Complete Python With Dsa Bootcamp + Leetcode Exercises

Master Python and Data Structures with Hands-on Projects and Coding Challenges for Tech Interviews and Beyond!

What you'll learn

Develop a solid foundation in Python, including syntax, data structures, and libraries, enabling learners to write efficient and clean code.

Gain a comprehensive understanding of fundamental data structures (such as arrays, linked lists, stacks, queues, trees, and graphs) and algorithms

Learn how to apply data structures and algorithms to solve practical problems, enhancing coding skills and preparing learners for technical interviews

Build confidence in solving coding challenges and improve problem-solving skills through hands-on exercises and interview-style questions

Requirements

Basic understanding of programming concepts (variables, loops, and conditionals).

Familiarity with Python syntax (data types, functions, and modules).

No prior knowledge of data structures or algorithms is required; eagerness to learn is essential.

Description

Welcome to the "Complete Python with DSA Bootcamp"! This comprehensive course is designed to take you from a beginner to a confident programmer, mastering both Python and essential data structures and algorithms (DSA) needed for technical interviews and real-world applications.What You Will LearnIn this bootcamp, you will start with the fundamentals of Python programming. You will become familiar with Python syntax, data types, control structures, and functions. As you progress, you will dive into more advanced topics, including object-oriented programming and error handling, ensuring you have a solid foundation before moving on to data structures.Next, we will explore various data structures in detail. You will learn about arrays, lists, stacks, queues, linked lists, trees, and graphs. For each data structure, you will understand its use cases, advantages, and limitations. You will also implement these structures from scratch, reinforcing your understanding through practical exercises.Algorithms are the backbone of problem-solving in programming. This course covers essential algorithms, including sorting (quick sort, merge sort) and searching (binary search), as well as more advanced topics like recursion and dynamic programming. You will learn to analyze the time and space complexity of algorithms, helping you to choose the most efficient solution for any problem.Hands-On Projects and Coding ChallengesThroughout the course, you will engage in hands-on projects and coding challenges that simulate real-world scenarios. Each section includes practical exercises to reinforce your learning, and you will work on projects that consolidate your understanding of Python and DSA. By the end of the course, you will have a portfolio of projects to showcase your skills to potential employers.Who This Course Is ForThis course is ideal for beginners who want to learn Python and data structures from scratch. It’s also perfect for aspiring software developers and data scientists preparing for technical interviews, as well as professionals looking to transition into tech roles. Whether you’re a student or a working professional, this course will equip you with the skills and knowledge needed to excel in coding interviews and advance your career.Course StructureThe course is structured into modules that progressively build on your knowledge. Each module contains video lectures, reading materials, and coding exercises, allowing you to learn at your own pace. You will also have access to a community of learners where you can ask questions, share insights, and collaborate on projects.Why Choose This Course?Comprehensive Curriculum: Covers Python programming, data structures, and algorithms in depth.Expert Instructor: Learn from an experienced instructor with over 13 years in data analytics and teaching.Hands-On Approach: Engage in practical exercises and real-world projects that reinforce your learning.Flexible Learning: Access course materials anytime, anywhere, and learn at your own pace.Join the "Complete Python with DSA Bootcamp" today and take your first step towards becoming a proficient programmer! Whether you aim to land a job in tech or simply want to enhance your coding skills, this course is your gateway to success. Enroll now and start your journey!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: IDE's And Code Editors You Can Use

Lecture 2 Getting Started With Google Colab

Lecture 3 Getting Started With Github Codespace

Lecture 4 Anaconda And VS Code IDE Installation

Lecture 5 Anaconda Installation In Mac

Lecture 6 Anaconda Installation In Linux

Section 3: Getting Started With Python Programming Language

Lecture 7 Getting Started With VS Code

Lecture 8 Python Basics- Syntax and Semantics

Lecture 9 Variables In Python

Lecture 10 Basic Datatypes In Python

Lecture 11 Operators In Python

Section 4: Python Control Flow

Lecture 12 Conditional Statements (if,elif,else)

Lecture 13 Loops In Python

Section 5: Inbuilt Data Structures In Python

Lecture 14 List And List Comprehrension In Python

Lecture 15 Sets In Python

Lecture 16 Dictionaries In Python

Lecture 17 Tuples In Python

Lecture 18 Real World Usecases Of List

Section 6: Functions In Python

Lecture 19 Getting Started With Functions

Lecture 20 More Coding Example With Functions

Lecture 21 Python Lambda Functions

Lecture 22 Map functions In Python

Lecture 23 Filter Function In Python

Section 7: Flowchart and Problem Solving

Lecture 24 Introduction to Flowcharts

Lecture 25 What is a Pseudocode ?

