CF

The Ultimate Data Structures & Algorithms: Part 2

5h 56m 46s
English
Paid

Data structures and algorithms are patterns for solving problems. Developers who know more about data structures and algorithms are better at solving problems. That’s why companies like Google, Microsoft and Amazon always include interview questions on data structures and algorithms. They want to assess your problem-solving skills. They don't care how many programming languages and frameworks you're familiar with.

By the end of this course…

You’ll be able to:

  • Ace your next coding interview
  • Write better, faster code
  • Become a better developer
  • Improve your problem solving skills
  • Master computer science fundamentals
  • Implement all the essential data structures from scratch
  • Master dozens of popular algorithms

About the Author: Mosh Hamedani (Code with Mosh)

Mosh Hamedani (Code with Mosh) thumbnail

Mosh Hamedani is the founder of Code with Mosh, one of the highest-volume independent online instructors in software education. He has been publishing courses continuously since the early Udemy era and has taught over a million students across his Udemy catalog and his standalone Code with Mosh platform. His teaching style is patient, rigorously structured, and deliberately beginner-tolerant — the courses are widely cited as some of the most accessible introductions to their respective topics.

The Code with Mosh catalog covers an unusually wide span: web development with HTML / CSS / JavaScript / React / Angular / Node.js, Python and Django, C# and .NET, mobile development with React Native and Flutter, SQL and database fundamentals, and the algorithm / data-structure interview prep tracks. Few independent instructors maintain this much breadth at consistent quality.

The CourseFlix listing under this source carries over 35 Code with Mosh courses spanning that range. Material is paid; Code with Mosh runs on per-course pricing on the original platform. Courses are aimed primarily at developers picking up a new technology from a clean start through to working production proficiency.

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#1: Introduction
All Course Lessons (94)
#Lesson TitleDurationAccess
1
Introduction Demo
00:51
2
Introduction
00:56
3
What are Trees
05:37
4
Exercise- Building a Tree
02:42
5
Solution- insert()
07:45
6
Solution- find()
02:01
7
Traversing Trees
05:59
8
Recursion
05:40
9
Depth First Traversals
05:24
10
Depth and Height of Nodes
07:07
11
Minimum Value in a Tree
07:38
12
Exercise- Equality Checking
00:46
13
Solution- Equality Checking
04:09
14
Exercise- Validating Binary Search Trees
04:15
15
Solution- Validating Binary Search Trees
04:19
16
Exercise- Nodes at K Distance
01:49
17
Solution- Nodes at K Distance from the Root
04:38
18
Level Order Traversal
02:56
19
Summary
01:23
20
Introduction
00:32
21
Balanced and Unbalanced Trees
03:02
22
Rotations
05:03
23
AVL Trees
04:05
24
Exercise- Building an AVL Tree
01:12
25
Solution- insert()
08:48
26
Exercise- Height Calculation
01:25
27
Solution- Height Calculation
02:44
28
Exercise- Balance Factor
02:05
29
Solution- Balance Factor
04:02
30
Exercise- Detecting Rotations
02:55
31
Solution- Detecting Rotations
03:34
32
Exercise- Implementing Rotations
03:52
33
Solution- Implementing Rotations
05:41
34
Summary
01:02
35
Introduction
00:22
36
What are Heaps
06:29
37
Exercise- Building a Heap
01:56
38
Solution- insert()
08:25
39
Solution- remove()
07:08
40
Solution - Edge Cases
06:12
41
Heap Sort
02:30
42
Priority Queues
05:05
43
Exercise- Heapify
01:27
44
Solution- Heapify
07:13
45
Solution- Optimization
02:43
46
Exercise- Kth Largest Item
00:32
47
Solution- Kth Largest Item
03:48
48
Summary
01:20
49
Introduction
00:31
50
What are Tries
03:51
51
What are Tries
03:04
52
Solution- Building a Trie
05:45
53
An Implementation with a HashTable
01:51
54
A Better Abstraction
05:29
55
Exercise- Looking Up a Word
01:13
56
Solution- Looking Up a Word
02:36
57
Traversals
03:36
58
Exercise- Removing a Word
01:54
59
Solution- Removing a Word
08:15
60
Exercise- Auto Completion
02:52
61
Solution- Auto Completion
06:00
62
Summary
00:46
63
Introduction
00:27
64
What are Graphs
02:10
65
Adjacency Matrix
04:15
66
Adjacency List
06:33
67
Exercise- Building a Graph
01:51
68
Solution- Adding Nodes and Edges
07:35
69
Solution- Removing Nodes and Edges
04:49
70
Traversal Algorithms
03:59
71
Exercise- Depth-first Traversal (Recursive)
01:30
72
Solution- Depth-first Traversal (Recursive)
03:45
73
Exercise- Depth-first Traversal (Iterative)
02:45
74
Solution- Depth-first Traversal (Iterative)
04:00
75
Exercise- Breadth-first Traversal (Iterative)
01:19
76
Solution- Breadth-first Traversal
02:42
77
Exercise- Topological Sorting
05:07
78
Solution- Topological Sort
04:06
79
Exercise- Cycle Detection (Directed Graphs)
03:43
80
Solution- Cycle Detection (Directed Graphs)
06:27
81
Graphs Summary
01:03
82
Introduction
00:27
83
Exercise- Weighted Graphs
01:31
84
Solution- Weighted Graphs
05:21
85
An Object-oriented Solution
06:14
86
Dijkstra's Shortest Path Algorithm
04:36
87
Exercise- Getting the Shortest Distance
06:09
88
Solution- The Shortest Distance
05:28
89
Solution- Shortest Path
07:54
90
Exercise- Cycle Detection (Undirected Graphs)
02:04
91
Solution- Cycle Detection (Undirected Graphs)
04:43
92
Minimum Spanning Tree
01:57
93
Exercise- Prim's Algorithm
02:46
94
Solution- Prim's Algorithm
10:40
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Frequently asked questions

