Master the Coding Interview: Big Tech (FAANG) Interviews

Name: Master the Coding Interview: Big Tech (FAANG) Interviews
Price: 12.75 USD
Availability: InStock

36h 33m 29s

English

Paid

December 4, 2024

You may want a job at a large tech company but feel lost when you face coding interview questions. You are not alone. Many people freeze during these interviews. This course helps you build clear steps to handle these questions with confidence.

You will not try to memorize 100 problems. Instead, you will learn a simple way to spot patterns. This helps you answer new questions on the spot. You will learn how to think, plan, and explain your work in a clear way.

You also join a private online community where many developers share tips and help you as you go.

What You Will Learn

You will work through common interview topics with clear examples. Each topic builds tools you can use in real interviews.

Big O Basics

You will learn how to measure how fast your code runs. This helps you compare ideas and explain trade‑offs during an interview.

Core Data Structures

You will see how to use the most common data structures. You will build them, read them, and pick the best one for each task.

Arrays
Hash tables
Singly and doubly linked lists
Stacks and queues
Binary trees and search trees
Tries and n‑ary trees
Heaps and priority queues
2‑D arrays and matrices
Graphs, adjacency lists, adjacency matrices
Interface design for custom structures

Key Algorithm Ideas

You will learn the main problem‑solving patterns used in coding interviews. You will also see how to break problems down step by step.

Recursion
Sorting and searching
Tree traversal and graph traversal
Breadth‑first search (BFS)
Depth‑first search (DFS)
Divide and conquer
Greedy methods
Dynamic programming
Backtracking

Important Algorithms

You will walk through real interview tasks where these algorithms appear.

Hoare’s quickselect
Floyd’s cycle detection
Bellman‑Ford
Dijkstra’s shortest path
Topological sort

Your Instructors

We are senior engineers who have both given and taken these interviews. We have led teams and worked with many candidates. We know what interviewers search for and how they judge each answer.

Our goal is simple: give you the skills to pass coding interviews with calm and clarity. This skill can shape your career in a strong way. We hope you join us and learn how to handle these interviews with confidence.

About the Author: zerotomastery.io

Whether you are just starting to learn to code or want to advance your skills, Zero To Mastery Academy will teach you React, Javascript, Python, CSS and more to help you advance your career, get hired and succeed at some of the top companies in the world.

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#1: Master The Coding Interview: Big Tech (FAANG) Interview

All Course Lessons (267)

#	Lesson Title	Duration
1	Master The Coding Interview: Big Tech (FAANG) Interview Demo	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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