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Data Structures & Algorithms with Python Training

Live Online Course

4.7/5 Ratings 4.7/5Ratings

30 Sessions

Live Classes

48 Videos

Video Course

52 Notes

Quick Notes

145 Labs

Hands-on Labs

Data Structures and Algorithms with Python Course Overview

The Data Structures and Algorithms with Python Training is designed for people interested in software development who want to advance their careers and succeed in interviews with top worldwide businesses. This training program will help you prepare for these important interviews by teaching key problem-solving techniques, coding methodologies, and more. You'll gain hands-on experience by working on over 100 data structures and algorithms-related issues. You'll begin the course by learning how to tackle challenges involving each data structure and method. Prepare for top product-based companies such as Meta, Microsoft, Amazon, Adobe, Netflix, and Google.

DSA Course objectives

Participants in this training will master data structures and algorithms using Python, and by the end of the course, attendees will be able to:

Identify techniques to design algorithms and evaluate their efficiency.
Sort data using bubble sort, selection sort, insertion sort, shell sort, quick sort, and merge sort.
Search data using linear search and binary search algorithms.
Implement and use hashing techniques for fast data retrieval.
Solve problems using arrays, lists, linked lists, queues, and stacks.
Tackle problems using non-linear data structures such as trees and graphs.

Outcomes of the Training

Mastery of backtracking & dynamic programming techniques.
Comprehensive understanding of hashing and its real-world applications.
Enhance the advancement of your technological career.
In technical interviews, get an advantage over other applicants.
Individualized goal-setting help and career coaching.
Expect a 40-60% pay increase after completing the course.

Why Learn Data Structures and Algorithms in 2024?

DSA is a fundamental skill for careers in software development, data science, Machine learning, and many more. These stats back the statement.

Strong Foundation for Programming: The DSA forms the core of computer science. Python programmers may create more effective code and solve problems more quickly by learning about data structures and algorithms, which are two aspects of data processing.
Problem-Solving Expertise: DSA gives you a methodical framework for problem-solving, enabling you to approach challenging Python tasks with more assurance.
Benefits Across Programming Domains: A strong grasp of DSA in Python is beneficial in all areas of programming, including web development, machine learning models, mobile apps, and data analysis tools.
Interview Preparation: Top IT employers place a strong emphasis on DSA during technical interviews; a common language to showcase these abilities is Python.
Career Growth: In the tech sector, being proficient in DSA with Python is highly valued and leads to opportunities in software engineering, data science, system architecture, technical leadership, & other fields.
Adaptability in a Rapidly Changing Field: As technology advances, it becomes simpler to adjust to new programming languages and environments because the fundamentals of DSA remain the same.

Why Learn DSA?

Data Structures and Algorithms Career Scope

DSA skills are employed by the world's leading product-based companies. Discover the salary and skills to become an SDE.

Software Engineering: Building scalable data structures, optimizing Python code for memory management, and building effective algorithms all depend on your ability to handle DSA with Python.
Data Science and Analytics: Python is a popular tool used by data scientists and analysts for activities requiring a good understanding of DSA, such as feature extraction, statistical analysis, machine learning, and predictive modeling.
Machine Learning and Artificial Intelligence: Building machine learning models and AI systems with Python requires proficiency in DSA, which makes model training, data processing, and performance optimization efficient.
System Design and Architecture: To maximize system performance, efficiently manage resources, and guarantee fault tolerance and high availability, system architects and engineers employ data structures and algorithms based on Python.
Competitive Programming: To solve issues quickly, adhere to limits, and produce the best answers, competitive programmers need to have strong DSA Python abilities.

DSA with Java Career Scope

Tools and Technologies Covered

Java

OOPs

Logic Building

Code Debugging

Arrays

Big-O

Complexity Analysis

Linked List

Stack

Queue

Tree

Graph

VS Code IDE

GitHub

Interview Prep

Data Structures and Algorithms with Python Course Key Features

3 Months of Intensive Live Training
Learn from SDEs in Product Based Companies
Learn to implement DSA

Career Coaching
Resume & Portfolio Building
Job Assistance

Data Structures and Algorithms Course Eligibility

Pre-requisites

Prerequisites for enrolling in the Data Structures and Algorithms (DSA) with Python Training include a strong understanding of Python and a background in computer science.

