Java Data Structures and Algorithms Masterclass

The topics that are covered in this course.

Section 1 – Introduction

• What are Data Structures?
• What is an algorithm?
• Why are Data Structures and Algorithms important?
• Types of Data Structures
• Types of Algorithms

Section 2 – Recursion

• What is Recursion?
• Why do we need recursion?
• How Recursion works?
• Recursive vs Iterative Solutions
• When to use/avoid Recursion?
• How to write Recursion in 3 steps?
• How to find Fibonacci numbers using Recursion?

Section 3 – Cracking Recursion Interview Questions

• Question 1 – Sum of Digits
• Question 2 – Power
• Question 3 – Greatest Common Divisor
• Question 4 – Decimal To Binary

Section 4 – Bonus CHALLENGING Recursion Problems (Exercises)

• power
• factorial
• productofArray
• recursiveRange
• fib
• reverse
• isPalindrome
• someRecursive
• flatten
• captalizeFirst
• nestedEvenSum
• capitalizeWords
• stringifyNumbers
• collectStrings

Section 5 – Big O Notation

• Analogy and Time Complexity
• Big O, Big Theta and Big Omega
• Time complexity examples
• Space Complexity
• Drop the Constants and the non dominant terms
• Add vs Multiply
• How to measure the codes using Big O?
• How to find time complexity for Recursive calls?
• How to measure Recursive Algorithms that make multiple calls?

Section 6 – Top 10 Big O Interview Questions (Amazon, Facebook, Apple and Microsoft)

• Product and Sum
• Print Pairs
• Print Unordered Pairs
• Print Unordered Pairs 2 Arrays
• Print Unordered Pairs 2 Arrays 100000 Units
• Reverse
• O(N)  Equivalents
• Factorial Complexity
• Fibonacci Complexity
• Powers of 2

Section 7 – Arrays

• What is an Array?
• Types of Array
• Arrays in Memory
• Create an Array
• Insertion Operation
• Traversal Operation
• Accessing an element of Array
• Searching for an element in Array
• Deleting an element from Array
• Time and Space complexity of One Dimensional Array
• One Dimensional Array Practice
• Create Two Dimensional Array
• Insertion – Two Dimensional Array
• Accessing an element of Two Dimensional Array
• Traversal – Two Dimensional Array
• Searching for an element in Two Dimensional Array
• Deletion – Two Dimensional Array
• Time and Space complexity of Two Dimensional Array
• When to use/avoid array

Section 8 – Cracking Array Interview Questions (Amazon, Facebook, Apple and Microsoft)

• Question 1 – Missing Number
• Question 2 – Pairs
• Question 3 – Finding a number in an Array
• Question 4 – Max product of two int
• Question 5 – Is Unique
• Question 6 – Permutation
• Question 7 – Rotate Matrix

Section 9 – CHALLENGING Array Problems (Exercises)

• Middle Function
• 2D Lists
• Best Score
• Missing Number
• Duplicate Number
• Pairs

Section 10 – Linked List

• What is a Linked List?
• Linked List vs Arrays
• Types of Linked List
• Linked List in the Memory
• Creation of Singly Linked List
• Insertion in Singly Linked List in Memory
• Insertion in Singly Linked List Algorithm
• Insertion Method in Singly Linked List
• Traversal of Singly Linked List
• Search for a value in Single Linked List
• Deletion of node from Singly Linked List
• Deletion Method in Singly Linked List
• Deletion of entire Singly Linked List
• Time and Space Complexity of Singly Linked List

Section 11 – Circular Singly Linked List

• Creation of Circular Singly Linked List
• Insertion in Circular Singly Linked List
• Insertion Algorithm in Circular Singly Linked List
• Insertion method in Circular Singly Linked List
• Traversal of Circular Singly Linked List
• Searching a node in Circular Singly Linked List
• Deletion of a node from Circular Singly Linked List
• Deletion Algorithm in Circular Singly Linked List
• Method in Circular Singly Linked List
• Deletion of entire Circular Singly Linked List
• Time and Space Complexity of Circular Singly Linked List

