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Design and Analysis of algorithms DAA

Course Reviews

Track :

Programming

Lessons no : 55

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What will you learn in this course?
  • Develop efficient algorithms for sorting, searching, and optimization problems using design techniques like divide and conquer, dynamic programming, and greedy methods
  • Analyze algorithm complexity and performance using Big O notation, asymptotic analysis, and time-space trade-offs
  • Implement algorithms for graph traversal, shortest path, and network flow to solve real-world problems in computer science and data analysis
  • Design recursive and iterative algorithms to improve problem-solving efficiency and code clarity in software development
  • Apply algorithmic strategies to solve combinatorial problems, including backtracking and branch-and-bound techniques
  • Evaluate algorithm correctness and robustness through testing, debugging, and optimization for practical applications
  • Utilize advanced data structures like heaps, hash tables, and trees to enhance algorithm efficiency and data management
  • Develop algorithms for pattern matching, string processing, and data compression to optimize information retrieval and storage
  • Understand the principles of parallel and distributed algorithms to improve computational speed and scalability
  • Create algorithms for machine learning, artificial intelligence, and data mining tasks to support intelligent systems
  • Analyze real-world case studies to identify suitable algorithmic solutions for complex problems
  • Apply theoretical concepts to design innovative algorithms that address emerging challenges in computer science

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Lessons | 55
Algorithm introduction | Design Algorithms | Lec 1 | Bhanu Priya
00:11:17

Algorithm introduction | Design Algorithms | Lec 1 | Bhanu Priya
space complexity | Design Algorithms | Lec 2 | Bhanu Priya
00:16:47

space complexity | Design Algorithms | Lec 2 | Bhanu Priya
time complexity | Algorithms Design and Analysis
00:10:42

time complexity | Algorithms Design and Analysis
pseudo code | Sequence Selection logic | Part 1 2 | Design Algorithms | Lec 4 | Bhanu Priya
00:11:44

pseudo code | Sequence Selection logic | Part 1 2 | Design Algorithms | Lec 4 | Bhanu Priya
pseudo code | Iteration logic | Part 2 2 | Design Algorithms | Lec 5 | Bhanu Priya
00:08:01

pseudo code | Iteration logic | Part 2 2 | Design Algorithms | Lec 5 | Bhanu Priya
Asymptotic notation | Design Algorithms | Lec 6 | Bhanu Priya
00:09:33

Asymptotic notation | Design Algorithms | Lec 6 | Bhanu Priya
asymptotic growth | Design Algorithms | Lec 7 | Bhanu Priya
00:08:30

asymptotic growth | Design Algorithms | Lec 7 | Bhanu Priya
Asymptotic notation | Theta notation | Design Algorithms | Lec 8 | Bhanu Priya
00:08:22

Asymptotic notation | Theta notation | Design Algorithms | Lec 8 | Bhanu Priya
Asymptotic notation | Big O notation | Design Algorithms | Lec 9 | Bhanu Priya
00:05:55

Asymptotic notation | Big O notation | Design Algorithms | Lec 9 | Bhanu Priya
Asymptotic notation | Big omega little oh omega | Design Algorithms | Lec 10 | Bhanu Priya
00:05:51

Asymptotic notation | Big omega little oh omega | Design Algorithms | Lec 10 | Bhanu Priya
divide and conquer algorithm | Design Algorithms | Lec 11 | Bhanu Priya
00:09:42

divide and conquer algorithm | Design Algorithms | Lec 11 | Bhanu Priya
Binary Search Algorithm | Design Algorithms | Lec 12 | Bhanu Priya
00:10:42

Binary Search Algorithm | Design Algorithms | Lec 12 | Bhanu Priya
Binary Search examples | Successful search | Design Algorithms | Lec 13 | Bhanu Priya
00:08:23

Binary Search examples | Successful search | Design Algorithms | Lec 13 | Bhanu Priya
Binary Search Examples | Unsuccessful search | Design Algorithms | Lec 14 | Bhanu Priya
00:07:04

Binary Search Examples | Unsuccessful search | Design Algorithms | Lec 14 | Bhanu Priya
merge sort algorithm
00:09:09

merge sort algorithm
Merge sort example | Design Algorithms | Lec 16 | Bhanu Priya
00:06:27

Merge sort example | Design Algorithms | Lec 16 | Bhanu Priya
Quicksort algorithm | Partition | Part 1 2 | Design Algorithms | Lec 17 | Bhanu Priya
00:10:26

Quicksort algorithm | Partition | Part 1 2 | Design Algorithms | Lec 17 | Bhanu Priya
Quicksort algorithm| Example | Part 2 2 | Design Algorithms | Lec 18 | Bhanu Priya
00:11:02

