The Complete Data Structures and Algorithms Course in Python

Welcome to the Full Information Buildings and Algorithms in Python Bootcamp, probably the most trendy, and probably the most full Information Buildings and Algorithms in Python course on the web.

At 40+ hours, that is probably the most complete course on-line that will help you ace your coding interviews and study Information Buildings and Algorithms in Python. You will note 100+ Interview Questions finished on the prime know-how corporations equivalent to Apple,Amazon, Google and Microsoft and find out how to face Interviews with complete visible explanatory video supplies which is able to deliver you nearer in direction of touchdown the tech job of your goals!

Studying Python is without doubt one of the quickest methods to enhance your profession prospects because it is without doubt one of the most in demand tech expertise! This course will enable you to in higher understanding each element of Information Buildings and the way algorithms are carried out in excessive degree programming language.

We’ll take you step-by-step by means of partaking video tutorials and educate you all the things you’ll want to succeed as an expert programmer.

After ending this course, it is possible for you to to:

Study fundamental algorithmic methods equivalent to grasping algorithms, binary search, sorting and dynamic programming to unravel programming challenges.

Study the strengths and weaknesses of a wide range of information buildings, so you’ll be able to select one of the best information construction in your information and functions

Study most of the algorithms generally used to type information, so your functions will carry out effectively when sorting massive datasets

Discover ways to apply graph and string algorithms to unravel real-world challenges: discovering shortest paths on large maps and assembling genomes from thousands and thousands of items.

Why this course is so particular and totally different from every other useful resource accessible on-line?

This course will take you from very starting to a really advanced and superior subjects in understanding Information Buildings and Algorithms!

You’re going to get video lectures explaining ideas clearly with complete visible explanations all through the course.

Additionally, you will see Interview Questions finished on the prime know-how corporations equivalent to Apple,Amazon, Google and Microsoft.

I cowl all the things you’ll want to learn about technical interview course of!

So whether or not you have an interest in studying the prime programming language on the planet in-depth

And all for studying the elemental Algorithms, Information Buildings and efficiency evaluation that make up the core foundational skillset of each completed programmer/designer or software program architect and is worked up to ace your subsequent technical interview that is the course for you!

And that is what you get by signing up right now:

Lifetime entry to 40+ hours of HD high quality movies. No month-to-month subscription. Study at your personal tempo, everytime you need

Pleasant and quick assist within the course Q&A at any time when you might have questions or get caught

FULL a reimbursement assure for 30 days!

Who is that this course for?

Self-taught programmers who’ve a fundamental information in Python and wish to be skilled in Information Buildings and Algorithms and start interviewing in tech positions!

In addition to college students at present learning laptop science and wish supplementary materials on Information Buildings and Algorithms and interview preparation for after commencement!

In addition to skilled programmers who want apply for upcoming coding interviews.

And at last anyone all for studying extra about information buildings and algorithms or the technical interview course of!

This course is designed that will help you to attain your profession targets. Whether or not you wish to get extra into Information Buildings and Algorithms , enhance your incomes potential or simply need a job with extra freedom, that is the suitable course for you!

The subjects which are coated on this course.

Part 1 – Introduction

• What are Information Buildings?
• What’s an algorithm?
• Why are Information Buildings and Algorithms essential?
• Sorts of Information Buildings
• Sorts of Algorithms

Part 2 – Recursion

• What’s Recursion?
• Why do we’d like recursion?
• How Recursion works?
• Recursive vs Iterative Options
• When to make use of/keep away from Recursion?
• How one can write Recursion in 3 steps?
• How one can discover Fibonacci numbers utilizing Recursion?

Part 3 – Cracking Recursion Interview Questions

• Query 1 – Sum of Digits
• Query 2 – Energy
• Query 3 – Best Widespread Divisor
• Query 4 – Decimal To Binary

Part 4 – Bonus CHALLENGING Recursion Issues (Workout routines)

• energy
• factorial
• productofArray
• recursiveRange
• fib
• reverse
• isPalindrome
• someRecursive
• flatten
• captalizeFirst
• nestedEvenSum
• capitalizeWords
• stringifyNumbers
• collectStrings

Part 5 – Huge O Notation

• Analogy and Time Complexity
• Huge O, Huge Theta and Huge Omega
• Time complexity examples
• House Complexity
• Drop the Constants and the non dominant phrases
• Add vs Multiply
• How one can measure the codes utilizing Huge O?
• How one can discover time complexity for Recursive calls?
• How one can measure Recursive Algorithms that make a number of calls?

