Published 12/2023
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 694.65 MB | Duration: 2h 13m
Master Data Structures in Python | Big O Notation (Space Complexity and Time Complexity) | Crack Coding Interviews
What you’ll learn
Understand time and space complexities and how to calculate them
Understand computer science and how do they work
Implement computer science data structures from scratch
Use built-in data structures in Python
Requirements
Basic Python knowledge
Description
Welcome to Data Structures in Python Course: Crack Coding Interviews course :)In this course we will dive deep into Data Structures and learn how to do they work, how to implement them in Python and how to use them for implementing and optimizing your application. We will also take a look at the built-in data structures provided by Python and learn how to use them. And we will learn how to calculate time complexity and space complexity of the code and how to decide which data structure should be used for solving a specific programming problem.Data structures is a very important aspect of computer science, learning and understanding data structures will help you become a better programmer, write more efficient code and solve problems quicker, that’s why Tech companies focus on data structures in the coding interviews.Throughout this course we will cover everything you need to master data structures , including:Big O notation (Time Complexity & Space Complexity)Linked listsStacksHeapsQueuesHash TablesTreesBinary Search TreesGraphs (Adjacency List & Adjacency Matrix)I am confident that you will like this course and that you will be a different programmer once you finish it, join me in this course and master data structures and algorithms! 🙂
Overview
Section 1: Big O Notation
Lecture 1 Introduction to Big O Notation
Lecture 2 Linear Complexity – O(n)
Lecture 3 Constant Complexity – O(1)
Lecture 4 Quadratic Complexity – O(n^2)
Lecture 5 Logarithmic Complexity – O(logn)
Lecture 6 Constants in Big O
Lecture 7 Dominant and Non-Dominant Factors in Big O
Lecture 8 Complexities Comparison
Section 2: Linked Lists
Lecture 9 Introduction to Linked Lists
Lecture 10 Linked List Implementation
Lecture 11 Linked Lists: Adding Elements
Lecture 12 Linked Lists: Append Implementation
Lecture 13 Linked Lists: Prepend Implementation
Lecture 14 Linked Lists: Iterating
Lecture 15 Linked Lists: Iterating Implementation
Lecture 16 Linked Lists: Removing Elements
Lecture 17 Linked Lists: Removing Elements Implementation
Lecture 18 Time Complexity of Linked Lists Operations
Lecture 19 When to Use Linked Lists
Section 3: Linked Lists: Python Built-In Lists
Lecture 20 Introduction to Python Built-In Lists
Lecture 21 Creating Lists
Lecture 22 Iterating Lists
Lecture 23 Append
Lecture 24 Extend
Lecture 25 Insert
Lecture 26 Remove
Lecture 27 Pop
Lecture 28 Clear
Lecture 29 Count
Lecture 30 Reverse
Section 4: Stacks
Lecture 31 Introduction to Stacks
Lecture 32 Stack Implementation: Stack and Node Classes
Lecture 33 Stack Implementation: Push
Lecture 34 Stack Implementation: Pop & isEmpty
Lecture 35 Python Built-In List as Stack
Section 5: Queues
Lecture 36 Introduction to Queues
Lecture 37 Queue Implementation: Queue and Node Classes
Lecture 38 Queue Implementation: isEmpty
Lecture 39 Queue Implementation: Enqueue
Lecture 40 Queue Imeplementation: Dequeue
Section 6: Trees
Lecture 41 Introduction to Trees
Lecture 42 Binary Trees
Lecture 43 Binary Search Trees
Lecture 44 Binary Search Trees: Insert Operation
Lecture 45 Binary Search Trees: Class Implementation
Lecture 46 Binary Search Trees: Insert Operation Implementation
Lecture 47 Binary Search Trees: Search Operation Implementation
Section 7: Heaps
Lecture 48 Introduction to Heaps
Lecture 49 Heaps: Insert
Lecture 50 Heaps: Pop
Lecture 51 Heap Implementation
Lecture 52 Heap Implementation: Insert & Heapify Up
Lecture 53 Heap Implementation: Pop
Lecture 54 Heap Implementation: Heapify Down
Section 8: Hash Tables
Lecture 55 Introduction to Hash Tables
Lecture 56 Using Dictionaries as Hash Tables in Python
Lecture 57 Hash Tables Time & Space Complexities
Section 9: Graphs
Lecture 58 Introduction to Graphs
Lecture 59 Graphs: Adjacency Matrix
Lecture 60 Graphs: Adjacency List
Lecture 61 Graph Implementation: Class & Constructor
Lecture 62 Graph Implementation: Add Node
Lecture 63 Graph Implementation: Add Edge
Lecture 64 Graph Implementation: Remove Edge
Lecture 65 Graph Implementation: Remove Node
Lecture 66 Graph Implementation: Display
Lecture 67 Graph Time & Space Complexities
Python developers who want to become better programmers by learning and understanding data structres and how to implement and use them
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