Description
Ultimate Data Structures and Algorithms: Part 2 is hands-on training in data structures and algorithms to help you code better and faster. Are you also a computer science major but not well versed in data structure and algorithm? Or your job interview due to lack of construction information and lack of good algorithm? If your answer is yes, then this period can completely eliminate your problem.
Data structures and algorithms are models for solving programming problems. Programmers who are familiar with data structures and algorithms are better at solving problems than others. In this course, you will learn about non-linear data structures and topics such as binary tree, AVL tree stack, try and draw a graph. The instructor uses Java to explain data structure and algorithm concepts, but the techniques can be used in any programming language.
Courses taught in this course:
- Introduction to non-linear data structure
- Better and faster coding
- Increase your problem solving skills
- Mastery of basic computer science concepts
- An introduction to the most popular algorithms
- Work through the important concepts of data preparation
- Introduction to binary tree and AVL tree
Course Profile Ultimate Data Structures and Algorithms: Part 2:
- English
- Duration: 6 hours
- Number of lessons: 130
- Education level: intermediate
- Instructor: Mosh Hamedani
- File format: mp4
Course titles
Getting started
1- Introduction (0:50)
2- Source code
Twin trees (73 m)
1- Introduction (0:55)
2- What are trees (5:36)
3. Exercise – Inserting a binary search tree
4- Exercise- Building a tree (2:41)
5- Solution-insert() (7:44)
6- Solution – find() (2:00)
7- Crossing the trees 5:58
8- Exercise – crossing the tree
9- Recursion (5:39)
First passes of depth 5:23
11- Depth and height of knots 7:06
12- minimum value in tree (7:37)
13 – Exercise – Equality check (0:45)
14- Solution – Equality Check (4:08)
Exercise 15 – Validating Binary Search Trees (4:14)
16- Solution – Validating Binary Search Trees (4:18)
17- Exercise – Knots at K-distance (1:48)
18- Solution- Nodes K distance from roots 4:37
Completing Level 19 Orders (2:55)
20- Trees- Exercises
21- Summary (1:22)
AVL trees (49m)
1- Introduction (0:31)
2- Balanced and unbalanced trees (3:01)
3- Turns (5:02)
4- AVL Trees (4:04)
5- Exercise: AVL rotations
6- Exercise- Building an AVL tree (1:11)
7- Solution-insert() (8:47)
8. Exercise – calculating height (1:24)
9- Solution – Calculating Height (2:43)
10- Exercise – Balance Factor (2:04)
11- Solution – Balance factor (4:01)
12- Exercise – Identifying rotations (2:54)
13- Solution – Detecting revolutions (3:32)
14- Exercise – Applying turns (3:51)
15- Solution Implementation Rotations (5:40)
16- AVL-trees- exercises
17- Summary (1:01)
A quick note
Piles (53 m)
1- Introduction (0:21)
2- What are piles (6:28)
3. Exercise – working with piles
Exercise 4 – Building a Pile (1:55)
5- Solution-insert() (8:24)
6- Solution-remove() (7:07)
7- The solution – edge boxes (6:11)
8- Pile Sorting (2:29)
9- priority queues (5:04)
10- Training-Heapify (1:26)
11. The Solution – Heap (7:12)
12- Optimizing the Solution (2:42)
13. Exercise – the largest unit of K. (0:31)
14- The Solution – K.’s Biggest Unit (3:47)
15-piles-exercises
16- Summary (1:19)
Samples (46 m)
1. Introduction (0:30)
2- What are trials (3:50)
3. Exercise – populating a tree
4- Exercise- Building a tree (3:03)
5. Solution – Creating a sample (5:44)
6. Application with hash table (1:50)
7- Better Abstraction (5:28)
Exercise 8 – Word Search (1:12)
9- Solution – Word Search (2:35)
10 – Passes (3:35)
11 – Exercise – Word Removal (1:53)
12- Solution – word removal (8:14)
13- Exercise – Auto Completion (2:51)
14- Solution – Auto-Finish (5:59)
15-experiments-exercises
16- Summary (0:45)
A quick note
Graphs (66 m)
1- Introduction (0:26)
2- What are graphs (2:09)
3- Adjacency Matrix (4:14)
4- list of neighboring places (6:32)
5. Exercise – making a graph (1:50)
6- Solution – Adding nodes and edges (7:34)
7- Solution – Removing Nodes and Edges (4:48)
8- Traversal algorithms (3:58)
9- Exercise- Traversal algorithms
10- Exercise – Depth-First Pass (1:29)
11- Solution – Depth-First Traversal (Recursive) (3:44)
12- Exercise – Depth-First Pass (Iterative) (2:44)
13- Solution – Depth-First Pass (Iterative) (3:59)
14- Exercise – width-first pass (iterative) (1:18)
15- Solution – width first pass (2:41)
16- Exercise – Topological Sorting (5:06)
17- Solution – Topological Sorting (4:05)
18- Training Cycle Detection (Directed Graphs) (3:42)
19- Solution – cycle detection (directed graphs) 6:26
20- Schedule Summary 1:02
Undirected graphs (59 m)
1- Introduction (0:26)
2. Exercise – weighted graphs (1:30)
3- Solution – Weighted Graphs (5:20)
4- object-oriented solution (6:13)
5- Dijkstra’s shortest path algorithm (4:35)
6. Exercise – covering the shortest distance (6:08)
7- Solution – shortest distance (5:27)
8- Solution – Shortest Path (7:53)
9- Training cycle detection (undirected graphs) (2:03)
10- Solution – Cycle Detection (Undirected Graphs) (4:42)
11- Minimum width tree (1:56)
12. Exercise – Prim algorithm (2:45)
13. Solution – Prim’s algorithm (10:39)
14- Thank you
Course prerequisites
In this course, I use Java to teach the concepts, but you can apply these concepts to any programming language. Our focus is on data structures and algorithms, not programming languages and tools.
All you need to complete this course is some basic programming skills. If you know variables, loops, and conditionals, you’re fine. If you need a quick refresher on Java syntax, you can check out the first part of my Java series.
Ideally, you should have taken the first part of this series, as the concepts and exercises in this part are more complex than those covered in the first part.
Snapshots of Ultimate Data Structures and Algorithms: Part 2
Demo Ultimate Data Structures and Algorithms: Part 2
installation manual
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english subtitles
Quality: 1080p
Changes:
Version 2020/1 is compared to 2019/9. added a whole lesson and sub-heading over the course of the year.
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size
6.36 GB