YouTube Tools
Support

Abdul Bari's Algorithms: study plan and how long it takes

The Abdul Bari playlist is 84 videos and 24.1 hours long. Here's how long it takes at different paces, a week-by-week schedule built from the real videos, and a planner that turns it into your own daily plan.

84 videos · 24.1 hours · Abdul Bari

Abdul Bari's Algorithms playlist is a classic for university algorithms courses and GATE preparation. It covers analysis, divide and conquer, greedy methods, dynamic programming, backtracking, branch and bound and NP-completeness, with clear whiteboard explanations.

Videos are mostly 10 to 25 minutes, which makes it easy to plan around a syllabus. At an hour a day, you can finish it in under a month.

Who it's for: Computer science students taking a design and analysis of algorithms course, GATE aspirants, and anyone who wants the theory behind classic algorithms.

How long does it take?

The full playlist is 24.1 hours across 84 videos. Here's roughly how many days that is at different daily watch times and speeds, counting video time only:

Days to finish the playlist by daily watch time and playback speed
Daily timeAt 1xAt 1.5xAt 2x
30 min a day49 days33 days25 days
1 h a day25 days17 days13 days
2 h a day13 days9 days7 days

Make your own schedule

The playlist link is already filled in. Press Plan, set how much time you have each day and your speed, and you'll get a day-by-day schedule with a finish date and a calendar file. It reads the live playlist, so new videos are included.

Open on YouTube Track watched videos

Week-by-week schedule

A sample plan at 1 hour a day and normal speed, using whole videos so none is split across days. That's 5 weeks in total. For a different pace, start date or speed, use the planner above.

Week 1Videos 1–31 · 5h 50m

  1. Day 1 · 52m

    • 11. Introduction to Algorithms12m
    • 21.1 Priori Analysis and Posteriori Testing2m
    • 31.2 Characteristics of Algorithm6m
    • 41.3 How Write and Analyze Algorithm11m
    • 51.4 Frequency Count Method12m
    • 61.5.1 Time Complexity #110m

  2. Day 2 · 44m

    • 71.5.2 Time Complexity Example #214m
    • 81.5.3 Time Complexity of While and if #322m
    • 91.6 Classes of functions3m
    • 101.7 Compare Class of Functions5m

  3. Day 3 · 58m

    • 111.8.1 Asymptotic Notations Big Oh - Omega - Theta #116m
    • 121.8.2 Asymptotic Notations - Big Oh - Omega - Theta #210m
    • 131.9 Properties of Asymptotic Notations12m
    • 141.10.1 Comparison of Functions #19m
    • 151.10.2 Comparison of Functions #210m

  4. Day 4 · 52m

    • 161.11 Best Worst and Average Case Analysis19m
    • 171.12 Disjoint Sets Data Structure - Weighted Union and Collapsing Find26m
    • 182 Divide And Conquer7m

  5. Day 5 · 53m

    • 192.1.1 Recurrence Relation (T(n)= T(n-1) + 1) #114m
    • 202.1.2 Recurrence Relation (T(n)= T(n-1) + n) #216m
    • 212.1.3 Recurrence Relation (T(n)= T(n-1) + log n) #312m
    • 222.1.4 Recurrence Relation T(n)=2 T(n-1)+1 #411m

  6. Day 6 · 58m

    • 232.2 Masters Theorem Decreasing Function8m
    • 242.3.1 Recurrence Relation Dividing Function T(n)=T(n/2)+1 #19m
    • 252.3.2 Recurrence Relation Dividing [ T(n)=T(n/2)+ n]. #27m
    • 262.3.3 Recurrence Relation [ T(n)= 2T(n/2) +n] #311m
    • 272.4.1 Masters Theorem in Algorithms for Dividing Function #117m
    • 282.4.2 Examples for Master Theorem #26m

  7. Day 7 · 32m

    • 292.5 Root function (Recurrence Relation)6m
    • 302.6.1 Binary Search Iterative Method20m
    • 312.6.2 Binary Search Recursive Method7m
Week 2Videos 32–50 · 5h 49m

  1. Day 8 · 51m

    • 322.6.3 Heap - Heap Sort - Heapify - Priority Queues51m

  2. Day 9 · 54m

    • 332.7.1 Two Way MergeSort - Iterative method20m
    • 342.7.2. Merge Sort Algorithm20m
    • 352.7.3 MergeSort in-depth Analysis13m

  3. Day 10 · 49m

    • 362.8.1 QuickSort Algorithm14m
    • 372.8.2 QuickSort Analysis12m
    • 382.9 Strassens Matrix Multiplication24m

  4. Day 11 · 51m

    • 393. Greedy Method - Introduction12m
    • 403.1 Knapsack Problem - Greedy Method16m
    • 413.2 Job Sequencing with Deadlines - Greedy Method13m
    • 423.3 Optimal Merge Pattern - Greedy Method10m

  5. Day 12 · 56m

    • 433.4 Huffman Coding - Greedy Method18m
    • 443.5 Prims and Kruskals Algorithms - Greedy Method20m
    • 453.6 Dijkstra Algorithm - Single Source Shortest Path - Greedy Method19m

  6. Day 13 · 50m

    • 464 Principle of Optimality - Dynamic Programming introduction15m
    • 474.1 MultiStage Graph - Dynamic Programming21m
    • 484.1.1 MultiStage Graph (Program) - Dynamic Programming14m

  7. Day 14 · 37m

    • 494.2 All Pairs Shortest Path (Floyd-Warshall) - Dynamic Programming14m
    • 504.3 Matrix Chain Multiplication - Dynamic Programming23m
Week 3Videos 51–63 · 5h 23m

