The One Wiki to Rule Them All

A beginner-friendly guide to Data Structures and Algorithms with clear explanations and Python code examples.

Start Here

• Big O Notation - Understanding algorithm speed and efficiency
• Data Structures - How to store and organize data
• Algorithms - How to solve problems efficiently

What You’ll Learn

This guide helps answer common questions:

• Which data structure should I use? - Arrays, linked lists, trees, graphs, and when to pick each one
• Which algorithm solves my problem? - Sorting, searching, pathfinding, and more
• How fast is it? - Time and space complexity explained simply
• What are the trade-offs? - Pros and cons of different approaches

Key Concepts

Time Complexity

How much longer does an algorithm take as input grows?

Example: Searching a phone book

• O(n) - Check every page (slow)
• O(log n) - Open to middle, eliminate half each time (fast)

Space Complexity

How much more memory does an algorithm need as input grows?

Example: Copying vs. sorting in-place

• O(n) - Need a full copy of the data
• O(1) - Only need a few extra variables

Learn more about Big O Notation - Complete guide with code examples

Structure

Each topic includes:

1. Definition - What it is in plain terms
2. Use cases - When to use it
3. Code - Working Python examples
4. Complexity - Time and space analysis
5. Trade-offs - When to use vs. avoid