - n-Queens Problem
Branch & Bound - Job Sequencing Problem
Branch & Bound - 0/1 Knapsack Problem

Again, each of these sections includes detailed videos tutorial.

Who this course is for:

Students who want to have better understanding of Data Structures
Python programmers curious about Data Structures
IT Professional experimenting implementation of Data Structures in Python

Data Structures and Algorithms: In-Depth DSA using Python

What you'll learn

Explore related topics

Course content

Course Overview7 lectures • 37min

Bonus: Python Crash Course (Basics and Fundamentals)33 lectures • 5hr 15min

Analysis of Algorithms9 lectures • 43min

Recursion and Analysis of Recursive Functions11 lectures • 1hr 35min

Searching Algorithms6 lectures • 47min

Sorting Algorithms25 lectures • 4hr 1min

Linked List48 lectures • 5hr 48min

Stacks5 lectures • 1hr 4min

Queues and DEque8 lectures • 1hr 27min

Binary Trees31 lectures • 3hr 46min

Requirements

Description

Who this course is for: