Master the Coding Interview: Big Tech (FAANG) Interviews

36h 33m 29s
English
Paid

Course description

Want to land a job at a great tech company like Google, Microsoft, Facebook, Netflix, Amazon, or other companies but you are intimidated by the interview process and the coding questions? Do you find yourself feeling like you get "stuck" every time you get asked a coding question? This course is your answer. Using the strategies, lessons, and exercises in this course, you will learn how to land offers from all sorts of companies. This is the ultimate resource to prepare you for coding interviews. Everything you need in one place!

Read more about the course

The goal of the course isn't to tell you: "Do 100 interview questions and hope you memorize their answers." NO! Our goal is to use the hand selected common interview questions to give you a framework to answer ANY interview question that these companies may throw at you. Instead of trying to memorize common questions, we teach you the principles and fundamentals that you can use to notice certain common patterns in questions so that any question that you get, you have a framework to answer and be confident in your programming interview.

You will also get access to our private online community with thousands of developers online to help you get through the course and the interview!

Here is what you will learn to use in this course while answering the interview questions step by step with us:

----Technical----

1. Big O Notation

2. Data Structures Used:

  * Arrays

  * Hash Tables

  * Singly linked lists

  * Doubly linked lists

  * Stacks

  * Queues

  * Binary Trees

  * Binary Search Trees

  * Tries

  * N-ary Trees

  * Min/Max Heaps

  * Priority Queues

  * 2-D Arrays/ Matrices

  * Graphs

  * Adjacency List

  * Adjacency Matrix

  * Interface Design

3. Algorithmic Paradigms Used:

  * Recursion

  * Sorting

  * Searching

  * Tree Traversals

  * Graph Traversals

  * Breadth First Search

  * Depth First Search

  * Divide and Conquer

  * Greedy Method

  * Dynamic Programming

  * Backtracking

4. Specific Algorithms Used:

  * Hoare's Quickselect Algorithm

  * Floyd's Tortoise and Hare Cycle Detection Algorithm

  * Bellman-Ford Algorithm

  * Dijkstra's Algorithm

  * Topological Sort

Unlike most instructors out there, We are not marketers or salespeople. We are senior engineers and programmers who have worked and managed teams of engineers and have been in these interviews both as an interviewee as well as the interviewer.

Our job as instructors will be successful if we are able to help you get your dream job at a big company. This one skill of mastering the coding interview can really change the course of your career and life and we hope you sign up today to see what it can do for your career!