Who can Join the DSA Course?

Students in college, recent graduates, and working professionals preparing for software development engineer (SDE) interviews with leading organizations like Microsoft, Amazon, Adobe, and other top product-based companies should enroll in the Data Structures and Algorithms with Python course.

Included in this Data Structures & Algorithms with Python Training

Live Sessions30 Classes
Video Course 48 Videos
Quick Notes 52 Notes
Labs 145 labs
Skill Tests 0 Tests

DSA Foundation

Data Structures

Introduction to Data Structure
Types of Data Structure
Abstract Data Type

Complexity Analysis

Complexity Analysis
Time complexity
Space complexity
Big O Notation
Iteration
Recursion
Exercises and Summary

Arrays and Strings

Arrays
Arrays Types
Strings
Iterations
Exercises and Summary

Sorting Algorithms

Bubble Sort
Insertion Sort
Selection Sort
Quick Sort
Merge Sort
Heap Sort
Count Sort
Bucket Sort
Exercises and Summary

Searching Algorithms

Introduction
Linear Search and its Implementation
Binary Search and its Implementation
Ternary Search and its Implementation
Jump Search and its Implementation
Exponential Search and its Implementation
Exercises and Summary

String Algorithms

Introduction
Count Vowels
Reverse a String
Reverse Words
Rotations
Remove Duplicates
Most Repeated Char
Sentence Capitalization
Exercises and Summary

Lists

Linked List
Singly Linked List
Doubly Linked List
Circular Linked List
Exercises and Summary

Stacks

Introduction
What are stacks
Working with Stacks
Building Stack using an Array
Exercises and Summary

Queues

What are queues
Working with Queues
Building Queues using an Array
Implementing Queues using a Stack
Priority Queues with different approaches
Exercises and Summary

Hash

What are Hash Tables
Working with Hash Tables
Hash Functions
Collision and Chaining
Building a Hash Table
Exercises and Summary

DSA Advanced

Binary Trees

What are Trees
Populating a Binary Tree
Building a Binary Tree
Traversing Trees
Working with Recursion
Depth First Traversals
Depth and Height of Nodes
Minimum values in a Tree
Level Order Traversal
Exercises and Summary

AVL Trees

Introduction
Balanced and Unbalanced Trees
Implementing Rotations
Building AVL Trees
Exercises and Summary

Heaps

What are Heaps
Working with Heaps
Building and Implementation Heaps
Heap Sort and its Implementation
Implementing Priority Queues
Working with Heapify
Exercises and Summary

Tries

What are the Tries?
Populating a Trie?
Implementation Tries with Hash Table
Abstraction and Traversal
Exercises and Summary

Graphs

What are Graphs
Adjacency Matrix and Adjacency List
Building Graphs
Adding Nodes and Edges
Removing Nodes and Edges
Traversal Algorithms
Depth-First Traversal
Breadth-First Traversal
Topological Sorting
Cycle Detection using Directed Graphs
Exercise and Summary

Undirected Graphs

Introduction
Weighted Graphs
Dijkstra’s Shortest Path Algorithm
Shortest Path Algorithm
Cycle Detection using Undirected Graphs
Minimum Spanning Tree
Prim’s Algorithm
Exercises and Summary

Greedy Algorithms

Greedy Algorithms
The strategies of Greedy Algorithms
Applications of Greedy Algorithms
Exercises and Summary

Divide and Conquer

Divide and Conquer Techniques
Master theorem Applications
Sliding Window problems
Merge Intervals problems
Exercises and Summary

Backtracking

Backtracking
Brute Force Approach
N Queens Problem
KMP
Exercises and Summary

Dynamic Programming

Dynamic Programming
Approaches of Dynamic Programming
Top-down approach
Bottom-up approach
Pattern matching algorithm
Exercises and Summary

