Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

Design and Analysis of Algorithms

via YouTube

Overview

Prepare for a new career with $100 off Coursera Plus
Gear up for jobs in high-demand fields: data analytics, digital marketing, and more.
This course is an introduction to algorithms and data structures and their analysis. Topics include Big O, Big Omega and Theta Notations, Time Complexities of Searching and Sorting Algorithms, Recurrence Relations and their solutions, Divide and Conquer, Greedy Techniques and Dynamic Programming. Specific algorithms discussed include Quick Sort, Bubble Sort, Insertion Sort, Selection Sort, Heap Tree, Huffman Coding, Job Sequencing, Optimal Merge Pattern and Spanning Tree. Specific data structures discussed include Binary Tree, Almost Complete Binary Tree, Full Binary Tree, Complete Binary Tree, Binary Search Tree, and Hash Table. In addition, the course will cover All Pair Shortest Path with Floyd Warshall Algorithm and Recurrence Relation using Master Theorem. The course also offers practice problems to ensure a comprehensive understanding of the material.

Syllabus

L-1.1: Introduction to Algorithm & Syllabus Discussion for GATE/NET & Placements Preparation | DAA.
L-1.2: What is Algorithm | How to Analyze an Algorithm | Priori vs Posteriori Analysis | DAA.
L-1.3: Asymptotic Notations | Big O | Big Omega | Theta Notations | Most Imp Topic Of Algorithm.
L-1.4: Various Properties of Asymptotic Notation with Example | Algorithm | DAA.
L-1.5: Comparison of Various Time Complexities | Different types in Increasing Order| Must Watch.
L-1.6: Time Complexities of all Searching and Sorting Algorithms in 10 minute | GATE & other Exams.
L-1.7: Question#1 on Comparison of Various Time Complexities | GATE Questions.
L-1.8: Question#2 on Comparison of Various Time Complexities | GATE Questions.
L-2.1: What is Recurrence Relation| How to Write Binary Search Recurrence Relation|How we Solve them.
L-2.2: How to Solve Recurrence Relation using Substitution Method | Question#2 | Algorithm.
L-2.3: What is Substitution Method| How to Solve Recurrence Relation using Substitution Method.
L-2.4: How Master Theorem Solve Recurrence Relations| Example#1 | All Cases Explained with Example.
L-2.5: How to Solve Recurrence Relation Using Master Method | Example-2 | Master Theorem | Algorithm.
L-3.0: Divide and Conquer | Algorithm.
L-3.1: How Quick Sort Works | Performance of Quick Sort with Example | Divide and Conquer.
L-3.2: Performance of Quick Sort | Worst Case Time Complexity with Example | Algorithm.
L-3.3: Imp. Question on Merge Sort | Divide and Conquer | Algorithm.
L-3.4: How Bubble Sort Works | Performance of Bubble Sort | All Imp Points with Example | Algorithm.
L-3.5: Insertion Sort | Time Complexity Analysis | Stable Sort | Inplace Sorting.
L-3.6: Selection Sort | Time Complexity(Best, Avg & Worst) Analysis | Stable or Not | Inplace or Not.
L-3.7: Introduction to Trees (Binary Tree, Almost Complete Binary Tree, Full BT, Complete BT, BST).
L-3.8: Introduction to Heap Tree with examples | Max Min Heap.
L-3.9: Insertion in Heap Tree | Max-Heap & Min-Heap Creation | Time Complexities.
L-3.10: Imp Question on Max Heap | GATE Question on Max/Min Heap | Algorithm.
L-3.11: Build Heap in O(n) time complexity | Heapify Method | Full Derivation with example.
L-3.12: Deletion in Heap tree | Time complexity.
L-3.13: Heap sort with Example | Heapify Method.
L-4.1: Introduction to Greedy Techniques With Example | What is Greedy Techniques.
L-4.2: Knapsack Problem With Example| Greedy Techniques| Algorithm.
L-4.3: Huffman Coding Algorithm in Hindi with Example | Greedy Techniques(Algorithm).
L-4.4: Huffman Coding Question in Greedy Technique | Imp Question for all competitive exams.
L-4.5: Job Sequencing Algorithm with Example | Greedy Techniques.
L-4.6: Optimal Merge Pattern using Greedy Method in Hindi | Algorithm.
L-4.7: What is Spanning Tree with Examples in Hindi | Algorithm.
L-4.8: Kruskal Algorithm for Minimum Spanning Tree in Hindi | Algorithm.
L-4.9: Prim's Algorithm for Minimum Cost Spanning Tree | Prims vs Kruskal.
L-4.10: Dijkstra's Algorithm - Single Source Shortest Path - Greedy Method.
L-4.11: Dijkstra's Algorithm Analysis | Time Complexity | Pseudocode Explanation.
L-4.12: Why does Dijkstra fail on Negative Weights?? Full Explanation with examples.
L-4.13: Bellman Ford Algorithm | Dijkstra's Vs Bellman Ford | Single Source Shortest Path.
L-4.14: Bellman Ford pseudo code and Time complexity | Single Source Shortest Path.
L-5.1: Introduction to Dynamic Programming | Greedy Vs Dynamic Programming | Algorithm(DAA).
L-5.2: 0/1 Knapsack failed using Greedy approach.
L-5.3: 0/1 Knapsack Problem |Dynamic Programming |Recursive Equation |Recursion Tree|Time Complexity.
L-5.4: Traveling Salesman Problem | Dynamic Programming.
Sum of Subsets Problem | Dynamic Programming.
Multistage Graph | Dynamic Programming.
L-6.1: What is hashing with example | Hashing in data structure.
L-6.2: Collision Resolution Techniques in Hashing | What are the collision resolution techniques?.
L-6.3: Chaining in Hashing | What is chaining in hashing with examples.
L-6.4: Linear Probing in Hashing with example.
L-6.5: Imp Question on Hashing | Linear Probing for Collision in Hash Table | GATE Questions.
L-6.6: Quadratic Probing in Hashing with example.
L-6.7: Double Hashing | Collision Resolution Technique.
Recurrence Relation T(n)=T(√n)+logn | Master Theorem.
Introduction to All Pair Shortest Path (Floyd Warshall Algorithm).
Floyd Warshall Working with example | All Pair Shortest Path Algorithm.
Floyd Warshall Time & Space complexity | All Pair Shortest Path.

Taught by

Gate Smashers

Reviews

5.0 rating, based on 1 Class Central review

Start your review of Design and Analysis of Algorithms

  • Sakshi Pravinrao Chandore
    When I went to university (M.Sc. in Computer Science and Engineering), I took both an algorithm and data structures course, so a lot of the material wasn’t foreign to me. However, that was over 20 years ago, so I thought this would be a good refresh…

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.