Lecture 26 Framework to Solve a Problem

Section 8: Inbuilt Data Structure : Practice Questions

Lecture 27 A Guide to attempting Coding Exercises

Section 9: Searching and sorting Algorithm

Lecture 28 Introduction to Arrays in Python

Lecture 29 Linear Search

Lecture 30 Bubble Sort - Implementation

Lecture 31 Binary Search Algorithm

Lecture 32 Bubble Sort - Explanation and visualisation

Lecture 33 List as Dynamic Array

Lecture 34 Coding Custom List- Part 1

Lecture 35 Coding Custom List - Part 2

Lecture 36 Selection Sort - Explanation and Visulization

Section 10: Binary Search Practice Questions

Section 11: List Practice Questions

Section 12: Practice Questions : 2D List

Section 13: Importing Creating Modules And Packages

Lecture 37 Import Modules And Packages In Python

Lecture 38 Standard Library Overview

Section 14: File Handling In Python

Lecture 39 File Operation In Python

Lecture 40 Working With File Paths

Section 15: Exception Handling In Python

Lecture 41 Exception Handling With Try Except And Finally Blocks

Section 16: OOPS Concepts With Classes And Objects

Lecture 42 Classes And Objects In Python

Lecture 43 Inheritance In OOPS

Lecture 44 Polymorphism In OOPS

Lecture 45 Encapsulation In OOPS

Lecture 46 Abstraction In OOPS

Lecture 47 Magic Methods In Python

Lecture 48 Operator Overloading In Python

Lecture 49 Custom Exception Handling

Section 17: Practice Questions OOPS

Section 18: More Advanced Python Topics

Lecture 50 Deep Dive Into Iterators In Python

Lecture 51 Generators With Practical Implementationn And Usecases

Lecture 52 Deep Dive Into Function Copy,Closures and Decorators

Section 19: Data Structure : Linked List

Lecture 53 Introduction To Data Structure

Lecture 54 Intro To Linked List

Lecture 55 Create Linked List

Lecture 56 Print LL

Lecture 57 Take Input of Linked List - I

Lecture 58 Take Input of Linked List II

Lecture 59 Take input of Linked List - Optimized

Lecture 60 Length Of Linked List

Lecture 61 Linked List Operations

Lecture 62 Insert at Head

Lecture 63 10. Insert at Tail.mp4

Lecture 64 11. HW - Insert at Tail - Recursive

Lecture 65 12. Insert at Index- Iteratively

Lecture 66 13. HW - Insert at Index - Recursion

Lecture 67 14. Delete a Node - Head

Lecture 68 15. Delete a Tail Node

Lecture 69 (HW) Delete Tail Recursively

Lecture 70 Delete Node at Given Index

Lecture 71 (HW) Delete a Node Recursively

Lecture 72 Delete Node by Value

Lecture 73 Delete a Node in LL

Lecture 74 Search in LL By Value

Lecture 75 (HW) Search by Index

Lecture 76 Array vs Linked List

Lecture 77 Linked List Class

Section 20: Linked List II

Lecture 78 Middle of LL

Lecture 79 Middle of LL - 2 pointer method

Lecture 80 Merge two Sorted Linked List

Lecture 81 Reverse a LL (Recursive)

Lecture 82 Reverse LL Optimized (Recursion)

Lecture 83 Reverse Linked List (Iteration)

Lecture 84 Merge Sort Linked List

Lecture 85 Types of Linked List

Section 21: Linked List Practice Questions

Section 22: Stacks

Lecture 86 Introduction To Stack

Lecture 87 Stack - LIFO Principle

Lecture 88 Operations on Stack

Lecture 89 Stack Implementation using List

Lecture 90 Visualizing Stack Using List

Lecture 91 Stack using Linked List

Lecture 92 Stack Using LL - Optimized

Lecture 93 Stack Using LL Implementation

Section 23: Queues

Lecture 94 Introduction To Queue

Lecture 95 Operations in Queue

Lecture 96 Queue with Inbuilt List

Lecture 97 Queue using List - Implementation

Lecture 98 Queue Using Linked list

Lecture 99 Queue Using LL (Implementation)

