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Semester 1MCA133

Analysis and Design of Algorithms (MCA133) crash course, Online MCA, Semester 1:

This subject covers how to measure the time and space complexity of algorithms and the main design techniques: divide and conquer, dynamic programming, greedy…

  • 18 video lessons
  • 6h 17m
  • Study notes
  • 90 quiz questions
  • Assignment
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Analysis and Design of Algorithms crash course

18 lessons in Analysis and Design of Algorithms

Each lesson pairs one topic with a hand-picked video, trimmed to the part that teaches it.

About this subject

This subject covers how to measure the time and space complexity of algorithms and the main design techniques: divide and conquer, dynamic programming, greedy methods and graph algorithms, along with sorting, searching, hashing, string matching and NP-completeness. After it you will be able to analyse an algorithm's best, worst and average-case cost, choose a suitable design technique for a problem, and recognise when a problem is likely NP-complete and needs an approximation.

  1. 1

    Algorithm Complexity: Best, Worst and Average Case

    Module I, Algorithm Analysis: Introduction Algorithms Complexity measures, Best, worst and average-case complexity functions, problem complexity

    1.11 Best Worst and Average Case Analysis· Abdul Bari· 19 min

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  2. 2

    Review of Data Structures and Algorithm Design Principles

    Module I, Algorithm Analysis: quick review of basic data structures and algorithm design principles

    L-1.2: What is Algorithm | How to Analyze an Algorithm | Priori vs Posteriori Analysis | DAA· Gate Smashers· 8 min

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  3. 3

    Selection, Insertion and Bubble Sort

    Module II, Sorting and searching Order statistics: Sorting by selection, insertion and bubble

    Sorting Algorithms | Bubble Sort, Selection Sort & Insertion Sort | DSA Series by Shradha Ma'am· Apna College· 32 min

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  4. 4

    Divide and Conquer: Merge Sort, Quick Sort and Strassen's Matrix Multiplication

    Module II, Sorting and searching Order statistics: Divide & Conquer Strategy, Quick Sort, merge sort, Strassen's Matrix Multiplication

    L-3.1: How Quick Sort Works | Performance of Quick Sort with Example | Divide and Conquer· Gate Smashers· 13 min

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  5. 5

    Heap Sort, Radix Sort and Sorting in Linear Time

    Module II, Sorting and searching Order statistics: Heap Sort, Data Sorting in Linear time, other sorting algorithms— radix sort

    L-3.18: Heap sort with Example | Heapify Method· Gate Smashers· 14 min

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  6. 6

    Binary Search and Optimal Binary Search Trees

    Module II, Sorting and searching Order statistics: Searching in static table— binary search, path lengths in binary trees and applications, optimality of binary search in worst case and average-case, Binary search trees, construction of optimal weighted binary search trees

    4.6 Optimal Binary Search Tree (Successful Search Only) - Dynamic Programming· Abdul Bari· 30 min

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  7. 7

    AVL Trees, (a,b) Trees and Hashing

    Module II, Sorting and searching Order statistics: Searching in dynamic table - randomly grown binary search trees, AVL and (a, b) trees, Hashing: Basic ingredients, analysis of hashing with chaining and with open addressing

    10.1 AVL Tree - Insertion and Rotations· Abdul Bari· 43 min

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  8. 8

    Dynamic Programming and Matrix Chain Multiplication

    Module III, Advanced Design and Analysis Techniques: Dynamic programming- Elements of dynamic programming, Chain-matrix multiplication

    4.3 Matrix Chain Multiplication - Dynamic Programming· Abdul Bari· 23 min

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  9. 9

    Floyd-Warshall Algorithm and Optimal BST

    Module III, Advanced Design and Analysis Techniques: All pair shortest path (Floyd algorithm), Optimal Binary Search Tree

    4.2 All Pairs Shortest Path (Floyd-Warshall) - Dynamic Programming· Abdul Bari· 14 min

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  10. 10

    Greedy Algorithms and Huffman Codes

    Module III, Advanced Design and Analysis Techniques: Greedy algorithms- Elements of the greedy strategy, Huffman codes

    3.4 Huffman Coding - Greedy Method· Abdul Bari· 18 min

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  11. 11

    Greedy Shortest Path and Knapsack Problem

    Module III, Advanced Design and Analysis Techniques: Single-source shortest path in a directed graph, Knapsack problem

    3.6 Dijkstra Algorithm - Single Source Shortest Path - Greedy Method· Abdul Bari· 19 min

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  12. 12

    Elementary Graph Algorithms (BFS, DFS)

