Mastering DSA with JavaScript

Name: Mastering DSA with JavaScript
Availability: InStock
Author: FreeAcademy

92Lessons

IntermediateLevel

FreeCertificate

Dive deep into essential data structures and algorithms, implementing them in JavaScript. Learn arrays, strings, linked lists, stacks, queues, hash tables, trees, graphs, and key algorithmic techniques including recursion, sorting, searching, and dynamic programming.

What you'll learn

Analyze algorithm efficiency using Big O, time, and space complexity
Implement core data structures including linked lists, trees, and graphs in JavaScript
Apply sorting and searching algorithms from bubble sort to Dijkstra's algorithm
Solve dynamic programming problems using memoization and tabulation techniques
Approach technical interview problems with a structured clarify-optimize-test strategy

About This Course

This free course on mastering DSA with JavaScript takes you from foundational concepts through advanced algorithmic techniques, all implemented in the language you already know. You will start by building a solid mental model of how data structures work, covering Big O notation, time complexity, and space complexity before moving into arrays, strings, linked lists, stacks, queues, hash tables, trees, tries, and graphs. Each topic pairs clear theory with hands-on JavaScript coding challenges, so you are not just reading about a palindrome checker or a cycle-detection algorithm, you are writing it.

The second half of the course shifts to algorithmic paradigms: recursion, binary search, merge sort, quick sort, divide and conquer, greedy strategies, dynamic programming with memoization and tabulation, and backtracking. You will tackle classic problems including the Knapsack problem, N-Queens, and Longest Common Subsequence. The final module prepares you for technical interviews by covering problem-solving patterns such as sliding window and two pointers, bit manipulation, and mock interview communication strategies.

This intermediate course suits developers who know JavaScript basics and want to strengthen their computer science fundamentals, students preparing for coding interviews, or self-learners who want to think more clearly about performance and problem-solving. The course is completely free and no signup is required to start. Complete all modules and pass the final exam to earn a certificate of completion you can add to your LinkedIn profile or resume.

Course Content

8 modules • 92 lessons

Module 1: Introduction to DSA and Core Concepts

9 lessons

1.1

Solutions and Code Examples

Module 2: Foundational Data Structures

22 lessons

2.1

Array Definition & JavaScript Arrays

2.2

Basic Array Operations (Insert, Delete, Access)

2.3

Challenge: Manual Array Insertion

2.4

Challenge: Manual Array Deletion

2.5

Multi-dimensional Arrays

2.6

Challenge: Rotate N×N Matrix by 90°

2.7

String Basics & Immutability

2.8

Common String Methods & Their Complexity

2.9

Challenge: String Reversal

2.10

Challenge: Palindrome Checker

2.11

Challenge: Anagram Detector

2.12

Node and Linked List Structure

2.13

Types of Linked Lists (Singly, Doubly, Circular)

2.14

Linked List Operations (Insertion, Deletion, Traversal)

2.15

Challenge: Detect a Cycle in a Linked List

2.16

Challenge: Merge Two Sorted Linked Lists

2.17

Challenge: Reverse a Linked List

2.18

Set Definition & Characteristics

2.19

JavaScript `Set` Object & Basic Operations

2.20

Set Operations (Union, Intersection, Difference)

2.21

Set Use Cases & Time Complexity

2.22

Challenge: Find Unique Elements

Module 3: Abstract Data Types (ADTs)

13 lessons

3.1

Stack Theory: LIFO Principle

3.2

Stack Implementations (Array-based, Linked List-based)

3.3

Stack Applications (Call Stack, Undo/Redo)

3.4

Challenge: Balanced Parentheses

3.5

Queue Theory: FIFO Principle

3.6

Queue Implementations (Array-based, Linked List-based)

3.7

Queue Applications (Task Scheduling, BFS)

3.8

Challenge: Implement a Queue

3.9

Hashing Concepts and Principles

3.10

Collision Resolution Techniques (Chaining, Open Addressing)

3.11

Hash Table Operations (Insert, Delete, Search)

3.12

JavaScript Map and Object as Hash Tables

3.13

Challenge: Two Sum

Module 4: Trees

15 lessons

4.1

Tree Terminology (Root, Node, Leaf, Depth, Height)

4.2

Types of Trees (General Tree, Binary Tree)