Quicksort algorithm| Example | Part 2 2 | Design Algorithms | Lec 18 | Bhanu Priya
Matrices multiplication | basic | Design Algorithms | Lec 19 | Bhanu Priya
00:10:39

Matrices multiplication | basic | Design Algorithms | Lec 19 | Bhanu Priya
Divide and Conquer | Matrix Multiplication | Design Algorithms | Lec 20 | Bhanu Priya
00:08:12

Divide and Conquer | Matrix Multiplication | Design Algorithms | Lec 20 | Bhanu Priya
2.9 Strassens Matrix Multiplication
00:09:52

2.9 Strassens Matrix Multiplication
Binary Search Tree | BST | Design Algorithms | Lec 22 | Bhanu Priya
00:10:49

Binary Search Tree | BST | Design Algorithms | Lec 22 | Bhanu Priya
Binary Search Tree traversal | Design Algorithms | Lec 23 | Bhanu Priya
00:04:51

Binary Search Tree traversal | Design Algorithms | Lec 23 | Bhanu Priya
BST traversal | In Pre Post order | Examples | Design Algorithms | Lec 24 | Bhanu Priya
00:08:34

BST traversal | In Pre Post order | Examples | Design Algorithms | Lec 24 | Bhanu Priya
Spanning Tree | Design Algorithms | Lec 25 | Bhanu Priya
00:10:23

Spanning Tree | Design Algorithms | Lec 25 | Bhanu Priya
Prim s algorithm | Minimum Spanning tree MST | Design Algorithms | Lec 26 | Bhanu Priya
00:13:02

Prim s algorithm | Minimum Spanning tree MST | Design Algorithms | Lec 26 | Bhanu Priya
Krushkal s algorithm | Minimum Spanning Tree MST | Design Algorithms | Lec 27 | Bhanu Priya
00:10:32

Krushkal s algorithm | Minimum Spanning Tree MST | Design Algorithms | Lec 27 | Bhanu Priya
Prim s and Krushkal s algorithm | MST | Differences | Design Algorithms | Lec 28 | Bhanu Priya
00:08:34

Prim s and Krushkal s algorithm | MST | Differences | Design Algorithms | Lec 28 | Bhanu Priya
Breadth First Search algorithm | BFS | Design Algorithms | Lec 29 | Bhanu Priya
00:08:09

Breadth First Search algorithm | BFS | Design Algorithms | Lec 29 | Bhanu Priya
Breadth First Search | BFS examples | Design Algorithms | Lec 30 | Bhanu Priya
00:07:08

Breadth First Search | BFS examples | Design Algorithms | Lec 30 | Bhanu Priya
Depth First Search | DFS | Graph traversal | Design Algorithms | Lec 31 | Bhanu Priya
00:05:03

Depth First Search | DFS | Graph traversal | Design Algorithms | Lec 31 | Bhanu Priya
Depth First Search | DFS | Examples | Graph traversal | Design Algorithms | Lec 32 | Bhanu Priya
00:11:08

Depth First Search | DFS | Examples | Graph traversal | Design Algorithms | Lec 32 | Bhanu Priya
AND OR graph | Design Algorithms | Lec 33 | Bhanu Priya
00:08:38

AND OR graph | Design Algorithms | Lec 33 | Bhanu Priya
Connected Components | Graph | Design Algorithms | Lec 34 | Bhanu Priya
00:08:13

Connected Components | Graph | Design Algorithms | Lec 34 | Bhanu Priya
Biconnected Components| Graph | Design Algorithms | Lec 35 | Bhanu Priya
00:07:24

Biconnected Components| Graph | Design Algorithms | Lec 35 | Bhanu Priya
Dijkstras Shortest Path Algorithm Explained | With Example
00:08:56

Dijkstras Shortest Path Algorithm Explained | With Example
bellman ford shortest path algorithm | DAA |
00:13:21

bellman ford shortest path algorithm | DAA |
Greedy Method | Design Algorithms | Lec 38 | Bhanu Priya
00:13:06

Greedy Method | Design Algorithms | Lec 38 | Bhanu Priya
Job sequencing problem with deadline | Greedy Method | Design Algorithms | Lec 39 | Bhanu Priya
00:25:17

Job sequencing problem with deadline | Greedy Method | Design Algorithms | Lec 39 | Bhanu Priya
Minimum Cost Spanning Tree | Krushkal s Prim s | Design Algorithms | Lec 40 | Bhanu Priya
00:10:36

Minimum Cost Spanning Tree | Krushkal s Prim s | Design Algorithms | Lec 40 | Bhanu Priya
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Md Junaid 

its good
2025-12-30

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GOOD
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good
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good
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good teaching
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nice course good explanation
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too good,weel explained
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Learnt the concepts good
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Design and Analysis of Algorithm help to design the algorithms for solving different types of problems in Computer Science. It also helps to design and analyze the logic on how the program will work before developing the actual code for a program .