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# Title Duration
1 Master The Coding Interview: Big Tech (FAANG) Interview 02:35
2 Course Breakdown 11:05
3 Interview Question #1 Two Sum 07:15
4 How To Approach Our Problem 11:46
5 Writing Our Brute Force Solution 06:41
6 Testing Our Solution With Our Test Cases 09:24
7 Submitting To Leetcode 01:50
8 Analyzing Space and Time Complexity 10:38
9 Optimizing Our Solution 08:07
10 Coding Our Optimal Solution 05:22
11 Testing Our Optimal Solution With Our Test Cases 08:03
12 Checking Performance On LeetCode 03:25
13 Interview Question #2 - Container With Most Water 04:47
14 Coming Up With Test Cases 04:20
15 Thinking Through A Logical Brute Force Solution 12:50
16 Coding Out Our Brute Force Solution 07:21
17 Stepping Through Our Code 04:45
18 Thinking About Our Optimal Solution 13:53
19 Coding Our Optimal Solution And Testing On LeetCode 09:51
20 Interview Question #3 - Trapping Rainwater 07:20
21 Thinking About A Logical Solution 12:46
22 Coding Our Brute Force Solution 08:53
23 Figuring Out Our Optimization Strategy 24:27
24 Coding Our Optimal Solution 13:27
25 Optimal Code And Testing On LeetCode 02:10
26 Interview Question #4 -Typed Out Strings 07:09
27 Logic Of Our Brute Force Solution 08:03
28 Coding Our Brute Force Solution 09:38
29 Space And Time Complexity 07:23
30 Coming Up With Optimal Solution 11:53
31 Coding Our Optimal Solution 12:36
32 Submitting To LeetCode 05:52
33 Interview Question #5 - Longest Substring Without Repeating Characters 06:40
34 Coming Up With A Brute Force Approach 06:35
35 Coding Our Brute Force Solution 09:58
36 Space And Time Complexity 03:17
37 Sliding Window Technique 05:19
38 Hints For Optimizing Our Solution 02:16
39 Thinking About Optimal Solution 09:10
40 Coding Our Optimal Solution 08:31
41 Optimal Code And Testing On LeetCode 03:19
42 Intro To Palindromes 09:42
43 Interview Question #6 - Valid Palindrome 08:40
44 Almost A Palindrome 10:16
45 Figuring Out Our Solution 06:51
46 Coding Our Solution 09:47
47 Linked List Introduction 03:59
48 Basic Algorithm: Reverse a Linked List 08:22
49 Thinking About Our Solution 10:56
50 Coding Reverse A Linked List Solution 07:08
51 Interview Question #7 - M, N Reversals 06:51
52 Coming Up With A Logical Solution 16:34
53 Coding Our Solution 15:39
54 Interview Question #8 - Merge Multi-Level Doubly Linked List 08:53
55 Figuring Out Our Test Cases 08:35
56 Thinking About Our Approach 16:48
57 Coding Out Our Solution 14:38
58 Interview Question #9 - Cycle Detection 11:40
59 Floyd's Tortoise And Hare 03:05
60 Coding Floyd's Algorithm 08:55
61 Optional: Proof Of How Floyd's Algorithm Works 25:52
62 Intro to Stacks and Queues 03:12
63 Interview Question #10 - Valid Parentheses 09:24
64 Walking Through Our Problem - Identifying The Need For A Stack 08:16
65 Coding Our Solution With A Stack 09:19
66 Interview Question #11 - Minimum Brackets To Remove 07:39
67 Thinking About Our Solution 12:46
68 Coding Our Solution 10:54
69 Question #12 - Implement Queue With Stack 04:11
70 Figuring Out Our Solution 10:20
71 Coding Our Solution 08:28
72 Introducing Recursion 03:08
73 Optional: Tail Recursion 19:55
74 Sorting 02:24
75 Interview Question #13 - Kth Largest Element 06:51
76 Insights From Quick Sort 12:37
77 Understanding Divide And Conquer 08:18
78 Coding Quicksort Into Our Solution 14:54
79 Understanding Hoare's Quickselect Algorithm 06:56
80 Coding Our Solution With Quickselect 11:48
81 Understanding Binary Search 09:33
82 How To Code Binary Search 06:23
83 Question #14 - Start And End Of Target In A Sorted Array 05:06
84 Walking Through Our Solution 10:49
85 Coding Our Solution 12:56
86 Intro to Binary Trees 01:53
