About Course
This course starts from basics but goes to very advanced topics required to crack top tech interviews like Amazon, Meta, Google, and Microsoft. In case you are targeting top tech companies but not FAANG, we would not recommend this course. We would also highly recommend this course only to students who have already taken our basic DSA course. Or who are at a good level at DSA. This is only for people interested in taking that extra leap to land a job at FAANG. We have more than 100+ hours of top quality content from Google and Microsoft engineers in this course. This course offers programming language support in C#, C++, Java and Python.
- ➣ 1 Year Access with Dynamically Updated Content.
- ➣ Crack Internal Coding Test and Get 100% Referral Guarantee.
- ➣ Weekly Doubt Clearing Classes.
- ➣ 100+ Leetcode video solutions with explanations.
- ➣ Offered by ACM ICPC winners and MAANG engineers.
- ➣ Student community, Github code review, Interview preparations.
Course Content
1. Time Complexity – Part 1
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28:15
-
What is O(n) or Big ‘Oh’ notation ?
15:14 -
What is Omega notation in time complexity ?
09:02 -
How to find best, worst and average time complexity ?
19:10
2. Time complexity – Part 2
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04:39
-
FAANG level MCQs for Time Complexity
29:17 -
Understanding log(n) used in Time Complexity
12:16 -
Solving Recurrence Relations in Time Complexity
36:29 -
A shortcut for time complexity: Masters Theorem
12:45 -
Understanding space complexity
23:10 -
Advanced problems on time complexity
13:05
3. Linked Lists – Part 1
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10:33
-
Understanding references and pointers
06:07 -
Singly Linked List
12:04 -
Deep Dive and fundamentals of Linked Lists
03:31 -
Creation of a linkedlist
06:04 -
Insertions and deletions in linkedlist
39:35 -
Finding Midpoint in linked list
11:10 -
Reversal of linked list
46:59
4. Linked Lists – Part 2
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19:22
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Cycles in a SinglyList
21:36 -
Advanced linkedlist cycle problem
34:19 -
Doubly linked list
38:37
5. Dictionaries and Sets
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33:41
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Introduction to Sets
05:07 -
Introduction to Arrays
13:54 -
Find Missing and Repeated Numbers
17:53 -
2-Sum All variations Explained !!
30:07 -
3-Sum All variations explained !!
01:01:22
6. Arrays and Binary Search
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Moore’s Algorithm
37:29 -
Kadane’s Algorithm – Contiguous Sum
29:26 -
Introduction to Binary Search
01:08:13 -
Binary Search Advanced problems
35:43
7. Stacks
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Introduction to Stacks and classic problems
38:38 -
Infix and postfix problems explained
35:06 -
Next Greater Element Using Stacks
44:29 -
Previous Greater, Next/Previous Smaller Problems
42:26
8. Queues
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Introduction to Queues
14:24 -
Implementation of Queues Using Arrays and Stacks
12:34 -
Implementation Using Singly Linked List
20:23 -
Which is better? : Array, Stack or LinkedList for Queues
03:33 -
Least Recently Used (LRU) Cache
14:31 -
LRU Cache solution deep dive
54:32 -
Introduction to sliding windows
18:32
9. Sliding Window and Circular Queue
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Sliding Window Problems
29:54 -
Introduction to Dequeues
19:22 -
Maximum of All contiguous subarray – Dequeue
24:35 -
Circular Queues
17:36 -
Implementing Stacks using Queues
07:51 -
Variation on Moore’s Algorithm – Google Interview Question
22:18
10. Sorting – Part 1
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Introduction to Sorting
11:21 -
Trivial Sorting – Selection Sort
21:01 -
Trivial Sorting – Bubble Sort
09:50 -
Trivial Sorting – Selection Sort
33:49 -
Introduction to Linear Sorting
30:07 -
Bucket Sorting
07:26 -
Radix Sort
07:01
11. Sorting – Part 2
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Merge Sort
52:51 -
Counting Inversions
18:44 -
Quick Sort
28:22 -
Nuts and Bolts problem
10:57 -
Quick Select
18:57 -
Rearranging -ve and +ve numbers – Explained!!