4.3

Binary Tree Properties and Characteristics

4.4

Binary Tree Traversals (Inorder, Preorder, Postorder, BFS)

4.5

Introduction to BSTs

4.6

BST Properties and Rules

4.7

BST Operations (Insert, Delete, Search)

4.8

Heap Properties and Types (Min-Heap, Max-Heap)

4.9

Heap Operations (Insert, Extract-Min/Max, Heapify)

4.10

Heap Applications (Priority Queues, Heap Sort)

4.11

The Need for Balanced Trees (AVL, Red-Black - conceptual)

4.12

Challenge: Check if a Binary Tree is Balanced

4.13

Challenge: Implement AVL Rotations

4.14

Trie Theory and Structure

4.15

Trie Operations and Applications (Autocomplete, Spell Checker)

Module 5: Graph Theory

10 lessons

5.1

Graph Terminology (Vertex, Edge, Directed, Weighted)

5.2

Graph Representations (Adjacency Matrix, Adjacency List)

5.3

Breadth-First Search (BFS)

5.4

Depth-First Search (DFS)

5.5

Dijkstra's Algorithm

5.6

Bellman-Ford & Floyd-Warshall (Introduction)

5.7

Prim's Algorithm

5.8

Kruskal's Algorithm

5.9

Topological Sort Algorithm and Applications

5.10

Union-Find Operations and Data Structure

Module 6: Algorithmic Paradigms & Techniques

14 lessons

6.1

Recursion Basics: Base Case & Recursive Step

6.2

Recursion Applications and Tracebacks

6.3

Linear Search

6.4

Binary Search

6.5

Basic Sorts (Bubble, Selection, Insertion)

6.6

Efficient Sorts (Merge Sort, Quick Sort)

6.7

Non-Comparison Sorts (Radix, Counting - conceptual)

6.8

Divide and Conquer Theory & Examples

6.9

Greedy Theory & Examples

6.10

DP Theory: Overlapping Subproblems & Optimal Substructure

6.11

Memoization vs. Tabulation

6.12

Classic DP Problems (Fibonacci, Knapsack, Longest Common Subsequence)

6.13

Backtracking Theory

6.14

Classic Backtracking Problems (N-Queens, Sudoku Solver, Permutations)

Module 7: Advanced Topics & Interview Preparation

7 lessons

7.1

Bitwise Operators in JavaScript

7.2

Bit Manipulation Applications (Flags, Optimization)

7.3

What and When to Use Advanced DS (Fenwick Tree, Segment Tree - conceptual)

7.4

Common Problem Solving Patterns (Sliding Window, Two Pointers, etc.)

7.5

Real-World DSA Examples in Frontend/Backend

7.6

Approaching Problems (Clarify, Examples, Brute Force, Optimize, Test)

7.7

Communication and Mock Interviews

Course Conclusion

2 lessons

8.1

Next Steps in Your DSA Journey

8.2

Community and Continued Engagement

Reviews

Frequently Asked Questions

What does this course cover?▼

The course covers the full DSA spectrum in JavaScript: foundational structures like arrays, strings, linked lists, sets, stacks, queues, and hash tables, then trees including BSTs, heaps, AVL trees, and tries, then graph algorithms including BFS, DFS, Dijkstra, Prim, Kruskal, and topological sort. It also covers algorithmic paradigms such as recursion, sorting, dynamic programming, and backtracking, plus an interview preparation module.

Is this course really free?▼

Yes, the entire course is free and no account is required to access the lessons. If you complete all modules and pass the final exam, you earn a certificate of completion at no cost.

What JavaScript knowledge do I need before starting?▼

You should be comfortable writing basic JavaScript: variables, functions, loops, arrays, and objects. The course does not teach JavaScript syntax from scratch, so some prior coding experience will help you focus on the data structures and algorithms themselves.

Which topics and tools are used in the course?▼

All code examples and challenges are written in plain JavaScript with no external libraries. Topics range from Big O notation and complexity analysis through hash tables, binary search trees, graph traversal algorithms, dynamic programming patterns, and bit manipulation, with dedicated coding challenges throughout.

Will I get a certificate when I finish?▼

Yes. Completing the course and passing the final exam earns you a certificate of completion that you can share on LinkedIn or include on your resume to demonstrate your data structures and algorithms knowledge.