JavaScript Algorithms - The Fundamentals

Learn all the core basics and fundamentals about JavaScript algorithms, dive into tons of examples and get a plan for building and measuring algorithms.

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Algorithms are a complex, yet vital part of programming and of being a developer!

Being able to come up with algorithms (= being able to solve problems) is extremely important, not only for interviews but also in general to grow as a developer.

But diving into algorithms and finding the best possible solution for a given problem can be a daunting task.

And even once you have found a solution, it might not be the best one.

This course helps you with all of that!

You will learn what algorithms are, which kinds of algorithms you may find out there and most importantly, you will learn how to derive algorithms and how to judge as well as compare them!

We will dive into this complex topic step by step and by the end of the course, you will have a very strong foundation and all the core fundamental concepts you need to build and optimize your own algorithms for any problem out there!

This course introduces you to algorithms all related important concepts step by step.

In detail, you will learn:

  • What algorithms are
  • Why algorithms are important
  • How to judge and compare algorithms
  • All about "Time Complexity", "Space Complexity" and the "Big O" notation
  • Different time complexities and how to derive them
  • How to use recursion with algorithms
  • Optimization approaches like "Dynamic Programming"
  • All core concepts you need for math, search, sorting and sets/ array algorithms
  • Different kinds of solving problems (e.g. greedy approaches, dynamic approaches)

By the end of the course, you will have a very solid foundation and a plan on how to tackle any problem and find a fitting algorithm! You will be well-prepared to dive deeper and explore more problems and algorithms.

The different concepts are taught step by step with small, focused code examples and all building blocks being visualized on slides to make understanding them as easy as possible!

This course is for you if you want to take the next step as a developer, if you want to ace interviews or if you simply want to grow as a developer.

It assumes no prior algorithm knowledge but you of course need solid programming fundamentals - preferably in JavaScript, the programming language used in this course.

Section: Getting Started

1. Welcome to the Course! (0:58) Preview
2. What & Why (6:42) Preview
3. Join the Learning Community (1:00)
4. Measuring Algorithm Performance & Time Complexity (9:31) Preview
5. Big O Notation (6:57) Preview
6. Deriving the Time Complexity / Big O of an Algorithm (11:27) Preview
7. Constant Time (4:50) Preview
8. Using Big O For Algorithm Comparison (3:42) Preview
9. A First Task! (2:13) Preview
10. Solution & A Gotcha (10:01) Preview
11. About this Course & Outline (5:30) Preview
12. Module Resources (1:00) Preview

Section: Math Algorithms & Practicing The Basics

13. Module Introduction (1:38)
14. The Fibonacci Problem (3:30)
15. The Fibonacci Solution (5:42)
16. Fibonacci Big O (Time Complexity) (4:17)
17. The "Is Prime" Problem (2:33)
18. The "Is Prime" Solution (3:56)
19. "Is Prime", Big O & Multiple Cases (12:48)
20. "Is Prime" - Improved Version (6:44)
21. Time to Practice (Problem) (1:27)
22. Time to Practice (Solution) (12:50)
23. Identifying Big O Quickly (5:53)
24. Is Power Of Two Algorithm (8:06)
25. Logarithmic Time Complexity (7:29)
26. Bitwise Operators & Power Of Two (8:19)
27. Again: Identifying Big O Quickly (2:50)
28. The Factorial Algorithm (5:12)
29. Module Resources (1:00)

Section: Recursion & Dynamic Programming

30. Module Introduction (1:06)
31. Recursion in Algorithms (8:30)
32. Exponential Time Complexity (11:30)
33. Quadratic Time Complexity vs Exponential Time Complexity (5:08)
34. What is "Dynamic Programming"? (3:25)
35. "Dynamic Programming" in Action (9:22)
36. The "Bottom Up" Approach (4:37)
37. Module Resources (1:00)

Section: Search Algorithms

38. Module Introduction (2:04)
39. What Search Algorithms Are About (1:19)
40. Introducing Linear Search (1:41)
41. Linear Search in Action (10:39)
42. Linear Search Time Complexity (4:21)
43. Binary Search in Theory (2:50)
44. Binary Search in Action (11:46)
45. Binary Search Time Complexity (3:20)
46. Recursive Binary Search (13:05)
47. The Master Theorem (10:39)
48. Module Resources (1:00)

Section: Sorting Algorithms

49. Module Introduction (0:52)
50. What are Sorting Algorithms About? (1:11)
51. Bubble Sort - Theory (5:07)
52. Bubble Sort in Action (17:31)
53. Bubble Sort Time Complexity (3:41)
54. Quicksort - Theory (2:55)
55. Quicksort in Action (13:14)
56. Quicksort Time Complexity (8:04)
57. Merge Sort - Theory (4:09)
58. Merge Sort in Action (16:38)
59. Merge Sort Time Complexity (4:01)
60. Summary (2:04)
61. Module Resources (1:00)

Section: Space Complexity

62. Module Introduction (1:13)
63. Introducing Space Complexity (3:02)
64. Deriving Space Complexity (2:39)
65. Factorial Algorithm (Loop) - Space Complexity (5:06)
66. Factorial Algorithm (Recursive) - Space Complexity (3:58)
67. Linear Search - Space Complexity (3:23)
68. Binary Search - Space Complexity (2:24)
69. Bubble Sort - Space Complexity (2:04)
70. Quicksort - Space Complexity (6:00)
71. Merge Sort - Space Complexity (2:13)
72. Module Resources (1:00)

Section: Sets (Array) Algorithms

73. Module Introduction (1:50)
74. An Introduction to Sets (3:17)
75. The Cartesian Product Algorithm (5:11)
76. Cartesian Product - Time Complexity (5:10)
77. The Cartesian Product Algorithm - Without Limits (16:37)
78. The Permutations Algorithms (3:27)
79. Permutations without Repetitions (19:00)
80. Understanding Permutations (4:44)
81. Permutations without Repetitions - Big O (3:53)
82. Permutations WITH Repetitions (15:15)
83. Module Resources (1:00)

Section: More Complex Algorithms & How To Solve Code Problems

84. Module Introduction (1:07)
85. Solving Problems In A Structured Way (7:06)
86. Simplifying Problems (4:00)
87. Practice Makes Perfect! (1:35)
88. The Knapsack Problem (0/1) (1:47)
89. A Wrong Approach (5:21)
90. Approaching the Problem Differently (5:26)
91. A Working Knapsack Solution (18:21)
92. An Improved Knapsack Algorithm (14:47)
93. Greedy vs Dynamic Algorithms (3:48)
94. A Greedy Knapsack Algorithm (9:46)
95. A New Problem Solving Plan (2:50)
96. The Change Making Problem (3:31)
97. A Greedy Change Making Algorithm (12:40)
98. A Variation to the Change Making Problem (13:17)
99. Module Resources (1:00)

Section: Roundup & Next Steps

100. Course Roundup (6:23)

Course Instructor

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Maximilian Schwarzmüller

As a self-taught professional I really know the hard parts and the difficult topics when learning new or improving on already-known languages. This background and experience enable me to focus on the most relevant key concepts and topics. My track record of many 5-star rated courses, more than 1,000,000 students worldwide as well as a successful YouTube channel is the best proof for that.

The most rewarding experience for me is to see how people find new, better jobs, build awesome web applications, work on amazing projects or simply enjoy their hobby with the help of my content. That's why, together with Manuel Lorenz, I founded Academind to offer the best possible learning experience and to share the pleasure of learning with our students.