  1. Day 15 · 52m

    • 51[New] Matrix Chain Multiplication using Dynamic Programming Formula52m

  2. Day 16 · 36m

    • 524.3.1 Matrix Chain Multiplication (Program) - Dynamic Programming19m
    • 534.4 Bellman Ford Algorithm - Single Source Shortest Path - Dynamic Programming17m

  3. Day 17 · 45m

    • 544.5 0/1 Knapsack - Two Methods - Dynamic Programming28m
    • 554.5.1 0/1 Knapsack Problem (Program) - Dynamic Programming17m

  4. Day 18 · 30m

    • 564.6 Optimal Binary Search Tree (Successful Search Only) - Dynamic Programming30m

  5. Day 19 · 57m

    • 574.6.2 [New] Optimal Binary Search Tree Successful and Unsuccessful Probability - Dynamic Programming57m

  6. Day 20 · 44m

    • 584.7 [New] Traveling Salesman Problem - Dynamic Programming using Formula17m
    • 594.8 Reliability Design - Dynamic Programming27m

  7. Day 21 · 59m

    • 604.9 Longest Common Subsequence (LCS) - Recursion and Dynamic Programming24m
    • 615.1 Graph Traversals - BFS & DFS -Breadth First Search and Depth First Search19m
    • 625.2 Articulation Point and Biconnected Components9m
    • 636 Introduction to Backtracking - Brute Force Approach8m
Week 4Videos 64–79 · 5h 31m

  1. Day 22 · 42m

    • 646.1 N Queens Problem using Backtracking14m
    • 656.2 Sum Of Subsets Problem - Backtracking12m
    • 666.3 Graph Coloring Problem - Backtracking16m

  2. Day 23 · 50m

    • 676.4 Hamiltonian Cycle - Backtracking19m
    • 687 Branch and Bound Introduction10m
    • 697.1 Job Sequencing with Deadline - Branch and Bound11m
    • 707.2 0/1 Knapsack using Branch and Bound11m

  3. Day 24 · 57m

    • 717.3 Traveling Salesman Problem - Branch and Bound25m
    • 728. NP-Hard and NP-Complete Problems32m

  4. Day 25 · 1h

    • 738.1 NP-Hard Graph Problem - Clique Decision Problem17m
    • 749.1 Knuth-Morris-Pratt KMP String Matching Algorithm19m
    • 759.2 Rabin-Karp String Matching Algorithm24m

  5. Day 26 · 43m

    • 7610.1 AVL Tree - Insertion and Rotations43m

  6. Day 27 · 40m

    • 7710.2 B Trees and B+ Trees. How they are useful in Databases40m

  7. Day 28 · 40m

    • 78Asymptotic Notations - Simplified23m
    • 79Hashing Technique - Simplified17m
Week 5Videos 80–84 · 1h 31m

  1. Day 29 · 55m

    • 80Shortest Path Algorithms (Dijkstra and Bellman-Ford) - Simplified26m
    • 81BFS DFS - Simplified19m
    • 82Tower of Hanoi Problem - Made Easy10m

  2. Day 30 · 36m

    • 83Row-Major and Column-Major Mapping19m
    • 84Merge Sort Algorithm - Hindi17m

Study plan, phase by phase

Video numbers are positions in the playlist. To schedule just one phase, enter its numbers in the planner's From and To fields.

  1. 01

    Analysis of algorithms

    Videos 1–17 · 3h 19m

    What algorithms are, frequency counts, time complexity, asymptotic notations and their properties, best, worst and average case, and disjoint sets.

  2. 02

    Divide and conquer

    Videos 18–38 · 5h 5m

    Recurrence relations, the master theorem, binary search, heaps and heap sort, merge sort, quick sort and Strassen's matrix multiplication.

  3. 03

    Greedy method

    Videos 39–45 · 1h 47m

    Knapsack, job sequencing, optimal merge patterns, Huffman coding, Prim's and Kruskal's algorithms and Dijkstra's algorithm.

  4. 04

    Dynamic programming

    Videos 46–60 · 6h 16m

    Multistage graphs, Floyd-Warshall, matrix chain multiplication, Bellman-Ford, 0/1 knapsack, optimal BSTs, the travelling salesman problem, reliability design and LCS.

  5. 05

    Graphs, backtracking, branch and bound, and NP

    Videos 61–73 · 3h 21m

    BFS and DFS, articulation points, N-Queens, sum of subsets, graph colouring, Hamiltonian cycles, branch and bound, and NP-hard and NP-complete problems.

  6. 06

    String matching, trees and quick revisions

    Videos 74–84 · 4h 16m

    KMP and Rabin-Karp, AVL trees, B and B+ trees, simplified revision videos on asymptotic notation, hashing, shortest paths and BFS/DFS, and a few extras like Tower of Hanoi.

Tips for finishing

  • Match the phases to your exam syllabus and use the planner's video range to schedule only the chapters you need.
  • Videos 78 to 81 are short simplified recaps of asymptotic notation, hashing, shortest paths and BFS/DFS. They're perfect for the week before an exam, so schedule them last.
  • Write out each algorithm on paper after the video. That's how exams and GATE test it.

Frequently asked questions

How long does it take to finish Abdul Bari's Algorithms?

The playlist has 84 videos and 24.1 hours of content. At 1 hour a day that's about 25 days at normal speed, or 17 days at 1.5x. At 2 hours a day it's about 13 days.

Is Abdul Bari's Algorithms playlist enough for GATE?

It covers most of the algorithms syllabus in depth. Pair it with previous years' GATE questions to practise the problem types, and with a data structures course for the data structures section.

Is Abdul Bari good for coding interviews?

It's excellent for understanding how algorithms work. For interviews, follow it with a problem-focused course like Striver's A2Z and practise coding the solutions.

More study plans

See all 166 study plans

Tools for any playlist