Section 12 – Doubly Linked List

• Creation of Doubly Linked List
• Insertion in Doubly Linked List
• Insertion Algorithm in Doubly Linked List
• Insertion Method in Doubly Linked List
• Traversal of Doubly Linked List
• Reverse Traversal of Doubly Linked List
• Searching for a node in Doubly Linked List
• Deletion of a node in Doubly Linked List
• Deletion Algorithm in Doubly Linked List
• Deletion Method in Doubly Linked List
• Deletion of entire Doubly Linked List
• Time and Space Complexity of Doubly Linked List

Section 13 – Circular Doubly Linked List

• Creation of Circular Doubly Linked List
• Insertion in Circular Doubly Linked List
• Insertion Algorithm in Circular Doubly Linked List
• Insertion Method in Circular Doubly Linked List
• Traversal of Circular Doubly Linked List
• Reverse Traversal of Circular Doubly Linked List
• Search for a node in Circular Doubly Linked List
• Delete a node from Circular Doubly Linked List
• Deletion Algorithm in Circular Doubly Linked List
• Deletion Method in Circular Doubly Linked List
• Entire Circular Doubly Linked List
• Time and Space Complexity of Circular Doubly Linked List
• Time Complexity of Linked List vs Arrays

Section 14 – Cracking Linked List Interview Questions (Amazon, Facebook, Apple and Microsoft)

• Linked List Class
• Question 1 – Remove Dups
• Question 2 – Return Kth to Last
• Question 3 – Partition
• Question 4 – Sum Linked Lists
• Question 5 – Intersection

Section 15 – Stack

• What is a Stack?
• What and Why of Stack?
• Stack Operations
• Stack using Array vs Linked List
• Stack Operations using Array (Create, isEmpty, isFull)
• Stack Operations using Array (Push, Pop, Peek, Delete)
• Time and Space Complexity of Stack using Array
• Stack Operations using Linked List
• Stack methods – Push , Pop, Peek, Delete and isEmpty using Linked List
• Time and Space Complexity of Stack using Linked List
• When to Use/Avoid Stack
• Stack Quiz

Section 16 – Queue

• What is a Queue?
• Linear Queue Operations using Array
• Create, isFull, isEmpty and enQueue methods using Linear Queue Array
• Dequeue, Peek and Delete Methods using Linear Queue Array
• Time and Space Complexity of Linear Queue using Array
• Why Circular Queue?
• Circular Queue Operations using Array
• Create, Enqueue, isFull and isEmpty Methods in Circular Queue using Array
• Dequeue, Peek and Delete Methods in Circular Queue using Array
• Time and Space Complexity of Circular Queue using Array
• Queue Operations using Linked List
• Create, Enqueue and isEmpty Methods in Queue using Linked List
• Dequeue, Peek and Delete Methods in Queue using Linked List
• Time and Space Complexity of Queue using Linked List
• Array vs Linked List Implementation
• When to Use/Avoid Queue?

Section 17 – Cracking Stack and Queue Interview Questions (Amazon,Facebook, Apple, Microsoft)

• Question 1 – Three in One
• Question 2 – Stack Minimum
• Question 3 – Stack of Plates
• Question 4 – Queue via Stacks
• Question 5 – Animal Shelter

Section 18 – Tree / Binary Tree

• What is a Tree?
• Why Tree?
• Tree Terminology
• How to create a basic tree in Java?
• Binary Tree
• Types of Binary Tree
• Binary Tree Representation
• Create Binary Tree (Linked List)
• PreOrder Traversal Binary Tree (Linked List)
• InOrder Traversal Binary Tree (Linked List)
• PostOrder Traversal Binary Tree (Linked List)
• LevelOrder Traversal Binary Tree (Linked List)
• Searching for a node in Binary Tree (Linked List)
• Inserting a node in Binary Tree (Linked List)
• Delete a node from Binary Tree (Linked List)
• Delete entire Binary Tree (Linked List)
• Create Binary Tree (Array)
• Insert a value Binary Tree (Array)
• Search for a node in Binary Tree (Array)
• PreOrder Traversal Binary Tree (Array)
• InOrder Traversal Binary Tree (Array)
• PostOrder Traversal Binary Tree (Array)
• Level Order Traversal Binary Tree (Array)
• Delete a node from Binary Tree (Array)
• Entire Binary Tree (Array)
• Linked List vs Python List Binary Tree