Download Curriculum

Introduction to Java

Java Introduction Preview 0h 06m 55s

JRE JVM and JDK 0h 07m 52s

Demo- Hello Word Program 0h 04m 44s

Variables

Variables 0h 07m 25s

DataTypes

Data Types 0h 12m 38s

Data Types Difference 0h 05m 36s

Type Casting 0h 02m 21s

Demo-Type Casting 0h 10m 21s

Wrapper Classes

Wrapper Classes 0h 01m 58s

Demo-Wrapper Classes 0h 08m 25s

Course Overview

Course Overview 0h 00m 38s

For Loop

Loops-Introduction 0h 01m 56s

For Loop 0h 09m 00s

while And Do While

While Loop 0h 06m 03s

Do While Loop 0h 06m 08s

For Each Loop 0h 05m 10s

Jump Statement

Jump Statement 0h 06m 35s

Arrays

Arrays Agenda 0h 00m 42s

Array Introduction 0h 06m 11s

Single Dimensional Array-Example 0h 04m 25s

Multi Dimensional Arrays-Example 0h 06m 49s

Strings

String Agenda 0h 00m 49s

String Introduction 0h 07m 58s

strings Methods 0h 05m 14s

Oops java Introduction

Course Overview 0h 00m 52s

Oops Introduction 0h 03m 55s

Class and Objects

Class and Objects 0h 03m 46s

Demo Class and Objects 0h 10m 07s

Encapsulation

Encapsulation 0h 05m 22s

Access Modefire 0h 07m 55s

Getter and Setter Methods 0h 07m 42s

Constructor

Constructor 0h 08m 05s

Parameterized Constructors 0h 08m 24s

Constructor Overloading 0h 05m 07s

Constructor Chaining 0h 04m 35s

Inhertiance

Inhertiance 0h 03m 58s

Demo Inheritance 0h 05m 48s

Types of Inheritance 0h 16m 44s

Constructor Chaining with Inheritance 0h 11m 13s

Polymorphism

Polymorphism 0h 03m 54s

Method Overloading 0h 08m 11s

Can We Overload Main method? 0h 04m 09s

Demo Method Overloading 0h 14m 22s

Interfaces

Abstract class and Abstract Method 0h 14m 47s

Interfaces 0h 11m 57s

DSA and Big O

Data Structure Introduction Preview 0h 04m 32s

Non-Linear Data Structure 0h 09m 25s

Asymptotic analysis 0h 14m 31s

Asymptotic Explanation 0h 20m 23s

Array

Array Introduction 0h 09m 58s

Array Traversal 0h 10m 46s

Array insert data 0h 06m 21s

Array Bound Checking 0h 16m 52s

Array Insertion 0h 12m 35s

Deletion in Array 0h 07m 38s

2D array 0h 13m 47s

Array List Integer 0h 20m 47s

Array Questions

Array List String 0h 18m 17s

Storing Heterogeneous Element in array 0h 13m 20s

Array Question1 0h 41m 40s

Array Question2 0h 18m 48s

Sum Leet code 0h 19m 24s

buy and sell Stock leetcode 0h 10m 34s

Product of Array Except Self 0h 16m 56s

Container With Most Water 0h 19m 45s

Spiral Matrix 0h 30m 10s

Sudoku valid 0h 29m 36s

String

String Preview 0h 01m 00s

String literals 0h 09m 15s

Immutable String 0h 05m 33s

String Methods 0h 21m 33s

StringBuilder vs String Buffer 0h 04m 42s

String Builder and Buffer example 0h 11m 29s

Valid Palindrome 0h 08m 05s

Valid Anagram 0h 15m 23s

Longest Substring Without Repeating Characters 0h 17m 22s

Find All Anagrams in a String 0h 31m 07s

Largest Number 0h 19m 26s

Largest Number 1 0h 09m 06s

Linear Search

Linear search1 Preview 0h 01m 42s

Linear Search Implementation 0h 08m 04s

Linear Search Questions 0h 25m 56s

Binary Search

Binary Search 0h 01m 34s

Binary Search implementation 0h 12m 19s

First and Last Occurence 0h 23m 33s

Arrays Searching

