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    The Complete Data Structures and Algorithms Course in Python for student

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    The Complete Data Structures and Algorithms Course in Python - Online Course Download
    The Complete Data Structures and Algorithms Course in Python - 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

    Requirements :

    Basic Python Programming skills

    Description :

    Welcome to the Complete Data Structures and Algorithms in Python Bootcamp, the most modern, and the most complete Data Structures and Algorithms in Python course on the internet.

    At 40+ hours, this is the most comprehensive course online to help you ace your coding interviews and learn about Data Structures and Algorithms in Python. 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 Python 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 40+ 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!

    Who is this course for?

    Self-taught programmers who have a basic knowledge in Python and want to be professional in Data Structures and Algorithms and begin interviewing in tech positions!

    As well as students currently studying computer science and want supplementary material on Data Structures and Algorithms and interview preparation for after graduation!

    As well as professional programmers who need practice for upcoming coding interviews.

    And finally anybody interested in learning more about data structures and algorithms or the technical interview process!

    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 – Python Lists

    What is a List? How to create it?

    Accessing/Traversing a list

    Update/Insert a List

    Slice/ from a List

    Searching for an element in a List

    List Operations/Functions

    Lists and strings

    Common List pitfalls and ways to avoid them

    Lists vs Arrays

    Time and Space Complexity of List

    List Interview Questions

    Section 9 – Cracking Array/List 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 10 – CHALLENGING Array/List Problems (Exercises)

    Middle Function

    2D Lists

    Best Score

    Missing Number

    Duplicate Number

    Pairs

    Section 11 – Dictionaries

    What is a Dictionary?

    Create a Dictionary

    Dictionaries in memory

    Insert /Update an element in a Dictionary

    Traverse through a Dictionary

    Search for an element in a Dictionary

    Delete / Remove an element from a Dictionary

    Dictionary Methods

    Dictionary operations/ built in functions

    Dictionary vs List

    Time and Space Complexity of a Dictionary

    Dictionary Interview Questions

    Section 12 – Tuples

    What is a Tuple? How to create it?

    Tuples in Memory / Accessing an element of Tuple

    Traversing a Tuple

    Search for an element in Tuple

    Tuple Operations/Functions

    Tuple vs List

    Time and Space complexity of Tuples

    Tuple Questions

    Section 13 – 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 14 – 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 15 – 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 16 – 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 17 – 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 18 – Stack

    What is a Stack?

    Stack Operations

    Create Stack using List without size limit

    Operations on Stack using List (push, pop, peek, isEmpty, )

    Create Stack with limit (pop, push, peek, isFull, isEmpty, )

    Create Stack using Linked List

    Operation on Stack using Linked List (pop, push, peek, isEmpty, )

    Time and Space Complexity of Stack using Linked List

    When to use/avoid Stack

    Stack Quiz

    Section 19 – Queue

    What is Queue?

    Queue using Python List – no size limit

    Queue using Python List – no size limit , operations (enqueue, dequeue, peek)

    Circular Queue – Python List

    Circular Queue – Python List, Operations (enqueue, dequeue, peek, )

    Queue – Linked List

    Queue – Linked List, Operations (Create, Enqueue)

    Queue – Linked List, Operations (Dequeue(), isEmpty, Peek)

    Time and Space complexity of Queue using Linked List

    List vs Linked List Implementation

    Collections Module

    Queue Module

    Multiprocessing module

    Section 20 – 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 21 – Tree / Binary Tree

    What is a Tree?

    Why Tree?

    Tree Terminology

    How to create a basic tree in Python?

    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 (Python List)

    Insert a value Binary Tree (Python List)

    Search for a node in Binary Tree (Python List)

    PreOrder Traversal Binary Tree (Python List)

    InOrder Traversal Binary Tree (Python List)

    PostOrder Traversal Binary Tree (Python List)

    Level Order Traversal Binary Tree (Python List)

    Delete a node from Binary Tree (Python List)

    Entire Binary Tree (Python List)

    Linked List vs Python List Binary Tree

    Section 22 – 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 23 – 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 24 – 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 25 – 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 26 – 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 27 – 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 28 – Searching Algorithms

    Introduction to Searching Algorithms

    Linear Search

    Linear Search in Python

    Binary Search

    Binary Search in Python

    Time Complexity of Binary Search

    Section 29 – Graph Algorithms

    What is a Graph? Why Graph?

    Graph Terminology

    Types of Graph

    Graph Representation

    Create a graph using Python

    Graph traversal – BFS

    BFS Traversal in Python

    Graph Traversal – DFS

    DFS Traversal in Python

    BFS Traversal vs DFS Traversal

    Topological Sort

    Topological Sort Algorithm

    Topological Sort in Python

    Single Source Shortest Path Problem (SSSPP)

    BFS for Single Source Shortest Path Problem (SSSPP)

    BFS for Single Source Shortest Path Problem (SSSPP) in Python

    Why does BFS not work with weighted Graphs?

    Why does DFS not work for SSSP?

    Dijkstra’s Algorithm for SSSP

    Dijkstra’s Algorithm in Python

    Dijkstra Algorithm with negative cycle

    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

    All pairs shortest path problem

    Dry run for All pair shortest path

    Floyd Warshall Algorithm

    Why Floyd Warshall?

    Floyd Warshall with negative cycle,

    Floyd Warshall in Python,

    BFS vs Dijkstra vs Bellman Ford vs Floyd Warshall,

    Minimum Spanning Tree,

    Disjoint Set,

    Disjoint Set in Python,

    Kruskal Algorithm,

    Kruskal Algorithm in Python,

    Prim’s Algorithm,

    Prim’s Algorithm in Python,

    Prim’s vs Kruskal

    Section 30 – 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 31 – 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 Python

    House Robber

    House Robber Problem in Python

    Convert one string to another

    Convert One String to another in Python

    Zero One Knapsack problem

    Zero One Knapsack problem in Python

    Longest Common Sequence Problem

    Longest Common Subsequence in Python

    Longest Palindromic Subsequence Problem

    Longest Palindromic Subsequence in Python

    Minimum cost to reach the Last cell problem

    Minimum Cost to reach the Last Cell in 2D array using Python

    Number of Ways to reach the Last Cell with given Cost

    Number of Ways to reach the Last Cell with given Cost in Python

    Section 32 – 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 33 – 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 34 – 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

    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 Python 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 :

    41 hours on-demand video
    111 articles
    113 downloadable resources
    2 practice tests
    68 coding exercises
    Full lifetime access
    Access on mobile and TV
    Certificate of completion

    View Demo

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