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BeginnerData Structures & Algorithms

~36 Hours 11 Lessons100% Free

Data Structures & Algorithms (DSA) Mastery

From Big-O complexity to Arrays, Trees, Graphs, Sorting, and Dynamic Programming.

Master foundational and advanced computer science algorithms: Asymptotic Big-O analysis, Arrays, Linked Lists, Stacks, Queues, Hash Tables, Binary Search Trees (BST), Heaps, Graphs (BFS/DFS, Dijkstra), Sorting algorithms, and Dynamic Programming.

What You Will Learn

Big-O Time & Space Complexity Analysis
Linear Structures (Arrays, Linked Lists, Stacks, Queues)
Non-Linear Structures (Binary Trees, BST, Heaps, Graphs)
Graph Traversal (BFS, DFS) & Shortest Path (Dijkstra)
Dynamic Programming (Memoization, Tabulation)

Prerequisites & Environment

Basic Programming Syntax

Complete Course Curriculum (11 Modules)

11 In-Depth Lessons

Module 01

Module 1: Asymptotic Analysis & Big-O Notation

1 Lessons

Time complexity, space complexity, Big-O classes, and asymptotic growth.

1.1Big-O Notation & Complexity Analysis

Module 02

Module 2: Dynamic Arrays & String Algorithms

1 Lessons

Dynamic array amortization, two pointers technique, and sliding window algorithms.

2.1Two Pointers Technique & Sliding Window

Module 03

Module 3: Singly & Doubly Linked Lists

1 Lessons

Node pointers, traversal, in-place list reversal, and Floyd's cycle detection algorithm.

3.1Linked Lists & Floyd's Cycle Detection (Tortoise & Hare)

Module 04

Module 4: Stacks & Queues

1 Lessons

LIFO Stacks, FIFO Queues, monotonic stacks, and circular queue buffers.

4.1Stacks (LIFO), Queues (FIFO) & Monotonic Stacks

Module 05

Module 5: Hash Tables & Hash Sets

1 Lessons

Hash functions, collision resolution (chaining vs open addressing), and load factor resizing.

5.1Hash Tables, Collision Resolution & O(1) Lookups

Module 06

Module 6: Recursion & Backtracking

1 Lessons

Base cases, recursive call stacks, tail call optimization, and backtracking algorithms (N-Queens).

6.1Recursion, Base Cases & Backtracking

Module 07

Module 7: Binary Trees & Binary Search Trees (BST)

1 Lessons

Tree traversals (Pre/In/Post/Level-order), BST search/insert/delete, and balanced AVL trees.

7.1Binary Search Trees (BST) & Tree Traversals

Module 08

Module 8: Heaps & Priority Queues

1 Lessons

Min-Heaps, Max-Heaps, binary heap array representation, Heapify, and Heap Sort.

8.1Binary Heaps & Priority Queues (O(log n))

Module 09

Module 9: Graph Representations, BFS & DFS

1 Lessons

Adjacency list vs adjacency matrix, Breadth-First Search (BFS), and Depth-First Search (DFS).

9.1Graph Traversal: BFS (Shortest Path) & DFS

Module 10

Module 10: Shortest Path & Minimum Spanning Trees (Dijkstra, Prim)

1 Lessons

Dijkstra's shortest path algorithm with priority queues, Bellman-Ford, and Union-Find (Disjoint Set).

10.1Dijkstra's Shortest Path & Disjoint Set (Union-Find)

Module 11

Module 11: Dynamic Programming (Memoization & Tabulation)

1 Lessons

Overlapping subproblems, optimal substructure, top-down memoization, bottom-up tabulation, and knapsack.

11.1Dynamic Programming: Top-Down & Bottom-Up Patterns