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.
The Ultimate Data Structures & Algorithms: Part 2
5h 56m 46s
English
Paid
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 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.
Watch Online 94 lessons
0:00
/ #1: Introduction
All Course Lessons (94)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 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.