Part 6 – Prime 10 Huge O Interview Questions (Amazon, Fb, Apple and Microsoft)

• Product and Sum
• Print Pairs
• Print Unordered Pairs
• Print Unordered Pairs 2 Arrays
• Print Unordered Pairs 2 Arrays 100000 Items
• Reverse
• O(N)  Equivalents
• Factorial Complexity
• Fibonacci Complexity
• Powers of two

Part 7 – Arrays

• What’s an Array?
• Sorts of Array
• Arrays in Reminiscence
• Create an Array
• Insertion Operation
• Traversal Operation
• Accessing a component of Array
• Looking for a component in Array
• Deleting a component from Array
• Time and House complexity of One Dimensional Array
• One Dimensional Array Apply
• Create Two Dimensional Array
• Insertion – Two Dimensional Array
• Accessing a component of Two Dimensional Array
• Traversal – Two Dimensional Array
• Looking for a component in Two Dimensional Array
• Deletion – Two Dimensional Array
• Time and House complexity of Two Dimensional Array
• When to make use of/keep away from array

Part 8 – Python Lists

• What’s a Listing? How one can create it?
• Accessing/Traversing an inventory
• Replace/Insert a Listing
• Slice/ from a Listing
• Looking for a component in a Listing
• Listing Operations/Features
• Lists and strings
• Widespread Listing pitfalls and methods to keep away from them
• Lists vs Arrays
• Time and House Complexity of Listing
• Listing Interview Questions

Part 9 – Cracking Array/Listing Interview Questions (Amazon, Fb, Apple and Microsoft)

• Query 1 – Lacking Quantity
• Query 2 – Pairs
• Query 3 – Discovering a quantity in an Array
• Query 4 – Max product of two int
• Query 5 – Is Distinctive
• Query 6 – Permutation
• Query 7 – Rotate Matrix

Part 10 – CHALLENGING Array/Listing Issues (Workout routines)

• Center Perform
• 2D Lists
• Greatest Rating
• Lacking Quantity
• Duplicate Quantity
• Pairs

Part 11 – Dictionaries

• What’s a Dictionary?
• Create a Dictionary
• Dictionaries in reminiscence
• Insert /Replace a component in a Dictionary
• Traverse by means of a Dictionary
• Seek for a component in a Dictionary
• Delete / Take away a component from a Dictionary
• Dictionary Strategies
• Dictionary operations/ in-built capabilities
• Dictionary vs Listing
• Time and House Complexity of a Dictionary
• Dictionary Interview Questions

Part 12 – Tuples

• What’s a Tuple? How one can create it?
• Tuples in Reminiscence / Accessing a component of Tuple
• Traversing a Tuple
• Seek for a component in Tuple
• Tuple Operations/Features
• Tuple vs Listing
• Time and House complexity of Tuples
• Tuple Questions

Part 13 – Linked Listing

• What’s a Linked Listing?
• Linked Listing vs Arrays
• Sorts of Linked Listing
• Linked Listing within the Reminiscence
• Creation of Singly Linked Listing
• Insertion in Singly Linked Listing in Reminiscence
• Insertion in Singly Linked Listing Algorithm
• Insertion Methodology in Singly Linked Listing
• Traversal of Singly Linked Listing
• Seek for a price in Single Linked Listing
• Deletion of node from Singly Linked Listing
• Deletion Methodology in Singly Linked Listing
• Deletion of whole Singly Linked Listing
• Time and House Complexity of Singly Linked Listing

Part 14 – Round Singly Linked Listing

• Creation of Round Singly Linked Listing
• Insertion in Round Singly Linked Listing
• Insertion Algorithm in Round Singly Linked Listing
• Insertion methodology in Round Singly Linked Listing
• Traversal of Round Singly Linked Listing
• Looking out a node in Round Singly Linked Listing
• Deletion of a node from Round Singly Linked Listing
• Deletion Algorithm in Round Singly Linked Listing
• Methodology in Round Singly Linked Listing
• Deletion of whole Round Singly Linked Listing
• Time and House Complexity of Round Singly Linked Listing

Part 15 – Doubly Linked Listing

• Creation of Doubly Linked Listing
• Insertion in Doubly Linked Listing
• Insertion Algorithm in Doubly Linked Listing
• Insertion Methodology in Doubly Linked Listing
• Traversal of Doubly Linked Listing
• Reverse Traversal of Doubly Linked Listing
• Looking for a node in Doubly Linked Listing
• Deletion of a node in Doubly Linked Listing
• Deletion Algorithm in Doubly Linked Listing
• Deletion Methodology in Doubly Linked Listing
• Deletion of whole Doubly Linked Listing
• Time and House Complexity of Doubly Linked Listing

