CF

Master Data Structure & Algorithms & Crack the Coding Interview

13h 48m 49s
English
Paid
Updated September 2026

Master Data Structure & Algorithms & Crack the Coding Interview is a 123-lesson 13 hours 48 minutes self-paced course by Internet Made Coder. Built by a developer who went from fearing algorithm questions to handling them with confidence, this Python course condenses that journey into a structured program for tackling technical interviews at any company.

Course facts

Lessons
123
Duration
13 hours 48 minutes
Level
All levels
Language
English
Updated
2026-09-11
Instructor
Internet Made Coder
Price
Premium

Built by a developer who went from fearing algorithm questions to handling them with confidence, this Python course condenses that journey into a structured program for tackling technical interviews at any company.

How it approaches the material

  • Key patterns that recur across most interview-style problems
  • A systematic, repeatable methodology rather than isolated tips
  • Practical exercises tied to real interview preparation

The material draws on hundreds of existing resources, books, and courses, consolidated into one program meant to work through data structures and algorithms without gaps.

Who it's for

Beginner programmers without a clear study plan, and anyone who wants a structured, confidence-building route into technical interviews rather than scattered practice.

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.

Who teaches Master Data Structure & Algorithms & Crack the Coding Interview? Internet Made Coder

Internet Made Coder thumbnail

Tuomas Kivioja publishes the Internet Made Coder YouTube channel, focused on practical career advice for self-taught developers and on the algorithm / interview-prep material needed to break into well-paying engineering roles. The channel name reflects his own background as a developer who learned online rather than through a formal CS degree.

His CourseFlix listing carries Master Data Structure & Algorithms & Crack the Coding Interview — a paid Python-based DSA course aimed at developers preparing for technical interviews at large tech companies.

What lessons are included in Master Data Structure & Algorithms & Crack the Coding Interview?

This is a demo lesson (10:00 remaining)

You can watch up to 10 minutes for free. Subscribe to unlock all 123 lessons in this course and access 10,000+ hours of premium content across all courses.

View Pricing
0:00
/
#1: Why every developer needs to learn Data Structures & Algorithms
All Course Lessons (123)
#Lesson TitleDurationAccess
1
Why every developer needs to learn Data Structures & Algorithms Demo
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
Unlock unlimited learning

Get instant access to all 122 lessons in this course, plus thousands of other premium courses. One subscription, unlimited knowledge.

Learn more about subscription

What courses are similar to Master Data Structure & Algorithms & Crack the Coding Interview?

Frequently asked questions

What prerequisites should I have before taking this course?
Before enrolling, it's beneficial to have a basic understanding of Python programming. The course begins with foundational topics such as Python variables, basic datatypes, and functions, which will help you build a strong base. However, prior experience with Python will allow you to progress through the course more smoothly and focus more on mastering data structures and algorithms.
What projects or exercises are included in the course?
The course includes practical exercises like reversing a linked list and checking if a linked list is a palindrome. There are also walkthroughs for list exercises and implementing data structures such as stacks and queues in Python. These projects are designed to help you apply the theoretical knowledge of data structures and algorithms in a hands-on manner.
Who is the target audience for this course?
This course is aimed at developers who want to master data structures and algorithms, particularly those preparing for technical interviews at large IT companies. The course covers essential topics that are often part of coding interviews, making it suitable for those looking to enhance their problem-solving skills in Python.
How does the depth of this course compare to others on the same topic?
The course offers a comprehensive exploration of data structures and algorithms, covering both basic and advanced topics such as Big O notation, space complexity, and various time complexities. Unlike some courses that may only skim the surface, this course delves into detailed implementations and efficiency analysis, helping students truly master the subject.
What specific tools or platforms does the course teach?
The course focuses on Python as the primary programming language for implementing data structures and algorithms. It includes lessons on Python-specific features like methods for datatypes, object-oriented programming, and using libraries, which are crucial for writing efficient code.
What topics are not covered in this course?
While the course extensively covers data structures and algorithms, it does not focus on other programming languages or frameworks beyond Python. Additionally, it does not delve into non-Python specific topics or the use of algorithms in data science or web development contexts.
What is the expected time commitment for completing the course?
The course consists of 123 lessons, but the total runtime is not specified. Students should be prepared to invest significant time in understanding both the theoretical concepts and practical implementations. The time commitment will vary based on prior experience and familiarity with Python programming and algorithms.