Learning DSA

A friendly, story-driven approach to learning Data Structures and Algorithms in Python!

What is this?

Learning DSA is an educational project that teaches computer science fundamentals through:

• Numbered lessons that build on each other
• Engaging narratives that make concepts memorable
• Clean Python code with type hints and doctests
• Performance analysis to understand complexity in practice

Getting Started

Prerequisites

• Python 3.14 or higher
• uv (recommended) or pip

Installation

# Clone the repository
git clone https://github.com/tony/learning-dsa.git
cd learning-dsa

# Install dependencies with uv (recommended)
uv sync --all-extras --dev

# Or with pip
pip install -e ".[dev]"

Running a Lesson

Each lesson is self-contained and can be run directly:

# Run a specific lesson
python src/algorithms/002_linear_search.py

# Or with uv
uv run python src/algorithms/002_linear_search.py

Project Structure

learning-dsa/
├── src/
│   ├── algorithms/       # Algorithm implementations (searching, sorting, etc.)
│   │   └── bst/         # Binary Search Tree algorithms
│   └── data_structures/ # Data structure implementations (arrays, stacks, trees, etc.)
└── notes/              # Learning progressions and templates

Learning Paths

Follow these progressions for a structured learning experience:

1. Data Structures Journey (notes/progression-ds.md)

   • Start with arrays and lists
   • Progress through stacks and queues
   • Master hash tables and trees
   • 45 chapters from basics to advanced

2. Algorithms Adventure (notes/progression-algo.md)

   • Begin with linear and binary search
   • Learn fundamental sorting algorithms
   • Explore advanced techniques
   • 205+ chapters covering everything from O(n²) to O(log n)

Development

Running Tests

# Run all tests
uv run pytest

# Watch mode (auto-rerun on changes)
uv run pytest-watcher

# Test a specific file
uv run pytest src/algorithms/002_linear_search.py

Code Quality

# Type checking
uv run mypy .

# Linting
uv run ruff check .

# Auto-format
uv run ruff format .

The Narratives

This project uses two storytelling frameworks to make learning memorable:

Data Analytics Pipeline

Used for data structures - follows a company building a data analytics system:

• Raw data storage → Arrays and Lists
• Task processing → Stacks and Queues
• Efficient lookups → Hash Tables and Trees
• Advanced indexing → Specialized structures

SRAS (Smart Routing and Analytics System)

Used for algorithms - simulates a growing logistics platform:

• Basic operations → Linear and Binary Search
• Data organization → Sorting algorithms
• Route optimization → Graph algorithms
• Real-time decisions → Dynamic programming

Contributing

This is a personal learning project, but suggestions and improvements are welcome! Each lesson follows the template in notes/lesson_template.py.

License

MIT License - See LICENSE file for details

Author

Created by Tony Narlock as a journey through computer science fundamentals.

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Happy learning! Remember: understanding complexity is the key to writing efficient code.