Are there any prerequisites for enrolling in the course?
This course is designed for individuals with a basic understanding of programming concepts. Familiarity with a programming language is recommended, as the course focuses on implementing data structures and algorithms without language-specific instruction. While prior exposure to data structures is beneficial, it is not mandatory as foundational concepts are covered.
What projects or exercises will I work on during the course?
The course includes numerous practical exercises such as building trees, AVL trees, and heaps. Students will also work on exercises like validating binary search trees, detecting rotations in AVL trees, and implementing heapify operations. These hands-on projects are designed to reinforce theoretical concepts and enhance problem-solving skills.
Who is the target audience for this course?
This course is ideal for software developers who want to enhance their problem-solving skills by mastering data structures and algorithms. It is particularly beneficial for those preparing for technical interviews at companies like Google, Microsoft, and Amazon, where such knowledge is often tested.
How does this course compare to other data structures courses in terms of depth and scope?
This course provides an extensive exploration of data structures such as trees, AVL trees, heaps, and tries. It goes beyond basic concepts by delving into specific problems and practical applications, like heap sort, priority queues, and auto-completion using tries. It is more detailed than entry-level courses, focusing on both the theory and practice of data structures.
What specific tools or platforms will be used in the course?
The course emphasizes understanding and implementing data structures and algorithms without tying to any specific tools or platforms. It focuses on the conceptual understanding and coding of these structures, making it applicable across various programming environments.
What topics are not covered in this course?
While the course covers a wide range of data structures and algorithms, it does not address topics like graph algorithms, dynamic programming, or language-specific data structure libraries. The focus remains on trees, heaps, and tries, along with their associated algorithms and applications.
What is the estimated time commitment for completing the course?
With a total of 94 lessons, the course requires a significant time commitment. While the runtime is not specified, students should allocate time for both the instructional content and the numerous exercises and coding projects. Consistent study and practice will be essential to fully grasp the concepts and applications taught.