Data Structures and Algorithms from Zero to Hero and Crack Top Companies 100+ Interview questions (Python Coding)

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

At 40+ hours, this is the most comprehensive Free 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 Free will help you in better understanding every detail** **of Data Structures and how algorithms are implemented in high level programming language.

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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.

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

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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

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The topics that are covered in this Free.

**Section 1 – Introduction**

**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)**

**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**

**Section 28 – Searching Algorithms**

**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