00:00
12. Strings
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Introduction to Strings
01:14:04 -
Find the Index of the first occurrence in the string
12:55 -
Repeated substring patterns
07:10 -
Repeated String Match
26:57 -
Longest Happy Prefix
09:09 -
String Search: KMP(Knuth Morris Pratt) Algorithm
09:06
13. Strings – Part 2
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String Reversals
06:54 -
Anagrams
23:12 -
Palindromes
26:27 -
Deep Dive on Palindromes
06:50 -
Manacher’s Algorithm
01:25:35
14. Binary Trees – Part 1
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Introduction to Binary Trees
33:35 -
Tree Traversals (Inorder, Preorder, Postorder)
27:25 -
Level Order Traversal
09:03 -
Zig Zag Traversal
13:58 -
Height of a tree
08:31 -
Diameter of a tree
28:19 -
Views in a Tree
43:43
15. Binary Trees – Part 2
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Top View Binary Tree Problems
12:08 -
Bottom View Binary Tree Problems
44:32 -
Serialization of Binary Trees
52:22 -
Introduction to Binary Search Tree (BST)
38:11 -
Implementations and Applications of BST
20:00
16. Binary Search Trees
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Largest BST Subtree in a binary tree – Google Interview
21:19 -
Serialization of Binary Trees
12:34 -
Iterative Inorder Traversal Using Stacks
20:40 -
Problems on Iterative Inorder – Google Interview
26:00
17. Heaps
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Introduction to Heaps
30:14 -
Heapify
48:32 -
Extract Max/Min
03:32 -
Kth Smallest/Largest Element Problem
27:32 -
Heap Sort
05:09 -
Increase/Decrease Key
10:32 -
Priority Queue Using Heaps
13:39 -
Merge K Sorted Lists – Heaps Problem
23:49
18. Graphs – Part 1
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Introduction to Graphs
34:57 -
BFS Traversal
12:57 -
DFS Traversal
08:24 -
Number of Islands problem
12:28 -
Cycle Detection in Graphs
21:36
19. Graphs – Part 2
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Graph based Matrix Problem – Google Interview
16:14 -
Introduction to Topological Sorting
37:51 -
Problems using Topological Sorting
12:29 -
Single Source Shortest Path – Dijkstra’s Algortihm
13:14
20. Graphs – Part 3
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Single Source Shortest Path Further Deep Dive
25:58 -
All Pairs Shortest Path
06:55 -
Minimum Spanning Tree
12:08 -
Minimum Weight Spanning Tree
04:46 -
Kruskal’s Algorithm
27:16 -
Prim’s Algorithm
14:05
21. Graphs – Part 4
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Union Find – Minimum cost to connect all points
25:39 -
Travelling Salesman Problem
08:35 -
Hamiltonian Cycle and TSP deep dive
36:34 -
Introduction to Tries
05:18
22. Tries
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Trie Implementation
37:17 -
Trie Problems
21:00 -
Storing Strings
03:39 -
Word Break Problem
11:08 -
Word Break Problem Revisited !!
33:28
23. Bit Manipulation
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Maximum XOR of two numbers in an array – Part 1
11:48 -
Maximum XOR of two numbers in an array – Part 2
51:14 -
Number of Distinct Substrings in a String
08:08 -
String Of Words Problem
20:33
24. Dynamic Programming – Part 1
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Introduction to Dynamic Programming
38:29 -
Dynamic Programming Basic Problems
56:00 -
Partition equal subset sum
23:28
25. Dynamic Programming – Part 2
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Coin Change Problem
24:51 -
Coin Change II Problem
30:46 -
Edit Distance Problem
25:35
26. Dynamic Programming – Part 3
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Longest Common Subsequence Problem
21:31 -
Palindrome Partitioning II Problem
38:36 -
Robot Grid Problem
18:18
Technical Mock Interviews
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Technical Interview 1
01:12:21 -
Technical Interview 2
56:58 -
Technical Interview 3
49:46 -
Technical Interview 4
48:14 -
Technical Interview 5
01:35:01 -
Resume Building
59:23
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