87 Question #15 - Maximum Depth of Binary Tree 06:55
88 Learning The Process For Solving Binary Tree Problems 19:13
89 Coding Our Solution 06:12
90 Question #16 - Level Order Of Binary Tree 05:29
91 Walking Through Our Solution 17:28
92 Coding Out Level Order Traversal 11:39
93 Question #17 - Right Side View of Tree 06:44
94 Understanding The Breadth First Search Approach 08:27
95 Understanding The Depth First Search Approach 09:23
96 Thinking About Pre-Order, In-Order, and Post-Order Traversals For Our Solution 12:24
97 Completing Our DFS Solution 07:42
98 Coding Our Final DFS Solution 11:56
99 Question #18 - Number Of Nodes In Complete Tree 08:32
100 Thinking Deeply About A Full Binary Tree 10:27
101 Figuring Out Number Of Nodes At Last Level 15:29
102 Coding Out Our Full Solution 20:40
103 Question #19 - Validate Binary Search Tree 08:48
104 Thinking About Our Logical Solution 15:05
105 Figuring Out Our Boundaries 06:54
106 Coding Our Our Full Solution 07:59
107 Introducing Heaps 08:46
108 Insertion In Heaps - Understanding Sift Up 07:36
109 Deletion In Heaps - Understanding Sift Down 10:49
110 Starting To Code Our Priority Queue Class 10:49
111 Coding Our Insertion And Sift Up Methods 05:50
112 Coding Our Deletion And Sift Down Methods 13:01
113 Introducing 2D Arrays - What Are They? 07:24
114 Depth First Search In 2D-Arrays 07:25
115 Coding DFS - Setting Up For DFS 09:44
116 Coding DFS - Implementing Recursive DFS 08:41
117 Breadth First Search In 2D-Arrays 07:49
118 Coding BFS 10:06
119 A General Approach To Thinking About Most Graph Questions 09:10
120 Question #20 Number Of Islands 07:51
121 Approaching Our Problem - Thinking Deeply About The Values 16:12
122 Approaching Our Problem - Thinking About Traversals 08:56
123 Coding Our Solution 19:09
124 Thinking About Space And Time Complexity 14:39
125 Question #21 Rotting Oranges 06:58
126 Figuring Out Our Initial Logic 11:05
127 Figuring Out The Rest Of Our Solution 14:28
128 Coding Out Our Solution 14:48
129 Question #22 - Walls And Gates 06:20
130 Figuring Out Our Logical Solution 12:17
131 Coding Out Our Solution 11:46
132 Introduction To The Types Of Graphs 11:07
133 Representing Our Graphs - Adjacency List & Adjacency Matrix 07:20
134 Breadth First Search In Graphs 04:40
135 Coding BFS 07:54
136 Depth First Search In Graphs 03:04
137 Coding DFS 07:04
138 Question #23 - Time Needed to Inform All Employees 12:52
139 Verifying Our Constraints And Thinking About Test Cases 08:13
140 How To Represent Our Graph As An Adjacency List 09:38
141 Solving Our Problem Logically Using DFS Traversal 07:49
142 Coding Our DFS Solution 10:02
143 Question #24 - Course Scheduler 10:25
144 Thinking About Our Initial Solution - BFS 07:57
145 Coding Out Our Initial BFS Solution 15:29
146 What is Topological Sort? 07:56
147 Thinking About A Solution With Topological Sort 04:50
148 Coding Our Final Solution 15:48
149 Question #25 - Network Time Delay 12:40
150 Thinking About How To Approach The Problem 08:38
151 Greedy Method & What Is Dijkstra's Algorithm? 15:31
152 Thinking About A Solution With Dijkstra's Algorithm 08:56
153 Coding Our Solution With Dijkstra 17:33
154 Time And Space Complexity Of Our Solution 13:09
155 Thinking About Negative Weights 07:42
156 What is The Bellman-Ford Algorithm? - Conceptualizing Dynamic Programming 16:23
157 What is The Bellman-Ford Algorithm? - The Algorithm Itself 16:18
158 Coding Our Solution With Bellman-Ford 09:20
159 Question #26 - Minimum Cost Of Climbing Stairs & How To Approach DP 09:39
160 Understanding & Identifying Recurrence Relation 15:49
161 First Step - Recursive Solution From Recurrence Relation 05:35
162 Second Step - Memoizing Our Redundant Recursive Calls 07:50
