Data Structures & Algorithms Python

Data Structures & Algorithms Python

What you’ll learn

Mastery of Data Structures and Algorithms
Confidently Answer Technical Interview Questions
Time and Space Complexity of Data Structures and Algorithms
Strengthen Your Skills as a Developer

Requirements

Basic programming
No experience with data structures or algorithms required

Description

This course is different…After each line of code, an animation of the data structure or algorithm is updated to show exactly what that line of code did.The advanced level animations provide huge advantages to students:Increased understanding of the conceptsGreater rate of retentionThe material can be covered in a fraction of the timeThat means that you can actually learn more material in less time and have higher retention of the material.That is the key combination of factors to prepare you for the technical interview that lands you your dream job!I invite you to watch a few of the videos in this course to see what I mean. The difference will be noticeable right away! I spent over a year to create this course with the goal that an absolute beginner can take it and understand all of the concepts the first time through.What you will get in this course…Over 100 hand crafted HD videos that use animations to illustrate technical concepts.Here is what you will learn in this course:TechnicalBig O notationData StructuresListsLinked ListsDoubly Linked ListsStacks & QueuesBinary TreesHash TablesGraphsAlgorithmsSorting Bubble SortSelection SortInsertion SortMerge SortQuick SortSearchingBreadth First SearchDepth First SearchI am excited to help you move forward with your coding and career goals.  Let’s get started!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Code Editor

Section 2: Big O

Lecture 3 Overview (Please Watch)

Lecture 4 Big O: Intro

Lecture 5 Big O: Worst Case

Lecture 6 Big O: O(n)

Lecture 7 Big O: Drop Constants

Lecture 8 Big O: O(n^2)

Lecture 9 Big O: Drop Non-Dominants

Lecture 10 Big O: O(1)

Lecture 11 Big O: O(log n)

Lecture 12 Big O: Different Terms for Inputs

Lecture 13 Big O: Lists

Lecture 14 Big O: Wrap Up

Section 3: Classes & Pointers

Lecture 15 Classes

Lecture 16 Pointers

Section 4: Data Structures: Linked Lists

Lecture 17 Linked List: Intro

Lecture 18 LL: Big O

Lecture 19 LL: Under the Hood

Lecture 20 LL: Constructor

Lecture 21 LL: Print List

Lecture 22 LL: Append

Lecture 23 LL: Reminder

Lecture 24 LL: Pop Intro

Lecture 25 LL: Pop Code

Lecture 26 LL: Prepend

Lecture 27 LL: Pop First

Lecture 28 LL: Get

Lecture 29 LL: Set

Lecture 30 LL: Insert

Lecture 31 LL: Remove

Lecture 32 LL: Reverse

Section 5: Data Structures: Doubly Linked Lists

Lecture 33 DLL: Constructor

Lecture 34 DLL: Append

Lecture 35 DLL: Pop

Lecture 36 DLL: Prepend

Lecture 37 DLL: Pop First

Lecture 38 DLL: Get

Lecture 39 DLL: Set

Lecture 40 DLL: Insert

Lecture 41 DLL: Remove

Section 6: Data Structures: Stacks & Queues

Lecture 42 Stack: Intro

Lecture 43 Stack: Constructor

Lecture 44 Stack: Push

Lecture 45 Stack: Pop

Lecture 46 Queue: Intro

Lecture 47 Queue: Constructor

Lecture 48 Queue: Enqueue

Lecture 49 Queue: Dequeue

Section 7: Data Structures: Trees

Lecture 50 Trees: Intro & Terminology

Lecture 51 Binary Search Trees: Example

Lecture 52 BST: Big O

Lecture 53 BST: Constructor

Lecture 54 BST: Insert – Intro

Lecture 55 BST: Insert – Code

Lecture 56 BST: Contains

Lecture 57 BST: Minimum Value

Section 8: Data Structures: Hash Tables

Lecture 58 Hash Table: Intro

Lecture 59 HT: Collisions

Lecture 60 HT: Constructor

Lecture 61 HT: Set

Lecture 62 HT: Get

Lecture 63 HT: Keys

Lecture 64 HT: Big O

Lecture 65 HT: Interview Question

Section 9: Data Structures: Graphs

Lecture 66 Graph: Intro

Lecture 67 Graph: Adjacency Matrix

Lecture 68 Graph: Adjacency List

Lecture 69 Graph: Big O

Lecture 70 Graph: Add Vertex

Lecture 71 Graph: Add Edge

Lecture 72 Graph: Remove Edge

Lecture 73 Graph: Remove Vertex

Section 10: Algorithms: Recursion

Lecture 74 Recursion: Intro

Lecture 75 Call Stack

Lecture 76 Factorial

Section 11: Algorithms: Basic Sorts

Lecture 77 Bubble Sort: Intro

Lecture 78 Bubble Sort: Code

Lecture 79 Selection Sort: Intro

Lecture 80 Selection Sort: Code

Lecture 81 Insertion Sort: Intro

Lecture 82 Insertion Sort: Code

Lecture 83 Insertion Sort: Big O

Section 12: Algorithms: Merge Sort

Lecture 84 Merge Sort: Overview

Lecture 85 Merge: Intro

Lecture 86 Merge: Code

Lecture 87 Merge Sort: Intro

Lecture 88 Merge Sort: Code

Lecture 89 Merge Sort: Big O

Section 13: Algorithms: Quick Sort

Lecture 90 Quick Sort: Intro

Lecture 91 Pivot: Intro

Lecture 92 Pivot: Code

Lecture 93 Quick Sort: Code

Lecture 94 Quick Sort: Big O

Section 14: Algorithms: Tree Traversal

Lecture 95 Tree Traversal: Intro

Lecture 96 BFS (Breadth First Search): Intro

Lecture 97 BFS: Code

Lecture 98 DFS (Depth First Search): PreOrder – Intro

Lecture 99 DFS: PreOrder – Code

Lecture 100 DFS: PostOrder – Intro

Lecture 101 DFS: PostOrder – Code

Lecture 102 DFS: InOrder – Intro

Lecture 103 DFS: InOrder – Code

Section 15: Coding Exercises

Section 16: BONUS SECTION: THANK YOU!

Lecture 104 BONUS LECTURE

Python programmers preparing for an interview,University students taking a data structures and algorithms course

Course Information:

Udemy | English | 7h 30m | 1.33 GB
Created by: Scott Barrett

You Can See More Courses in the Developer >> Greetings from CourseDown.com