The Complete Data Structures and Algorithms Course in Python

100+ DSA Interview Questions for Cracking FAANG with Animated Examples for Deeper Understanding and Faster Learning

**Preview this Course**

**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

**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.