Section 19 – Binary Search Tree

• What is a Binary Search Tree? Why do we need it?
• Create a Binary Search Tree
• Insert a node to BST
• Traverse BST
• Search in BST
• Delete a node from BST
• Delete entire BST
• Time and Space complexity of BST

Section 20 – AVL Tree

• What is an AVL Tree?
• Why AVL Tree?
• Common Operations on AVL Trees
• Insert a node in AVL (Left Left Condition)
• Insert a node in AVL (Left Right Condition)
• Insert a node in AVL (Right Right Condition)
• Insert a node in AVL (Right Left Condition)
• Insert a node in AVL (all together)
• Insert a node in AVL (method)
• Delete a node from AVL (LL, LR, RR, RL)
• Delete a node from AVL (all together)
• Delete a node from AVL (method)
• Delete entire AVL
• Time and Space complexity of AVL Tree

Section 21 – Binary Heap

• What is Binary Heap? Why do we need it?
• Common operations (Creation, Peek, sizeofheap) on Binary Heap
• Insert a node in Binary Heap
• Extract a node from Binary Heap
• Delete entire Binary Heap
• Time and space complexity of Binary Heap

Section 22 – Trie

• What is a Trie? Why do we need it?
• Common Operations on Trie (Creation)
• Insert a string in Trie
• Search for a string in Trie
• Delete a string from Trie
• Practical use of Trie

Section 23 – Hashing

• What is Hashing? Why do we need it?
• Hashing Terminology
• Hash Functions
• Types of Collision Resolution Techniques
• Hash Table is Full
• Pros and Cons of Resolution Techniques
• Practical Use of Hashing
• Hashing vs Other Data structures

Section 24 – Sort Algorithms

• What is Sorting?
• Types of Sorting
• Sorting Terminologies
• Bubble Sort
• Selection Sort
• Insertion Sort
• Bucket Sort
• Merge Sort
• Quick Sort
• Heap Sort
• Comparison of Sorting Algorithms

Section 25 – Searching Algorithms

• Introduction to Searching Algorithms
• Linear Search
• Linear Search in Python
• Binary Search
• Binary Search in Python
• Time Complexity of Binary Search

Section 26 – Graph Algorithms

• What is a Graph? Why Graph?
• Graph Terminology
• Types of Graph
• Graph Representation
• Graph in Java using Adjacency Matrix
• Graph in Java using Adjacency List

Section 27 – Graph Traversal

• Breadth First Search Algorithm (BFS)
• Breadth First Search Algorithm (BFS) in Java – Adjacency Matrix
• Breadth First Search Algorithm (BFS) in Java – Adjacency List
• Time Complexity of Breadth First Search (BFS) Algorithm
• Depth First Search (DFS) Algorithm
• Depth First Search (DFS) Algorithm in Java – Adjacency List
• Depth First Search (DFS) Algorithm in Java – Adjacency Matrix
• Time Complexity of Depth First Search (DFS) Algorithm
• BFS Traversal vs DFS Traversal

Section 28 – Topological Sort

• What is Topological Sort?
• Topological Sort Algorithm
• Topological Sort using Adjacency List
• Topological Sort using Adjacency Matrix
• Time and Space Complexity of Topological Sort

Section 29 – Single Source Shortest Path Problem

• SWhat is Single Source Shortest Path Problem?
• Breadth First Search (BFS) for Single Source Shortest Path Problem (SSSPP)
• BFS for SSSPP in Java using Adjacency List
• BFS for SSSPP in Java using Adjacency Matrix
• Time and Space Complexity of BFS for SSSPP
• Why does BFS not work with Weighted Graph?
• Why does DFS not work for SSSP?

