Python Data Structures Practice - WGU MSSWEAIE

Welcome to the Python Data Structures Practice repository! This educational resource is specifically designed for students in WGU’s Master of Science in Software Engineering - AI Engineering program.

🎯 Learning Objectives

• Master Python data structure fundamentals
• Practice real-world data manipulation scenarios
• Build confidence with slicing, indexing, and operations
• Prepare for advanced AI/ML data handling

📚 Interactive Learning Path

Core Exercises

1. Lists Basics - Foundation list operations (slicing, indexing)
2. Dictionaries Basics - Key-value mappings and lookups
3. Sets Basics - Unique collections and set operations
4. Combined Practice - Integration of all three structures
5. AI Scenarios - AI/ML relevant applications

🧠 ADHD-Friendly Design

• Short, focused exercises (~30-45 minutes each)
• Clear visual separation of concepts
• Multiple solution approaches provided
• Step-by-step progression with immediate feedback

🚀 Quick Start

Option 1: Using uv (Recommended)

# Clone the repository
git clone https://github.com/chevyphillip/python-data-structures-practice.git
cd python-data-structures-practice

# Install dependencies with uv
uv sync

# Start Jupyter Notebook
jupyter notebook

Option 2: Using pip

# Clone the repository
git clone https://github.com/chevyphillip/python-data-structures-practice.git
cd python-data-structures-practice

# Create virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Start Jupyter Notebook
jupyter notebook

📖 Documentation

• Installation Guide - Detailed setup instructions for both uv and pip
• Study Guide - Structured learning approach and memory aids
• Solutions - Complete solutions with multiple approaches

🔧 Features

• ✅ Modern Dependency Management - Full support for both uv and pip
• ✅ Comprehensive Testing - Verification tools included
• ✅ Professional Documentation - Installation guides and troubleshooting
• ✅ Real Sample Data - Music playlists and AI model metadata for practice
• ✅ Multiple Learning Styles - Visual, hands-on, and theoretical approaches

🎓 Educational Context

This repository supports the data structures fundamentals required for advanced AI/ML coursework. Each exercise builds practical skills needed for:

• Data preprocessing and cleaning
• Feature engineering
• Model input preparation
• Result analysis and visualization

🤝 Contributing

This is an educational resource. If you find issues or have suggestions for improvements, please open an issue or submit a pull request.

📄 License

This educational content is available for academic and learning purposes. Please see the repository for specific licensing information.

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Ready to start learning? Begin with Lists Basics and work through the structured progression!

View on GitHub

Installation Guide

Study Guide