Part 16 – Round Doubly Linked Listing

• Creation of Round Doubly Linked Listing
• Insertion in Round Doubly Linked Listing
• Insertion Algorithm in Round Doubly Linked Listing
• Insertion Methodology in Round Doubly Linked Listing
• Traversal of Round Doubly Linked Listing
• Reverse Traversal of Round Doubly Linked Listing
• Seek for a node in Round Doubly Linked Listing
• Delete a node from Round Doubly Linked Listing
• Deletion Algorithm in Round Doubly Linked Listing
• Deletion Methodology in Round Doubly Linked Listing
• Whole Round Doubly Linked Listing
• Time and House Complexity of Round Doubly Linked Listing
• Time Complexity of Linked Listing vs Arrays

Part 17 – Cracking Linked Listing Interview Questions (Amazon, Fb, Apple and Microsoft)

• Linked Listing Class
• Query 1 – Take away Dups
• Query 2 – Return Kth to Final
• Query 3 – Partition
• Query 4 – Sum Linked Lists
• Query 5 – Intersection

Part 18 – Stack

• What’s a Stack?
• Stack Operations
• Create Stack utilizing Listing with out dimension restrict
• Operations on Stack utilizing Listing (push, pop, peek, isEmpty, )
• Create Stack with restrict (pop, push, peek, isFull, isEmpty, )
• Create Stack utilizing Linked Listing
• Operation on Stack utilizing Linked Listing (pop, push, peek, isEmpty, )
• Time and House Complexity of Stack utilizing Linked Listing
• When to make use of/keep away from Stack
• Stack Quiz

Part 19 – Queue

• What’s Queue?
• Queue utilizing Python Listing – no dimension restrict
• Queue utilizing Python Listing – no dimension restrict , operations (enqueue, dequeue, peek)
• Round Queue – Python Listing
• Round Queue – Python Listing, Operations (enqueue, dequeue, peek, )
• Queue – Linked Listing
• Queue – Linked Listing, Operations (Create, Enqueue)
• Queue – Linked Listing, Operations (Dequeue(), isEmpty, Peek)
• Time and House complexity of Queue utilizing Linked Listing
• Listing vs Linked Listing Implementation
• Collections Module
• Queue Module
• Multiprocessing module

Part 20 – Cracking Stack and Queue Interview Questions (Amazon,Fb, Apple, Microsoft)

• Query 1 – Three in One
• Query 2 – Stack Minimal
• Query 3 – Stack of Plates
• Query 4 – Queue by way of Stacks
• Query 5 – Animal Shelter

Part 21 – Tree / Binary Tree

• What’s a Tree?
• Why Tree?
• Tree Terminology
• How one can create a fundamental tree in Python?
• Binary Tree
• Sorts of Binary Tree
• Binary Tree Illustration
• Create Binary Tree (Linked Listing)
• PreOrder Traversal Binary Tree (Linked Listing)
• InOrder Traversal Binary Tree (Linked Listing)
• PostOrder Traversal Binary Tree (Linked Listing)
• LevelOrder Traversal Binary Tree (Linked Listing)
• Looking for a node in Binary Tree (Linked Listing)
• Inserting a node in Binary Tree (Linked Listing)
• Delete a node from Binary Tree (Linked Listing)
• Delete whole Binary Tree (Linked Listing)
• Create Binary Tree (Python Listing)
• Insert a price Binary Tree (Python Listing)
• Seek for a node in Binary Tree (Python Listing)
• PreOrder Traversal Binary Tree (Python Listing)
• InOrder Traversal Binary Tree (Python Listing)
• PostOrder Traversal Binary Tree (Python Listing)
• Stage Order Traversal Binary Tree (Python Listing)
• Delete a node from Binary Tree (Python Listing)
• Whole Binary Tree (Python Listing)
• Linked Listing vs Python Listing Binary Tree

Part 22 – Binary Search Tree

• What’s a Binary Search Tree? Why do we’d like it?
• Create a Binary Search Tree
• Insert a node to BST
• Traverse BST
• Search in BST
• Delete a node from BST
• Delete whole BST
• Time and House complexity of BST

Part 23 – AVL Tree

• What’s an AVL Tree?
• Why AVL Tree?
• Widespread Operations on AVL Bushes
• Insert a node in AVL (Left Left Situation)
• Insert a node in AVL (Left Proper Situation)
• Insert a node in AVL (Proper Proper Situation)
• Insert a node in AVL (Proper Left Situation)
• Insert a node in AVL (all collectively)
• Insert a node in AVL (methodology)
• Delete a node from AVL (LL, LR, RR, RL)
• Delete a node from AVL (all collectively)
• Delete a node from AVL (methodology)
• Delete whole AVL
• Time and House complexity of AVL Tree

Part 24 – Binary Heap

• What’s Binary Heap? Why do we’d like it?
• Widespread operations (Creation, Peek, sizeofheap) on Binary Heap
• Insert a node in Binary Heap
• Extract a node from Binary Heap
• Delete whole Binary Heap
• Time and area complexity of Binary Heap

