Get ready to solve modern computing problems with data structures and algorithms
About This Video
In Detail
With the knowledge of data structures and algorithms at your fingertips, you can …
Get ready to solve modern computing problems with data structures and algorithms
About This Video
In Detail
With the knowledge of data structures and algorithms at your fingertips, you can write efficient computer programs to solve real-world problems. This course will help you to get up and running with data structures and algorithms in no time.
The course starts by explaining the concepts of complexity analysis and big O notation. Next, you will become familiar with the concepts of memory and logarithms. Moving along, you will get a solid understanding of data structure concepts, such as arrays, linked lists, stacks, queues, hash tables, trees, heaps, and graphs. Next, you will understand what recursion is and get to grips with a variety of basic and advanced algorithms, such as searching, sorting elementary, sorting advanced, tree traversal, and graph traversal. Towards the end, you will go through a set of interview questions that will enhance your knowledge of data structures and algorithms and prepare you for job interviews.
By the end of this course, you will have gained exceptional knowledge of data structure and algorithms and will have developed skills to apply in the real world.
Who this book is for
This course is designed for anyone who wants to learn the concepts of data structures and algorithms from scratch. Basic knowledge of Python programming is needed to get started with this course.
Chapter 1 : Course Introduction
Course Introduction
Curriculum Walkthrough
Chapter 2 : Big O Notation
Section Introduction
Complexity Analysis
Why We Need Big O Notation?
Big O(n) Complexity
Big O(1) Complexity
Counting Operations
Simplifying Big O - Part 1
Big O(n^2) Complexity
Simplifying Big O - Part 2
Big O(n!) Complexity
Space Complexity – Part 1
Space Complexity – Part 2
Section Summary
Chapter 3 : Essential Concepts - I
Memory
Logarithm
Chapter 4 : Data Structure - Introduction
Introduction to Data Structures
Chapter 5 : Data Structure - Arrays
Array Introduction
Array - Common Operations – Part 1
Array - Common Operations – Part 2
Static versus Dynamic Array - Common Operations – Part 3
Chapter 6 : Data Structures – Linked Lists
Linked Lists
Linked List Complexities
Doubly Linked List
Circular Linked List and Implementing a Linked List
Chapter 7 : Data Structures – Stack and Queue
Stack and Queue
Chapter 8 : Data Structures – Hash Tables
Hash Tables
Chapter 9 : Data Structures - Trees
Trees - Part 1
Trees - Part 2
Binary Tree
Binary Search Tree
Adelson-Vekskii Landis (AVL) Trees versus Red Black Trees
Chapter 10 : Data Structures – Heaps
Heaps
Heap Sort and Priority Queues
Chapter 11 : Data Structures – Tries
Trie - I
Trie - II
Why Are Tries Important?
Chapter 12 : Data Structures – Graphs
Graphs
Chapter 13 : Essential Concepts - II
What is Recursion?
Recursion: Control of a Function – Part 1
Recursion: Tracing Tree – Part 2
Recursion: Understanding a Call Stack – Part 3
Recursion: Tree Recursion – Part 4
Recursion Example - Factorial of a Number
Chapter 14 : Algorithm: Searching
Linear Search
Binary Search
Binary Search Complexity
Implementing Binary Search – Part 1
Implementing Binary Search Implementation – Recursion – Part 2
Chapter 15 : Algorithm: Sorting Elementary
Sorting Algorithm – Introduction
Bubble Sort
Bubble Sort Visualization
Implementing Bubble Sort
Bubble Sort Complexity
Selection Sort
Selection Sort Visualization
Implementing Selection Sort
Selection Sort Complexity
Insertion Sort
Implementing Insertion Sort
Insertion Sort Complexity
Performance Analysis
Chapter 16 : Algorithm: Sorting Advanced
Quick Sort
Quick Sort Complexity
Implementing Quicksort
Merge Sort
Merge Sort Complexity
Implementing Merge Sort
Chapter 17 : Algorithm: Tree Traversals
Tree Traversal
Depth-first Search – (Preorder, Inorder, and Postorder)
Implementing a Binary Tree
Implementing Depth-first Search
Depth-first Search Complexity
Breadth-first Search - Level Order
Implementing Breadth-first Search
Breadth-first Search Complexity
Chapter 18 : Algorithms: Graph Traversal
Graph Traversal
Implementing Graph Animation
Implementing Breadth-first Search
Implementing Depth-first Search
Graph Traversal Complexity
Chapter 19 : Implementations and Interview Questions
Implementing Data Structures
Problem Solving Approach
Chapter 20 : Question 1: Two Sum
Two Sum
Chapter 21 : Question 2: Min Stack
Min Stacks
Implementing a Min Stack
Chapter 22 : Question 3: Max Stack
Max Stacks
Chapter 23 : Question 4: Design of a Linked List
Designing a Linked List – Part I
Designing a Linked List – Part 2
Designing a Linked List – Part 3
Designing a Linked List – Part 4
Chapter 24 : Question 5: Reserve Linked List
Reversing Linked List - I
Reversing Linked List - II
Chapter 25 : Question 6: Constructing a Binary Tree
Traversal (Preorder, Inorder, and Postorder)
Constructing a Binary Tree: From Preorder and Inorder Traversal – Part 1
Constructing a Binary Tree: From Preorder and Inorder Traversal – Part 2
Chapter 26 : Question 7: Invert Binary Tree
Invert Binary Tree – Part 1
Invert Binary Tree – Part 2
Chapter 27 : Question 8: Constructing a Binary Search Tree
Constructing a Binary Search Tree: From Preorder Traversal – Part 1
Constructing a Binary Search Tree: From Preorder Traversal – Part 2
Chapter 28 : Question 9: Detect Capital
Detect Capital
Chapter 29 : Question 10: Reverse Strings
Reverse Strings
Chapter 30 : Question 11: Longest Palindromic Substring
Longest Palindromic Substring – Part 1
Longest Palindromic Substring – Part 2
Chapter 31 : Thank You for Being Here!
Thank You for Being Here!