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    Java Data Structures And Algorithms Masterclass for Student

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    Java Data Structures And Algorithms Masterclass - Online Course Download
    Java Data Structures And Algorithms Masterclass - Online Course Download
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    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 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.

    Course Size Details :

    45 hours on-demand video
    74 articles
    96 downloadable resources
    64 coding exercises
    Full lifetime access
    Access on mobile and TV
    Certificate of completion

    View Demo

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