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Complete Data Structures And Algorithms: Software Interviews - Panter - 15.07.2022
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Complete Data Structures And Algorithms: Software Interviews
Published 7/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 6.01 GB | Duration: 16h 4m
Learn data structures and algorithms with Python. Solve technical questions by Google, Amazon, Meta, Netflix and more!
What you'll learn
Data Structures
Algorithms
Technical Interview Question Solutions
Python
Requirements
Knowledge in any programming language
Description
Welcome to the Complete Data Structure & Algorithms: Technical Interviews courseData structures and algorithms is not just a subject which every programmer should master but also a major topic in technical interviews by giant technology companies such as Google, Amazon, Microsoft, Netflix, Uber, Tesla etc.Not only we will learn about the theory and practical implementations of the data structures & algorithms but also we will solve many technical interview questions and practice what we learn in each section.During the course we will use Python programming language for all implementations and question solutions. However if you are sufficient in any other programming language before, you would be fine. We have a quick Python Refresher section where you can learn about the fundamentals if you want to adapt. Alternatively you can learn all the algorithms and solutions and implement them in your own preferred language as well.This course is brought to you by Atil Samancioglu, teaching more than 300.000 students worldwide on programming and cyber security along with the Codestars, serving more than 1 million students! Atil also teaches mobile application development in Bogazici University and he is founder of his own training startup Academy Club. Some of the topics that will be covered during the course:Technical Interview QuestionsBig O NotationStackQueueDequeArraysLinked ListHeapGraphTreeHashTableAfter you complete the course you will be able to solve technical interview questions, improve your programming skills and implement ideas in real life problems. You will be given many opportunities to solve questions on your own during the training and it will be vital for you to follow these instructions.If you are ready, let's get started!
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Course Outline
Section 2: Big O Notation
Lecture 3 Big O Introduction
Lecture 4 What is Big O?
Lecture 5 Big O Code Examples
Lecture 6 Space Complexity
Lecture 7 Big O GitHub Link
Section 3: Lists & Arrays
Lecture 8 Lists Introduction
Lecture 9 Arrays 101
Lecture 10 Lists
Lecture 11 Arrays & Lists GitHub Link
Lecture 12 Contains Duplicate
Lecture 13 Contains Duplicate Solution
Lecture 14 Contains Duplicate GitHub Link
Lecture 15 Find Single
Lecture 16 Single Number Solution
Lecture 17 Find Single GitHub Link
Lecture 18 Majority Element
Lecture 19 Boyer Moore
Lecture 20 Majority Element GitHub Link
Section 4: Stack, Queue & Deque
Lecture 21 Stack, Queue, Deque Introduction
Lecture 22 What is Stack, Queue, Deque?
Lecture 23 LifoQueue
Lecture 24 Stack Implementation
Lecture 25 Queue Implementation
Lecture 26 Deque Implementation
Lecture 27 Stack, Queue, Deque GitHub Link
Lecture 28 Implement Stack Using Queue
Lecture 29 Writing the Stack
Lecture 30 Implement Stack GitHub Link
Lecture 31 Baseball Game
Lecture 32 Baseball Solution
Lecture 33 Baseball GitHub Link
Lecture 34 Daily Temperatures
Lecture 35 Daily Temperatures Solution
Lecture 36 Daily Temperatures GitHub Link
Section 5: Linked List
Lecture 37 Linked List Introduction
Lecture 38 What is Linked List?
Lecture 39 Doubly Linked List
Lecture 40 Linked List O Notation
Lecture 41 Linked List GitHub Link
Lecture 42 Remove nth Node
Lecture 43 Remove nth Node Solution
Lecture 44 Remove nth Node GitHub Link
Lecture 45 Linked List Intersection
Lecture 46 Intersection Solution
Lecture 47 Intersection GitHub Link
Lecture 48 Duplicate
Lecture 49 Floyd
Lecture 50 Duplicate GitHub Link
Section 6: Tree
Lecture 51 Tree Introduction
Lecture 52 What is Tree?
