Data Structures & Algorithms Tutorial
Learn Data Structures & Algorithms step by step through 87 structured lessons. The course starts with essential concepts and progresses toward practical, advanced topics, with explanations, syntax, examples, common mistakes and exercises you can try yourself.
- Level: Beginner to advanced
- Format: 87 free, self-paced lessons
- Best for: Students, career changers and developers refreshing their skills
Start learning Data Structures & Algorithms →
What You Will Learn
This learning path covers the following areas in curriculum order:
- DSA Fundamentals
- Arrays and Strings
- Searching Algorithms
- Sorting Algorithms
- Linked Lists
- Stacks and Queues
- Hashing
- Trees
- Heaps and Priority Queues
- Graphs
- Recursion and Backtracking
- Dynamic Programming
- Greedy Algorithms
- Advanced Techniques
- Problem-Solving Practice
Who This Course Is For
This tutorial is suitable for complete beginners as well as learners who already know some programming and want a structured reference. Start from the first lesson if the subject is new to you. Experienced learners can use the curriculum below to jump directly to a specific concept.
How to Use This Tutorial
- Read each lesson in curriculum order and run every example yourself.
- Change the examples to test your understanding instead of only copying them.
- Complete the exercises and review the common-mistake sections before moving on.
- Bookmark this course page so you can return to the complete lesson index.
Course Contents
DSA Fundamentals
- Data Structures and Algorithms Introduction
- Why Algorithms Matter
- Time Complexity and Big O Notation
- Space Complexity
- Best, Average, and Worst Case Analysis
- Setting Up: Python for DSA
Arrays and Strings
- Arrays (Python Lists) Explained
- Array Traversal and Manipulation
- Two-Pointer Technique
- Sliding Window Technique
- String Manipulation Basics
- Prefix Sums
Searching Algorithms
Sorting Algorithms
- Bubble Sort
- Selection Sort
- Insertion Sort
- Merge Sort
- Quick Sort
- Comparing Sorting Algorithms
- Python’s Built-in Sort (Timsort)
Linked Lists
- Singly Linked Lists
- Doubly Linked Lists
- Linked List Traversal and Insertion
- Reversing a Linked List
- Detecting a Cycle (Floyd’s Algorithm)
- Linked Lists vs Arrays
Stacks and Queues
- Stacks Explained
- Implementing a Stack in Python
- Queues Explained
- Implementing a Queue in Python
- Deques (Double-Ended Queues)
- Monotonic Stacks
Hashing
- Hash Tables Explained
- Python Dictionaries as Hash Maps
- Handling Hash Collisions
- Sets and Their Uses
- Solving Problems with Hash Maps
Trees
- Binary Trees Explained
- Tree Traversal: Inorder, Preorder, Postorder
- Level-Order Traversal (BFS)
- Binary Search Trees
- BST Insertion, Search, and Deletion
- Balanced Trees and AVL Basics
- Tries (Prefix Trees)
Heaps and Priority Queues
- Heaps Explained
- Min-Heap vs Max-Heap
- Python’s heapq Module
- Priority Queues in Practice
- The Top-K Elements Pattern
Graphs
- Graph Representations
- Breadth-First Search (BFS)
- Depth-First Search (DFS)
- Detecting Cycles in a Graph
- Topological Sort
- Dijkstra’s Shortest Path Algorithm
- Union-Find (Disjoint Set)
Recursion and Backtracking
- Recursion Fundamentals
- The Call Stack and Recursion
- Recursion vs Iteration
- Backtracking Explained
- Permutations and Combinations
- N-Queens and Classic Backtracking Problems
Dynamic Programming
- Dynamic Programming Introduction
- Memoization (Top-Down DP)
- Tabulation (Bottom-Up DP)
- The Fibonacci and Climbing Stairs Patterns
- The Knapsack Problem
- Longest Common Subsequence
- DP on Grids
Greedy Algorithms
- Greedy Algorithms Explained
- Activity Selection Problem
- Interval Scheduling and Merging Intervals
- When Greedy Fails: Greedy vs DP
Advanced Techniques
- Divide and Conquer
- Bit Manipulation Basics
- Matrix Traversal Patterns
- Fast and Slow Pointers
- Merge Intervals and Overlapping Ranges
Problem-Solving Practice
- How to Approach a DSA Problem
- Recognizing Patterns in Problems
- Analyzing Your Own Solutions
- Common DSA Interview Mistakes
- Choosing the Right Data Structure
- DSA Complexity Cheat Sheet
Frequently Asked Questions
Is this Data Structures & Algorithms tutorial free?
Yes. The lessons listed on this page are free to read and can be completed at your own pace.
Do I need previous experience?
No previous experience is required for the introductory lessons. Some advanced modules are easier if you first complete the fundamentals in this course.
How should I practise?
Type and run the examples, deliberately change their inputs, predict the result before execution and build a small project after completing each major section.