Lecture 100 Types Of Queue

Section 24: Practice Questions - Stack and Queues

Section 25: Trees : Generic Trees

Lecture 101 Introduction To Trees

Lecture 102 Tree Examples and Applications

Lecture 103 Terminologies in a Tree

Lecture 104 Defining a TreeNode

Lecture 105 Print Tree

Lecture 106 Print Tree Detailed

Lecture 107 Take Input (Recursively)

Lecture 108 Take Input Level Wise

Lecture 109 Count Nodes in a Tree

Lecture 110 Height of a Tree

Lecture 111 Traversal in a Tree

Section 26: Generic Trees Practice Questions

Section 27: Binary Trees

Lecture 112 Introduction To Binary Tree

Lecture 113 Binary Tree Node

Lecture 114 Print Binary Tree

Lecture 115 Take Input Binary Trees

Lecture 116 Take Input level Wise

Lecture 117 Diameter of Tree

Lecture 118 Diameter of Tree - Optimised

Lecture 119 IsBalanced binary Tree

Lecture 120 Traversals in Binary Tree

Lecture 121 Construct Tree from Preorder and Inorder

Lecture 122 Construct Tree from Preorder and Inorder - Solution

Lecture 123 Construct a tree from inorder and postorder

Lecture 124 Types of Binary Tree

Section 28: Binary Tree Practice Questions

Section 29: Binary Search Tree (BST)

Lecture 125 Introduction To BST

Lecture 126 BST Node and Print

Lecture 127 Search in a BST

Lecture 128 Sorted List to BST

Lecture 129 Check BST

Lecture 130 Check BST Optimized

Lecture 131 Print Elements in a range

Lecture 132 Check BST using Limits

Lecture 133 BST Class - Search

Lecture 134 BST Class - Insert Function

Lecture 135 BST Class - Delete Method

Lecture 136 BST Class - Complexity

Lecture 137 Balancing a Tree

Section 30: BST Practice Questions

Section 31: Hashmaps

Lecture 138 Introduction to Hashmaps

Lecture 139 Why Hashmaps ?

Lecture 140 Inbuilt Hashmap in Python

Lecture 141 Hashmap/Dictionaries Questions

Lecture 142 Implementing our own hashmap - Hashing

Lecture 143 Collision Handing

Lecture 144 Open Addressing - Insert and Search

Lecture 145 Open Addressing - Delete

Lecture 146 Hashmap Implementation - Chaining (Linked List Class)

Lecture 147 Hashmap Chaining Implementation

Lecture 148 Complexity Analysis of our Implemented Hashmap

Lecture 149 Implementing Rehashing in our Hashmap

Section 32: Hashmap Practice Questions

Section 33: Python For Data Analysis

Lecture 150 Working With Numpy With Python

Lecture 151 Pandas Dataframe And Series

Lecture 152 Data Analysis And Manipulation

Section 34: Data Visualization With Python

Lecture 153 Read Data From Various Data Scources

Lecture 154 Data Visualization With Matplotlib

Section 35: Working With Sqlite And Python

Lecture 155 Data Visualization With Seaborn

Lecture 156 Sqlite With Python

Section 36: Graph : Practice Question

Section 37: Introduction To MultiThreading With Python

Lecture 157 What is Process And Threads

Lecture 158 MultiThreading Practical Impelemntation

Lecture 159 Multiprocessing With Python

Lecture 160 Thread Pool Executor And Process Pool

Lecture 161 Webscraping Usecases With Multithread

Lecture 162 Factorial Usecase With Multi Processing

Section 38: Logging In Python

Lecture 163 Logging In Python

Lecture 164 Loggign With Multiple Loggers

Lecture 165 Logging Implementation With a real World Example

Section 39: Dynamic Programming : Practice Question

Section 40: Introduction To Flask Framework

Lecture 166 Introduction To Flask Framework

Lecture 167 Understanding A Simple Flask Web Application

Lecture 168 Integrating HTML With Flask

Lecture 169 HTTP Verbs GET And Post

Lecture 170 Building Dynamically Url Jinja 2

Lecture 171 Put Delete And API's In Flask

Beginners looking to learn Python and data structures from scratch.,Aspiring software developers and data scientists preparing for technical interviews in product based companies,Students seeking to enhance their programming skills and problem-solving abilities.,Professionals transitioning to roles in tech who want a solid foundation in algorithms and data structures.