Master Data Structure & Algorithms & Crack the Coding Interview

13h 48m 49s
English
Paid
November 22, 2024

Take a unique Python course that will help you master data structures and algorithms, allowing you to confidently handle any tasks in a technical interview. You will gain knowledge that will make you a professional in algorithms and prepare you for interviews at the largest IT companies.

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What to expect:

  • Learning key patterns for solving any interview tasks.
  • Systematic knowledge to become a true master of technical interviews.
  • Practical advice and a ready-to-use training program based on a proven methodology.

This course is created by a developer who went from fearing tasks to being confident in their abilities. "Algo University" combines hundreds of resources, books, articles, and courses into one complete program that will eliminate your uncertainty.

You will master:

  • The most important topics of data structures and algorithms.
  • Methodologies for applying knowledge to solve real problems.
  • Practical exercises for interview preparation.

Result:

Upon completing the course, you will be able to confidently solve any interview tasks, increase your chances of employment at leading companies, and clear the path to a high-paying developer career.

This course is for you if:

  • You are a beginner programmer struggling to progress due to a lack of a clear learning plan.
  • You feel you lack the skills to pass interviews but are serious about learning everything necessary.
  • You cannot or do not want to spend $40,000+ on a computer science degree but are ready to move forward in your career.
  • You do not want to study unnecessary theory that is not useful for employment and only wastes your time.
  • You want to learn only truly important topics in a simple and understandable way.

This course is not for you if:

  • You are only looking for theoretical information and are not ready for practical tasks.
  • You are not ready to invest in yourself and prefer the cheapest ways of learning.
  • You are not ready to work hard - to achieve results, you will need to watch all the lessons and solve 50-100 practical tasks.
  • You are not interested in becoming an excellent programmer or working in large tech companies. If this is the case, this course may not be suitable for you.