Part 25 – Trie

• What’s a Trie? Why do we’d like it?
• Widespread Operations on Trie (Creation)
• Insert a string in Trie
• Seek for a string in Trie
• Delete a string from Trie
• Sensible use of Trie

Part 26 – Hashing

• What’s Hashing? Why do we’d like it?
• Hashing Terminology
• Hash Features
• Sorts of Collision Decision Methods
• Hash Desk is Full
• Professionals and Cons of Decision Methods
• Sensible Use of Hashing
• Hashing vs Different Information buildings

Part 27 – Kind Algorithms

• What’s Sorting?
• Sorts of Sorting
• Sorting Terminologies
• Bubble Kind
• Choice Kind
• Insertion Kind
• Bucket Kind
• Merge Kind
• Fast Kind
• Heap Kind
• Comparability of Sorting Algorithms

Part 28 – Looking out Algorithms

• Introduction to Looking out Algorithms
• Linear Search
• Linear Search in Python
• Binary Search
• Binary Search in Python
• Time Complexity of Binary Search

Part 29 – Graph Algorithms

• What’s a Graph? Why Graph?
• Graph Terminology
• Sorts of Graph
• Graph Illustration
• Create a graph utilizing Python
• Graph traversal – BFS
• BFS Traversal in Python
• Graph Traversal – DFS
• DFS Traversal in Python
• BFS Traversal vs DFS Traversal
• Topological Kind
• Topological Kind Algorithm
• Topological Kind in Python
• Single Supply Shortest Path Downside (SSSPP)
• BFS for Single Supply Shortest Path Downside (SSSPP)
• BFS for Single Supply Shortest Path Downside (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 detrimental cycle
• Bellman Ford Algorithm
• Bellman Ford Algorithm with detrimental cycle
• Why does Bellman Ford run V-1 occasions?
• Bellman Ford in Python
• BFS vs Dijkstra vs Bellman Ford
• All pairs shortest path drawback
• Dry run for All pair shortest path
• Floyd Warshall Algorithm
• Why Floyd Warshall?
• Floyd Warshall with detrimental cycle,
• Floyd Warshall in Python,
• BFS vs Dijkstra vs Bellman Ford vs Floyd Warshall,
• Minimal 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

Part 30 – Grasping Algorithms

• What’s Grasping Algorithm?
• Well-known Grasping Algorithms
• Exercise Choice Downside
• Exercise Choice Downside in Python
• Coin Change Downside
• Coin Change Downside in Python
• Fractional Knapsack Downside
• Fractional Knapsack Downside in Python

Part 31 – Divide and Conquer Algorithms

• What’s a Divide and Conquer Algorithm?
• Widespread Divide and Conquer algorithms
• How one can clear up Fibonacci collection utilizing Divide and Conquer strategy?
• Quantity Issue
• Quantity Consider Python
• Home Robber
• Home Robber Downside in Python
• Convert one string to a different
• Convert One String to a different in Python
• Zero One Knapsack drawback
• Zero One Knapsack drawback in Python
• Longest Widespread Sequence Downside
• Longest Widespread Subsequence in Python
• Longest Palindromic Subsequence Downside
• Longest Palindromic Subsequence in Python
• Minimal price to succeed in the Final cell drawback
• Minimal Price to succeed in the Final Cell in 2D array utilizing Python
• Variety of Methods to succeed in the Final Cell with given Price
• Variety of Methods to succeed in the Final Cell with given Price in Python

Part 32 – Dynamic Programming

• What’s Dynamic Programming? (Overlapping property)
• The place does the identify of DC come from?
• Prime Down with Memoization
• Backside Up with Tabulation
• Prime Down vs Backside Up
• Is Merge Kind Dynamic Programming?
• Quantity Issue Downside utilizing Dynamic Programming
• Quantity Issue : Prime Down and Backside Up
• Home Robber Downside utilizing Dynamic Programming
• Home Robber : Prime Down and Backside Up
• Convert one string to a different utilizing Dynamic Programming
• Convert String utilizing Backside Up
• Zero One Knapsack utilizing Dynamic Programming
• Zero One Knapsack – Prime Down
• Zero One Knapsack – Backside Up

Part 33 – CHALLENGING Dynamic Programming Issues

• Longest repeated Subsequence Size drawback
• Longest Widespread Subsequence Size drawback
• Longest Widespread Subsequence  drawback
• Diff Utility
• Shortest Widespread Subsequence  drawback
• Size of Longest Palindromic Subsequence
• Subset Sum Downside
• Egg Dropping Puzzle
• Most Size Chain of Pairs

Part 34 – A Recipe for Downside Fixing

• Introduction
• Step 1 – Perceive the issue
• Step 2 – Examples
• Step 3 – Break it Down
• Step 4 – Remedy or Simplify
• Step 5 – Look Again and Refactor