Lecture 53 Tree Big O Notation
Lecture 54 Insert Method
Lecture 55 Finishing BST
Lecture 56 Tree GitHub Link
Lecture 57 Recursion
Lecture 58 Recursion GitHub Link
Lecture 59 Reverse String
Lecture 60 Reverse String Recursion
Lecture 61 Reverse String GitHub Link
Lecture 62 Fibonacci
Lecture 63 Recursion vs Iteration
Lecture 64 Memoization
Lecture 65 Fibonacci GitHub Link
Lecture 66 Invert Binary Tree
Lecture 67 Invert Tree Solution
Lecture 68 Invert Binary GitHub Link
Section 7: Tree Traversal
Lecture 69 Tree Traversal Introduction
Lecture 70 BFS vs DFS
Lecture 71 BFS Implementation
Lecture 72 DFS Implementation
Lecture 73 DFS Other Methods
Lecture 74 Tree Traversal GitHub Link
Lecture 75 BST to Tree
Lecture 76 DFS Solution
Lecture 77 Greater BST GitHub Link
Lecture 78 Binary Tree Max Path Sum
Lecture 79 DFS Returning Solution
Lecture 80 Binary Tree Max GitHub Link
Section 8: Graph
Lecture 81 Graph Introduction
Lecture 82 What is Graph?
Lecture 83 Graph Implementation
Lecture 84 Graph GitHub Link
Lecture 85 Reorder Routes
Lecture 86 DFS Solution
Lecture 87 Reorder Routes GitHub Link
Lecture 88 Number of Islands
Lecture 89 BFS Solution
Lecture 90 Number of Islands GitHub Link
Lecture 91 Redundant Connection
Lecture 92 Union Find
Lecture 93 Redundant Connection GitHub Link
Section 9: Searching & Hash Tables
Lecture 94 Hash Tables Introduction
Lecture 95 Sequential vs Binary
Lecture 96 Search Implementation
Lecture 97 Search Algorithms GitHub Link
Lecture 98 What is Hash Table?
Lecture 99 Hash Function
Lecture 100 Hash Table Implementation
Lecture 101 HashTable GitHub Link
Lecture 102 Two Sum
Lecture 103 HashMap Solution
Lecture 104 Two Sum GitHub Link
Lecture 105 Encode Decode
Lecture 106 Tiny Url Solution
Lecture 107 Tiny Url GitHub Link
Lecture 108 Brick Wall
Lecture 109 Brick Wall Solution
Lecture 110 Brick Wall GitHub Link
Section 10: Sorting & Heap
Lecture 111 Heap Introduction
Lecture 112 Sorting Algorithms
Lecture 113 Bubble Sort
Lecture 114 Selection Sort
Lecture 115 Insertion Sort
Lecture 116 Merge Sort
Lecture 117 Merge Sort Implementation
Lecture 118 Quick Sort
Lecture 119 Quick Sort Implementation
Lecture 120 What is Heap?
Lecture 121 Heap Sort
Lecture 122 Sorting Algorithms GitHub Link
Lecture 123 K Closest Points
Lecture 124 Heap Solution
Lecture 125 K Closest GitHub Link
Lecture 126 Data Stream
Lecture 127 Max Heap Solution
Lecture 128 Data Stream GitHub Link
Section 11: Python Refresher
Lecture 129 Python Refresher Introduction
Lecture 130 Anaconda Installation (Windows)
Lecture 131 Anaconda Installation (MAC)
Lecture 132 Python Variables
Lecture 133 String Details
Lecture 134 Collections
Lecture 135 Dictionary
Lecture 136 Set and Tuple
Lecture 137 Conversions
Lecture 138 Error Handling
Lecture 139 Conditions and Loops
Lecture 140 Useful Functions
Lecture 141 Functions
Lecture 142 Classes
Lecture 143 Scope
Lecture 144 Python Refresher GitHub Link
Section 12: Closing
Lecture 145 Closing
Programmers trying to land a job in big technology companies,Programmers looking forward to improve their coding skills,Programmers looking to learn about data structures & algorithms
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