Gate Smashers Design and Analysis of Algorithms: study plan and how long it takes
The Gate Smashers playlist is 79 videos and 13.0 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.
79 videos · 13.0 hours · Gate Smashers
Gate Smashers' DAA playlist covers algorithm analysis, recurrence relations, sorting algorithms, heaps, greedy methods, shortest paths, dynamic programming, backtracking, hashing and branch and bound, in short Hindi lectures aimed at GATE and university exams.
At 13 hours it's one of the fastest ways to cover the whole DAA syllabus before an exam.
Who it's for: GATE, UGC NET and university students revising design and analysis of algorithms.
How long does it take?
The full playlist is 13.0 hours across 79 videos. Here's roughly how many days that is at different daily watch times and speeds, counting video time only:
| Daily time | At 1x | At 1.5x | At 2x |
|---|---|---|---|
| 30 min a day | 27 days | 18 days | 14 days |
| 1 h a day | 14 days | 9 days | 7 days |
| 2 h a day | 7 days | 5 days | 4 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.
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 3 weeks in total. For a different pace, start date or speed, use the planner above.
Week 1Videos 1–38 · 5h 49m
Day 1 · 52m
- 1L-1.1: Introduction to Algorithm & Syllabus Discussion for GATE/NET & Placements Preparation | DAA11m
- 2L-1.2: What is Algorithm | How to Analyze an Algorithm | Priori vs Posteriori Analysis | DAA8m
- 3L-1.3: Asymptotic Notations | Big O | Big Omega | Theta Notations | Most Imp Topic Of Algorithm14m
- 4L-1.4: Various Properties of Asymptotic Notation with Example | Algorithm | DAA9m
- 5L-1.5: Comparison of Various Time Complexities | Different types in Increasing Order| Must Watch10m
Day 2 · 54m
- 6L-1.6: Time Complexities of all Searching and Sorting Algorithms in 10 minute | GATE & other Exams13m
- 7L-1.7: Question#1 on Comparison of Various Time Complexities | GATE Questions7m
- 8L-1.8: Question#2 on Comparison of Various Time Complexities | GATE Questions5m
- 9L-2.1: What is Recurrence Relation| How to Write Binary Search Recurrence Relation|How we Solve them7m
- 10L-2.2: Recurrence Relation [ T(n)= T(n/2) + c] | Substitution Method | Algorithm6m
- 11L-2.3: Recurrence Relation [ T(n)= n*T(n-1) ] | Substitution Method | Algorithm8m
- 12L-2.4: Recurrence Relation [ T(n)= 2T(n/2) +n] | Substitution Method | Algorithm9m
Day 3 · 48m
- 13L-2.5: Recurrence Relation [ T(n)= T(n-1) +logn] | Substitution Method | Algorithm10m
- 14L-2.6: Recurrence Relation [ T(n)= 8T(n/2) + n^2 ] | Master Theorem | Example#1 | Algorithm7m
- 15L-2.7: Recurrence Relation [ T(n)= T(n/2) +c] | Master Theorem | Example-2 | Algorithm4m
- 16L-2.8: Recurrence Relation T(n)=T(√n)+logn | Master Theorem6m
- 17L-2.9: Recurrence Relation [T(n)= 2T(n/2) +cn] | Recursive Tree method | Algorithm7m
- 18L-2.10: Recurrence Relation [T(n)= 3T(n/4) +cn^2] | Recursive Tree method | Algorithm10m
- 19L-3.0: Divide and Conquer | Algorithm4m
Day 4 · 54m
- 20L-3.1: How Quick Sort Works | Performance of Quick Sort with Example | Divide and Conquer13m
- 21L-3.2: Performance of Quick Sort | Worst Case Time Complexity with Example | Algorithm8m
- 22L-3.3: How Merge Sort Works?? Full explanation with example10m
- 23L -3.4: Merge Sort Pseudocode | Merge Sort with example8m
- 24L-3.5: Imp. Question on Merge Sort | Divide and Conquer | Algorithm6m
- 25L-3.6: How Bubble Sort Works | Performance of Bubble Sort | All Imp Points with Example | Algorithm9m
Day 5 · 51m
- 26L-3.7: Insertion Sort | Time Complexity Analysis | Stable Sort | Inplace Sorting16m
- 27L-3.8: Selection Sort | Time Complexity(Best, Avg & Worst) Analysis | Stable or Not | Inplace or Not12m
- 28L-3.9: Radix Sort | Easiest explanation with example6m
- 29L-3.10: Counting Sort | Easiest explanation with example8m
- 30L-3.11: Bucket Sort | Easiest explanation with examples | Time Complexity analysis with Pseudocode10m
Day 6 · 40m
- 31L-3.12: Introduction to Trees (Binary Tree, Almost Complete Binary Tree, Full BT, Complete BT, BST)11m
