What you’ll learn

Learn, implement, and use different Data Structures

Learn, implement and use different Algorithms

Become a better developer by mastering computer science fundamentals

Learn everything you need to ace difficult coding interviews

Cracking the Coding Interview with 100+ questions with explanations

Time and Space Complexity of Data Structures and Algorithms

Recursion

Big O

Dynamic Programming

Divide and Conquer Algorithms

Graph Algorithms

Greedy Algorithms
Requirements

Basic Java Programming skills
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 detail of Data Structures and how algorithms are implemented in high level programming language.
We’ll take you stepbystep 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 realworld 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 indepth 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 V1 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!
 Selftaught 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.
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