Complete DSA in Python One Shot | DSA in Python from Basics To Advance

Video Description

⚠️Use DECODE20 To Get Extra 20% Off on All decode Batches 🌐Enroll Now :- https://pwskills.com/programming-courses/ In this One Shot video, you’ll learn Complete DSA in Python — starting from the basics and going all the way to advanced concepts. Whether you're a beginner or preparing for coding interviews, this session will help you understand logic building, problem-solving, and writing clean Python code. Perfect for students, job seekers, and anyone aiming to master DSA quickly and effectively. Lecture Timestamps 00:00 - Importance of DSA 04:20-Introduction to Python (Popularity, Simple Syntax, Vast Library Support) 05:41-Video Agenda and Goals (Overview of the entire 10-section course) 08:35-Introduction to DSA (Data Structures and Algorithms) (Why DSA is important, Data is the most valuable thing, The need for Data Structures and Algorithms) 16:15-Why is DSA asked in interviews? (Relating DSA to problem-solving skills) 25:21-Prerequisites for DSA (Basic Python knowledge needed) 25:41-Time and Space Complexity (Big O Notation) (Concepts, Asymptotic Notation, Types of Complexity, Comparison of Complexities) 01:03:05-Section 2: Basic Data Structures & Techniques 01:03:50-Lists in Python (Properties, Mutability, Operations like append, extend, insert, pop, Slicing) 01:44:48-Tuples in Python (Immutability, Differences from Lists) 01:49:21-Strings in Python (Representations, Immutability, Operations like strip, split, join) 02:05:00-Two Pointers Technique (Solving Two Sum and Move Zeros) 02:45:00-Prefix Sum Technique (Solving Find Pivot Index) 03:09:00-Sets and Hashing (Unique Elements, Set Operations: Union, Intersection, Difference) 03:23:40-Dictionaries (Hash Maps) and Frequency Counting (Key-Value Pairs, Applications) 03:45:00-Sliding Window Technique (Solving Maximum Average Subarray and Longest Substring Without Repeating Characters) 04:02:40-Section 3: Recursion and Mathematics 04:06:50-Introduction to Recursion (Function calling itself, Base Case, Recursive Case) 04:26:50-Fibonacci Sequence (Implementation using Recursion and Recurrence Tree) 04:50:00-Mathematics Concepts (Finding GCD using Euclidean Algorithm) 05:04:00-Mathematical Problems (Solving Power of Two, X to the power of N) 05:17:00-Prime Numbers (Checking primality, Sieve of Eratosthenes) 05:30:20-Section 4: Sorting and Searching Algorithms 05:37:00-Basic Sorting Algorithms (Bubble Sort, Insertion Sort, Selection Sort) 06:17:00-Advanced Sorting Algorithms (Merge Sort and Quick Sort) 06:45:00-Searching Algorithms (Linear Search and Binary Search) 07:12:00-Lower Bound and Upper Bound (Concepts and Application) 07:40:00-Section 5: Linked Lists 07:49:30-Introduction to Linked Lists (Nodes, Pointers, Why use Linked Lists) 08:00:00-Linked List Operations (Insertion, Deletion, Traversal) 08:11:00-Fast and Slow Pointer Technique (Solving Find Middle of Linked List) 08:29:00-Linked List Problems (Solving Reverse Linked List and Remove Nth Node from End) 08:34:00-Linked List Cycles (Detecting and Removing Cycles) 08:40:00-Section 6: Stack and Queue 08:42:00-Stack Data Structure (LIFO, Operations: Push, Pop, Top, Array Implementation) 08:50:20-Stack Applications (Solving Valid Parentheses) 09:07:00-Queue Data Structure (FIFO, Operations, Array/List Implementation) 09:21:00-Section 7: Trees and Binary Trees 09:23:00-Tree Terminology (Root, Node, Leaf, Height, Depth) 09:25:00-Binary Tree Types (Full, Perfect, Complete, Balanced) 09:31:00-Tree Traversal Techniques (Pre-order, In-order, Post-order, Level-order/BFS) 09:56:00-Section 8: Binary Search Trees (BST) and Heaps 09:59:00-Binary Search Tree Properties and Search (Efficiency, Insertion, Deletion) 10:14:00-BST Problems (Solving Validate BST) 10:23:00-Heaps and Priority Queues (Max Heap, Min Heap, Introduction to Priority Queues) 10:29:00-Heap Problem (Solving Last Stone Weight) 10:33:00-Section 9: Graphs 10:37:00-Graph Representation (Adjacency List, Adjacency Matrix) 10:52:00-Graph Traversal (Breadth-First Search (BFS) and Depth-First Search (DFS)) 11:03:00-Graph Problem (Solving Count Number of Connected Components) 11:10:00-Section 10: Dynamic Programming (DP) 11:16:00-DP Techniques (Memoization (Top-Down) and Tabulation (Bottom-Up)) 11:32:00-DP Problem (Solving House Robber) Code link :- https://colab.research.google.com/drive/1VW-jJ-WTRzzahKLjNnSlujBlvu-qCwB6?usp=sharing Complete DSA in Python One Shot | DSA in Python from Basics To Advance #DSAinPython #PythonForBeginners #PythonDSA #CodingInterviewPrep #DataStructures #Algorithms #PythonTutorial #OneShotCourse #PlacementPrep #learncoding