    Module IV, Graph Algorithms: Elementary graphs Algorithms

    5.1 Graph Traversals - BFS & DFS -Breadth First Search and Depth First Search· Abdul Bari· 19 min

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  13. 13

    Minimum Spanning Trees: Kruskal's and Prim's Algorithms

    Module IV, Graph Algorithms: Minimum spanning Trees minimum spanning trees— Kruskal's and Prim's algorithms— Johnson's implementation of Prim's algorithm using priority queue data structures

    3.5 Prims and Kruskals Algorithms - Greedy Method· Abdul Bari· 20 min

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  14. 14

    Single Source and All Pairs Shortest Paths

    Module IV, Graph Algorithms: Single source Shortest paths, All Pair Shortest Paths

    3.6 Dijkstra Algorithm - Single Source Shortest Path - Greedy Method· Abdul Bari· 19 min

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  15. 15

    KMP String Matching Algorithm

    Module IV, Graph Algorithms: String processing: String searching and Pattern matching, Knuth-Morris-Pratt algorithm and its analysis

    9.1 Knuth-Morris-Pratt KMP String Matching Algorithm· Abdul Bari· 19 min

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  16. 16

    P vs NP and Nondeterministic Algorithms

    Module V, NP-completeness: Informal concepts of deterministic and nondeterministic algorithms, P and NP

    Non Deterministic Algorithm || Example || Deterministic Algorithm || DAA || P Problem || NP Problem· DIVVELA SRINIVASA RAO· 23 min

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  17. 17

    NP-Completeness and Cook's Theorem

    Module V, NP-completeness: NP-completeness, statement of Cook's theorem, some standard NP-complete problems

    Cook's Theorem / Cooks Theorem /#CookTheorem/#Cookstheorem/#DAA/#NPHardAndNPComplete/#PrasadSir/· PRASAD Sir· 14 min

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  18. 18

    Approximation Algorithms

    Module V, NP-completeness: approximation algorithms

    Design and Analysis of Algorithm | Approximation Algorithms | AKTU Digital Education· AKTU Digital Education· 30 min

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Assignment

Algorithm Analysis Problem Set: Sorting, Dynamic Programming, Greedy, Graphs and Strings

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Analysis and Design of Algorithms: common questions

This subject covers how to measure the time and space complexity of algorithms and the main design techniques: divide and conquer, dynamic programming, greedy methods and graph algorithms, along with sorting, searching, hashing, string matching and NP-completeness. After it you will be able to analyse an algorithm's best, worst and average-case cost, choose a suitable design technique for a problem, and recognise when a problem is likely NP-complete and needs an approximation. It's the Analysis and Design of Algorithms subject of Online MCA, Semester 1, in 18 lessons: Algorithm Complexity: Best, Worst and Average Case; Review of Data Structures and Algorithm Design Principles; Selection, Insertion and Bubble Sort; Divide and Conquer: Merge Sort, Quick Sort and Strassen's Matrix Multiplication; Heap Sort, Radix Sort and Sorting in Linear Time; Binary Search and Optimal Binary Search Trees; AVL Trees, (a,b) Trees and Hashing; Dynamic Programming and Matrix Chain Multiplication; Floyd-Warshall Algorithm and Optimal BST; Greedy Algorithms and Huffman Codes; Greedy Shortest Path and Knapsack Problem; Elementary Graph Algorithms (BFS, DFS); Minimum Spanning Trees: Kruskal's and Prim's Algorithms; Single Source and All Pairs Shortest Paths; KMP String Matching Algorithm; P vs NP and Nondeterministic Algorithms; NP-Completeness and Cook's Theorem; Approximation Algorithms.

Yes. Every video lesson is free to watch without an account. A free login unlocks study notes, quizzes, the assignment, a notebook and progress that syncs across devices.

About 6h 17m of video across 18 lessons, plus time for notes and quizzes. Go at your own pace; your place is saved.

Yes: “Algorithm Analysis Problem Set: Sorting, Dynamic Programming, Greedy, Graphs and Strings”. It comes after the last lesson, with a brief, clear tasks and how the work is judged.

A members-only live chat beside the video, shared by everyone studying Online MCA, where learners ask doubts, share tips and react with stickers and GIFs. One ₹19 pass, paid once from your InsideEdge wallet, opens the live chat for every degree.

No. It's a crash course to help you understand and revise the subject quickly. Use it alongside your official study material and assignments.

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