- 32L-3.13: Introduction to Heap Tree with examples | Max Min Heap8m
- 33L-3.14: Insertion in Heap Tree | Max-Heap & Min-Heap Creation | Time Complexities12m
- 34L-3.15: Imp Question on Max Heap | GATE Question on Max/Min Heap | Algorithm10m
Day 7 · 49m
- 35L-3.16: Build Heap in O(n) time complexity | Heapify Method | Full Derivation with example21m
- 36L-3.17: Deletion in Heap tree | Time complexity6m
- 37L-3.18: Heap sort with Example | Heapify Method14m
- 38L-4.1: Introduction to Greedy Techniques With Example | What is Greedy Techniques8m
Week 2Videos 39–74 · 6h 30m
Day 8 · 54m
- 39L-4.2: Knapsack Problem With Example| Greedy Techniques| Algorithm12m
- 40L-4.3: Huffman Coding Algorithm in Hindi with Example | Greedy Techniques(Algorithm)13m
- 41L-4.4: Huffman Coding Question in Greedy Technique | Imp Question for all competitive exams8m
- 42L-4.5: Job Sequencing Algorithm with Example | Greedy Techniques11m
- 43L-4.6: Optimal Merge Pattern using Greedy Method in Hindi | Algorithm11m
Day 9 · 56m
- 44L-4.7: What is Spanning Tree with Examples in Hindi | Algorithm7m
- 45L-4.8: Kruskal Algorithm for Minimum Spanning Tree in Hindi | Algorithm11m
- 46L-4.9: Prim's Algorithm for Minimum Cost Spanning Tree | Prims vs Kruskal10m
- 47L-4.10: Dijkstra's Algorithm - Single Source Shortest Path - Greedy Method16m
- 48L-4.11: Dijkstra's Algorithm Analysis | Time Complexity | Pseudocode Explanation11m
Day 10 · 53m
- 49L-4.12: Why does Dijkstra fail on Negative Weights?? Full Explanation with examples8m
- 50L-4.13: Bellman Ford Algorithm | Dijkstra's Vs Bellman Ford | Single Source Shortest Path17m
- 51L-4.14: Bellman Ford pseudo code and Time complexity | Single Source Shortest Path8m
- 52L-4.15: BFS & DFS | Breadth First Search | Depth First Search | Graph Traversing | DAA11m
- 53L-5.1: Introduction to Dynamic Programming | Greedy Vs Dynamic Programming | Algorithm(DAA)9m
Day 11 · 56m
- 54L-5.2: 0/1 Knapsack failed using Greedy approach10m
- 55L-5.3: 0/1 Knapsack Problem |Dynamic Programming |Recursive Equation |Recursion Tree Time Complexity17m
- 56L-5.4: Traveling Salesman Problem | Dynamic Programming14m
- 57L-5.5: Sum of Subsets Problem | Dynamic Programming15m
Day 12 · 59m
- 58L-5.6: Multistage Graph | Dynamic Programming18m
- 59L-5.7: Introduction to All Pair Shortest Path (Floyd Warshall Algorithm)6m
- 60L-5.8: Floyd Warshall Working with example | All Pair Shortest Path Algorithm26m
- 61L-5.9: Floyd Warshall Time & Space complexity | All Pair Shortest Path9m
Day 13 · 58m
- 62L-6.1: What is hashing with example | Hashing in data structure6m
- 63L-6.2: Collision Resolution Techniques in Hashing | What are the collision resolution techniques?6m
- 64L-6.3: Chaining in Hashing | What is chaining in hashing with examples7m
- 65L-6.4: Linear Probing in Hashing with example13m
- 66L-6.5: Imp Question on Hashing | Linear Probing for Collision in Hash Table | GATE Questions9m
- 67L-6.6: Quadratic Probing in Hashing with example8m
- 68L-6.7: Double Hashing | Collision Resolution Technique10m
Day 14 · 55m
- 69L- 6.8:Topological Sorting with examples | Topological Sorting using DFS12m
- 70L-6.9:Kahn's Algorithm | Topological Sorting | DAA | Imp for Placements8m
- 71Lec-41: Branch & Bound Algorithm with Example | Easiest Explanation of B&B with example13m
- 72Lec-42 : 0/1 Knapsack using Branch and Bound with example11m
- 73Shortcut for Swaps🔁 in Bubble🫧 Sorting📶| Best Case | Worst Case3m
- 74Optimized Bubble Sort | Best Case | O(n) Time Complexity | Sorting Algorithms |DAA9m
Week 3Videos 75–79 · 41m
Day 15 · 41m
- 75Recurrence Relation [T(n) = 2T(n/2) + 2] | Min-Max Algorithm10m
- 76Min-Max Algorithm without Divide & Conquer🏆 | Linear Approach | Find Max & Min7m
- 77Min Max Algorithm with Divide & Conquer🏆9m
- 78Time complexity and Comparison of All Data Structures8m
- 79Bubble Sort Pseudocode | Performance & Time Complexity8m
Tips for finishing
- Solve previous years' questions after each unit; the lectures are built around how GATE asks them.
- Pair it with Abdul Bari's Algorithms playlist if you want deeper explanations of specific topics.