163 Coding Our Memoization Optimization 07:51
164 Understanding The Bottom Up Approach (Tabulation) 06:30
165 Third Step - Bottom Up Tabulation 05:25
166 Fourth Step - Bottom Up Optimization 06:53
167 Question #27 - Knight Probability In Chessboard 10:10
168 Thinking About Test Cases To Help Us 03:52
169 Identifying The Recurrence Relation 15:30
170 First Step - Recursive Solution From Recurrence Relation 05:11
171 Second Step - Memoizing Our Redundant Recursive Calls 14:14
172 Figuring Out The Logic For Our Bottom Up Solution 14:00
173 Third Step - Bottom Up Tabulation 08:32
174 Fourth Step - Bottom Up Optimization 06:56
175 Understanding The Basics Of Backtracking 07:07
176 Question #28 - Sudoku Solver 04:20
177 Learning The Backtracking Template 11:19
178 Applying Our Backtracking Template To Sudoku Solver Logic 06:43
179 Coding How To Get Box ID 07:50
180 Setting Up Our Solution Code 07:06
181 Coding The Recursive Backtracking Portion Of Our Solution 19:19
182 Thinking About The Space And Time Complexity 06:59
183 Understanding Interface Design & Question #29 - Monarchy 10:53
184 Figuring Out Our Test Cases 05:58
185 Thinking About The Logical Of The Monarchy 09:42
186 Coding Our Monarchy Solution 14:58
187 Introducing Tries 01:08
188 Question #30 - Implement Prefix Trie 09:50
189 Understanding The Logic For Our Methods 03:55
190 Implementing Our Prefix Trie Data Structure Solution 15:16
191 Section Overview 02:25
192 What Is Good Code? 06:58
193 Big O and Scalability 11:09
194 O(n) 05:40
195 O(1) 06:11
196 Solution: Big O Calculation 05:55
197 Solution: Big O Calculation 2 02:30
198 Simplifying Big O 01:51
199 Big O Rule 1 04:29
200 Big O Rule 2 06:37
201 Big O Rule 3 03:14
202 O(n^2) 07:14
203 Big O Rule 4 06:48
204 Big O Cheat Sheet 03:19
205 What Does This All Mean? 05:33
206 O(n!) 01:19
207 3 Pillars Of Programming 03:33
208 Space Complexity 02:23
209 Exercise: Space Complexity 06:25
210 Exercise: Twitter 07:14
211 Section Summary 04:44
212 Arrays Introduction 13:52
213 Static vs Dynamic Arrays 06:41
214 Optional: Implementing An Array 17:20
215 Hash Tables Introduction 04:11
216 Hash Function 05:57
217 Hash Collisions 09:44
218 Hash Tables VS Arrays 02:02
219 Linked Lists Introduction 02:27
220 What Is A Linked List? 04:37
221 Exercise: Why Linked Lists? 02:06
222 Solution: Why Linked Lists? 05:36
223 What Is A Pointer? 05:46
224 Doubly Linked Lists 03:19
225 Singly VS Doubly Linked Lists 02:41
226 Linked Lists Review 05:08
227 Stacks + Queues Introduction 02:59
228 Stacks 03:29
229 Queues 03:31
230 Exercise: Stacks VS Queues 03:07
231 Solution: Stacks VS Queues 03:40
232 Stacks + Queues Review 02:20
233 Trees Introduction 06:24
234 Binary Trees 05:46
235 O(log n) 07:01
236 Binary Search Tree 06:13
237 Balanced VS Unbalanced BST 03:43
238 BST Pros and Cons 02:27
239 Trie 03:17
240 Graph + Tree Traversals 03:57
241 BFS Introduction 02:46
242 DFS Introduction 03:24
243 BFS vs DFS 03:21
244 Exercise: BFS vs DFS 00:45
245 Solution: BFS vs DFS 03:17
246 breadthFirstSearch() 09:16
247 PreOrder, InOrder, PostOrder 05:22
248 depthFirstSearch() 12:04
249 Recursion Introduction 05:37
250 Stack Overflow 06:18
251 Anatomy Of Recursion 10:28
252 Recursive VS Iterative 04:17
253 When To Use Recursion 04:01
254 Recursion Review 02:48
255 Sorting Introduction 07:02
256 The Issue With sort() 06:52
257 Sorting Algorithms 03:39
258 Bubble Sort 03:46
259 Solution: Bubble Sort 05:07
260 Selection Sort 02:40
261 Solution: Selection Sort 02:24
262 Dancing Algorithms 01:37
263 Insertion Sort 02:39
264 Solution: Insertion Sort 02:07
265 Merge Sort and O(n log n) 09:00
266 Solution: Merge Sort 04:45
267 Quick Sort 07:41

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