Section 30 – Dijkstra’s Algorithm

• Dijkstra’s Algorithm for SSSPP
• Dijkstra’s Algorithm in Java – 1
• Dijkstra’s Algorithm in Java – 2
• Dijkstra’s Algorithm with Negative Cycle

Section 31 – Bellman Ford Algorithm

• Bellman Ford Algorithm
• Bellman Ford Algorithm with negative cycle
• Why does Bellman Ford run V-1 times?
• Bellman Ford in Python
• BFS vs Dijkstra vs Bellman Ford

Section 32 – All Pairs Shortest Path Problem

• All pairs shortest path problem
• Dry run for All pair shortest path

Section 33 – Floyd Warshall

• Floyd Warshall Algorithm
• Why Floyd Warshall?
• Floyd Warshall with negative cycle,
• Floyd Warshall in Java,
• BFS vs Dijkstra vs Bellman Ford vs Floyd Warshall,

Section 34 – Minimum Spanning Tree

• Minimum Spanning Tree,
• Disjoint Set,
• Disjoint Set in Java,

Section 35 – Kruskal’s and Prim’s Algorithms

• Kruskal Algorithm,
• Kruskal Algorithm in Python,
• Prim’s Algorithm,
• Prim’s Algorithm in Python,
• Prim’s vs Kruskal

Section 36 – Cracking Graph and Tree Interview Questions (Amazon,Facebook, Apple, Microsoft)

Section 37 – Greedy Algorithms

• What is Greedy Algorithm?
• Well known Greedy Algorithms
• Activity Selection Problem
• Activity Selection Problem in Python
• Coin Change Problem
• Coin Change Problem in Python
• Fractional Knapsack Problem
• Fractional Knapsack Problem in Python

Section 38 – Divide and Conquer Algorithms

• What is a Divide and Conquer Algorithm?
• Common Divide and Conquer algorithms
• How to solve Fibonacci series using Divide and Conquer approach?
• Number Factor
• Number Factor in Java
• House Robber
• House Robber Problem in Java
• Convert one string to another
• Convert One String to another in Java
• Zero One Knapsack problem
• Zero One Knapsack problem in Java
• Longest Common Sequence Problem
• Longest Common Subsequence in Java
• Longest Palindromic Subsequence Problem
• Longest Palindromic Subsequence in Java
• Minimum cost to reach the Last cell problem
• Minimum Cost to reach the Last Cell in 2D array using Java
• Number of Ways to reach the Last Cell with given Cost
• Number of Ways to reach the Last Cell with given Cost in Java

Section 39 – Dynamic Programming

• What is Dynamic Programming? (Overlapping property)
• Where does the name of DC come from?
• Top Down with Memoization
• Bottom Up with Tabulation
• Top Down vs Bottom Up
• Is Merge Sort Dynamic Programming?
• Number Factor Problem using Dynamic Programming
• Number Factor : Top Down and Bottom Up
• House Robber Problem using Dynamic Programming
• House Robber : Top Down and Bottom Up
• Convert one string to another using Dynamic Programming
• Convert String using Bottom Up
• Zero One Knapsack using Dynamic Programming
• Zero One Knapsack – Top Down
• Zero One Knapsack – Bottom Up

Section 40 – CHALLENGING Dynamic Programming Problems

• Longest repeated Subsequence Length problem
• Longest Common Subsequence Length problem
• Longest Common Subsequence  problem
• Diff Utility
• Shortest Common Subsequence  problem
• Length of Longest Palindromic Subsequence
• Subset Sum Problem
• Egg Dropping Puzzle
• Maximum Length Chain of Pairs

Section 41 – A Recipe for Problem Solving

• Introduction
• Step 1 – Understand the problem
• Step 2 – Examples
• Step 3 – Break it Down
• Step 4 – Solve or Simplify
• Step 5 – Look Back and Refactor

Section 41 – Wild West