Search in Rotated Sorted array 0h 19m 31s

Search in rotated Sorted Array 0h 10m 39s

Find Minimum in Rotated Sorted Array 0h 04m 59s

Bubble, Selection and Insertion Sort

Bubble Sort 0h 01m 28s

Bubble Sort Implementation 0h 07m 33s

Selection Sort 0h 02m 05s

Selection Sort Implementation 0h 07m 15s

Insertion Sort 0h 01m 06s

Insertion Sort implementation 0h 10m 18s

Merge, Quick and Counting Sort

Merge Sort 0h 09m 58s

Merge Sort Implementation 0h 20m 22s

Quick Sort 0h 08m 51s

Quick Sort Implementation 0h 08m 51s

Counting Sort 0h 02m 46s

Counting Sort Implementation 0h 17m 57s

Bucket, Find Kth Element and Find Pages

Bucket Sot 0h 01m 03s

Bucket Sort Implementation 0h 15m 47s

FindKthElement 0h 19m 26s

Kth largest element 0h 22m 27s

Find Pages 0h 09m 33s

Find Pages PART 2 0h 20m 01s

Theif problem 0h 13m 19s

Minimum swaps 0h 15m 06s

Hashing

Hashing Introduction Preview 0h 02m 35s

Hasing Function 0h 17m 35s

Hash map or Hash table 0h 18m 34s

Phonebook

Phonebook 1 0h 19m 01s

Phonebook 2 0h 02m 26s

Phonebook by HashMap 0h 13m 43s

HashMap different methods 0h 21m 13s

Symmetric pair Example 0h 14m 27s

First Unique Character 0h 13m 51s

Hash Set

HashSet Example 0h 12m 31s

Contiguous Integers in array 0h 16m 38s

Pair with given Sum 0h 07m 40s

Itinary fromticket 0h 30m 59s

Match Locks and Keys 0h 12m 34s

Stack

Stack 0h 03m 38s

Stack Implementation by library 0h 14m 58s

Stack Implementation using Array 0h 23m 16s

Stack Implementation using LinkedList 0h 22m 09s

Valid Parentheses 0h 16m 36s

Simplify Path 1 0h 12m 27s

Simplify path 2 0h 09m 27s

Evaluate Reverse Polish Notation 0h 25m 12s

Trapping Rainwater 0h 24m 04s

Longest valid Parentheses 0h 24m 13s

Basic Calculator 0h 26m 05s

Queue

Introduction to Queue Preview 0h 04m 49s

Queue Implementation 0h 13m 16s

Queue Implementation user defined function 0h 28m 12s

Implement Stack using Queues 0h 25m 32s

Implement Queue using Stack 0h 21m 23s

First Unique Character in a String 0h 26m 01s

Reveal Cards in Increasing Order 0h 19m 32s

Find the Winner of the Circular Game 0h 17m 23s

Linked List

Linked List 0h 05m 25s

Singly LinkedList 0h 12m 51s

LinkedList Library 0h 19m 01s

Singly LinkedList Implementation 1h 10m 14s

Doubly LinkedList 0h 05m 30s

Doubly linkedList Implementation 0h 54m 15s

Circular LinkedList

Circular LinkedList 0h 02m 27s

Circular LinkedList 1 0h 02m 58s

Circular LinkedList implementation 0h 51m 31s

Linked List Cycle 0h 10m 34s

Linked List Cycle II 0h 21m 03s

Reverse Linked List

Happy Numbers 0h 15m 50s

Middle of the Linked List 0h 07m 10s

Reverse Linked List 0h 13m 33s

Reverse Linked List II 0h 28m 35s

Reverse Nodes in k-Group 0h 23m 33s

Tree and Binary Tree

Introduction to Tree 0h 23m 00s

Binary Tree Traversal Whiteboard 0h 55m 45s

Traversal DFS 0h 38m 15s

Tree Traversal BFS 0h 16m 01s

Max Min Search SumofLeafNodes 0h 26m 02s

White Board Explanation of Max,Min ,SumOfLeafNodes 0h 12m 14s

Mirror a Tree (Code) 0h 19m 36s

Spiral level Order traversal 1 0h 30m 50s

Spiral level Order traversal 2 0h 01m 33s

Quick Notes Notes (52)

Data Structures & Algorithms