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# Title Duration
1 Why every developer needs to learn Data Structures & Algorithms 06:27
2 Introduction to Data Structures 06:14
3 Introduction to Algorithms 07:54
4 How to MASTER Data Structures & Algorithms 07:29
5 How to Solve Coding Problems - My 6-Step Framework 10:41
6 Applying the framework: TwoSum 09:46
7 What this Bonus Module is about 01:40
8 1 - Writing Our First Python Program 13:45
9 2 - Python variables (Building Block 1) 11:49
10 3 - Errors (when things go wrong...) 13:21
11 4 - Basic Python datatypes - (Building Block 2) 12:28
12 5 - Basic Python datatypes 2 17:57
13 6 - Making our datatypes more POWERFUL - Methods 18:04
14 7 - Python Functions (Building Block 3) 23:23
15 8 - The flow of a program (Building Block 4) 17:01
16 9 - Compound data types 17:19
17 11 - Error handling - how to prevent crashes... 07:45
18 12 - Libraries - standing on the shoulders of giants 16:35
19 Modeling the real world using code - Object-Oriented Programming Introduction 04:01
20 OOP-1 - Classes & Objects - Let's make some cookies! 10:45
21 OOP-2: Objects & Classes in Python - I have been lying to you.. 07:13
22 OOP-3: Creating our Own Classes 05:50
23 OOP-4: Creating our Own Classes 2 06:27
24 OOP-5: Private attributes & Properties: Creating Secrets (Advanced) 10:43
25 Module Overview - How do developers analyze algorithms? 02:07
26 Introduction to Efficiency 03:42
27 Big O notation - What makes an algorithm "fast"? 12:23
28 Good vs bad runtime - O(n) & O(n^2) 08:02
29 Best runtime - O(1) 04:46
30 Logarithmic & linearithmic time complexity:O(logn) & O(nlogn) 08:03
31 (Advanced) Terrible Time Complexities! - O(2^n), O(n!) and beyond 07:38
32 Multiple inputs 04:24
33 Space complexity 05:08
34 Data Structures Introduction 06:44
35 The Computer's Memory 07:13
36 Lists/Arrays 1 04:15
37 Lists/Arrays 2 - Big O 06:29
38 (advanced) Dynamic Lists & list memory allocation 05:56
39 List Exercise 1 walkthrough 04:44
40 List exercise 2 walkthrough 05:26
41 Linked Lists Introduction - What is a Linked List? 02:18
42 Linked Lists Implementation in Python 1 11:06
43 Linked List implementation in Python 2 06:00
44 Linked List Big O 04:48
45 List vs Linked List 03:11
46 Linked List Exercise 1: Reverse a Linked List 05:57
47 Linked List Exercise 2: Palindrome 05:47
48 Stacks & Queues Introduction 03:25
49 Stacks & Queues in Memory 03:42
50 Stack Implementation in Python 06:18
51 Queue Implementation in Python 02:03
52 Stacks Big O 01:37
53 Queue Big O 01:50
54 Stack exercise walkthrough 07:57
55 Queue exercise walkthrough 05:20
56 Trees Introduction 03:24
57 Binary Search Trees 06:25
58 Binary Search Tree Implementation 1 - Insertion 03:58
59 Binary Search Tree Implementation 2 - Searching 11:00
60 Binary Search Tree Implementation 3 - Deletion 01:39
61 Heaps 04:08
62 Heap Implementation 1 13:57
63 Heap Implementation 2 05:42
64 Graphs Introduction 04:38
65 Undirected Graph Implementation 05:41
66 Different Types of Graphs 03:35
67 Directed Graph Implementation 02:02
68 Weighted (Directed) Graph Implementation 01:31
69 Hash Maps Introduction 03:42
70 Hash Maps Behind the Scenes 08:15
71 Hash Maps Big O 02:56
72 Hash Map Implementation from First Principles 08:36
73 Hash Map Exercise Walkthrough 03:44
74 Algorithms introduction 03:56
75 List Algorithms 12:00
76 Recursion introduction 02:44
77 The Call Stack & Stack Overflow 06:42
78 How to use recursion (step-by-step) 07:26
79 Recursion exercise 1 walkthrough - Fibonacci 05:52
80 Recursion exercise 2 walkthrough - Palindrome 03:48
81 Recursive vs iterative programming 05:32
82 Sorting introduction 04:33
83 Insertion Sort 04:41
84 Insertion Sort Implementation 07:09
85 Bubble Sort 03:11
86 Bubble Sort Implementation 03:54
87 Merge Sort 08:10
88 Merge Sort Implementation 10:56
89 Quick Sort 05:12
90 Quick Sort Implementation 06:13
91 Which sorting algorithm should you use? 03:40
92 Graph Searching Introduction 02:06
93 Breadth-First Search (BFS) 08:18
94 BFS Implementation 07:20
95 Depth-First Search (DFS) 01:47
96 DFS Implementation 06:37
97 DFS vs BFS 03:57
98 Dijkstra's Algorithm 07:44
99 Dijkstra's Algorithm Implementation 05:50
100 Dynamic Programming Introduction 06:26
101 Dynamic programming exercise walkthrough: Fibonacci 03:31
102 When To Use Dynamic Programming? (My Framework) 04:47
103 Dynamic programming practical examples from my startup 04:04
104 Two Pointers 05:13
105 Two Pointers 2 05:52
106 Two Pointers 3 10:39
107 Sliding Window 03:03
108 Sliding Window 2 06:36
109 Fast and Slow Pointers (Tortoise and Hare) 04:32
110 Fast and Slow Pointers 2 04:00
111 Fast and Slow Pointers 3 03:04
112 Backtracking 05:31
113 Backtracking 2 08:54
114 Divide and Conquer 09:54
115 Divide and Conquer 2 07:23
116 Prefix sum 03:20
117 Prefix sum 2 06:43
118 Prefix sum 3 06:43
119 Note on this module 02:42
120 How to Apply for Jobs and Get More Interviews 07:28
121 How to Leverage LinkedIn to Get Interviews 07:02
122 The Top Non-Technical Skills to get Hired 12:49
123 How to Crack the Coding Interview 11:26

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