Description

Welcome to the Java Data Structures and Algorithms Masterclass,the most modern, and the most complete Data Structures and Algorithms in Java course on the internet.

At 44+ hours, this is the most comprehensive course online to help you ace your coding interviews and learn about Data Structures and Algorithms in Java. You will see 100+ Interview Questions done at the top technology companies such as Apple,Amazon, Google and Microsoft and how to face Interviews with comprehensive visual explanatory video materials which will bring you closer towards landing the tech job of your dreams!

Learning Java is one of the fastest ways to improve your career prospects as it is one of the most in demand tech skills! This course will help you in better understanding every detailof Data Structures and how algorithms are implemented in high level programming language.

We’ll take you step-by-step through engaging video tutorials and teach you everything you need to succeed as a professional programmer.

After finishing this course, you will be able to:

Learn basic algorithmic techniques such as greedy algorithms, binary search, sorting and dynamic programming to solve programming challenges.

Learn the strengths and weaknesses of a variety of data structures, so you can choose the best data structure for your data and applications

Learn many of the algorithms commonly used to sort data, so your applications will perform efficiently when sorting large datasets

Learn how to apply graph and string algorithms to solve real-world challenges: finding shortest paths on huge maps and assembling genomes from millions of pieces.

Why this course is so special and different from any other resource available online?

This course will take you from very beginning to a very complex and advanced topics in understanding Data Structures and Algorithms!

You will get video lectures explaining concepts clearly with comprehensive visual explanations throughout the course.

You will also see Interview Questions done at the top technology companies such as Apple,Amazon, Google and Microsoft.

I cover everything you need to know about technical interview process!

So whether you are interested in learning the top programming language in the world in-depth and interested in learning the fundamental Algorithms, Data Structures and performance analysis that make up the core foundational skillset of every accomplished programmer/designer or software architect and is excited to ace your next technical interview this is the course for you!

And this is what you get by signing up today:

Lifetime access to 44+ hours of HD quality videos. No monthly subscription. Learn at your own pace, whenever you want

Friendly and fast support in the course Q&A whenever you have questions or get stuck

FULL money back guarantee for 30 days!

This course is designed to help you to achieve your career goals. Whether you are looking to get more into Data Structures and Algorithms , increase your earning potential or just want a job with more freedom, this is the right course for you!

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
Who this course is for:

   Anybody interested in learning more about data structures and algorithms or the technical interview process!
   Self-taught programmers who have a basic knowledge in Java and want to be professional in Data Structure and Algorithm and begin interviewing in tech positions!
   Students currently studying computer science and want supplementary material on Data Structure and Algorithm and interview preparation for after graduation!
   Professional programmers who need practice for upcoming coding interviews.

Requirements

   Basic Java Programming skills

Last Updated 5/2021