Python Data Structures Practice - WGU MSSWEAIE

🌐 Live Documentation

📖 Visit the interactive documentation website: https://chevyphillip.github.io/python-data-structures-practice/

Overview

This repository contains comprehensive Python data structures and standard library exercises designed specifically for WGU's Master of Science in Software Engineering - AI Engineering program.

What You'll Learn:

• Core Data Structures: Master lists, dictionaries, and sets with practical applications
• Advanced Python Modules: Deep dive into itertools, collections, and functools
• Functional Programming: Learn modern Python patterns and optimization techniques
• Real-world Applications: Practice with scenarios relevant to AI/ML and software engineering

Learning Objectives

🎯 Core Data Structures

• Master Python data structure fundamentals (lists, dictionaries, sets)
• Practice real-world data manipulation scenarios
• Build confidence with slicing, indexing, and operations

🚀 Advanced Python Modules

• Master built-in functions for data transformation (map, filter, zip, enumerate)
• Learn advanced iteration patterns with itertools module
• Utilize specialized data structures from collections module
• Apply functional programming concepts with functools module

💡 Practical Applications

• Prepare for advanced AI/ML data handling
• Develop algorithmic thinking and problem-solving skills
• Build efficient, Pythonic code using standard library tools

Structure

📁 Basics (basics/)

🟢 Beginner Level - Foundation concepts and operations

• 01_lists_basics.ipynb - Foundation list operations and methods
• 02_dictionaries_basics.ipynb - Dictionary fundamentals and key-value operations
• 03_sets_basics.ipynb - Set operations, logic, and mathematical operations
• 04_combined_basics.ipynb - Integration practice with multiple data structures

📁 Intermediate (intermediate/)

🟡 Intermediate Level - Advanced Python modules and functional programming

• 01_builtin_functions.ipynb - Master map(), filter(), zip(), enumerate(), sorted()
• 02_itertools_mastery.ipynb - Advanced iteration with chain(), combinations(), groupby(), infinite iterators
• 03_collections_mastery.ipynb - Specialized data structures: Counter, defaultdict, deque, namedtuple
• 04_functools_mastery.ipynb - Functional programming: partial, reduce, lru_cache, singledispatch

📁 Advanced (advanced/)

🔴 Advanced Level - Real-world applications and complex scenarios

• 01_combined_practice.ipynb - Complex multi-structure problems
• 02_ai_scenarios.ipynb - AI/ML relevant applications and data processing

📁 Assessments (assessments/)

📝 Testing and Evaluation

• ds_while_loops_assessment.ipynb - Comprehensive assessment combining data structures with control flow

📁 Data Files (data/)

• sample_data.json - Sample data for practice exercises

📁 Solutions (solutions/)

• Complete solutions with explanations organized by difficulty level
• Alternative approaches and optimization strategies for each problem

🚀 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

📖 For detailed setup instructions, visit: Installation Guide

Difficulty Progression

🟢 Beginner (Basics):

• Basic operations, simple indexing
• Core data structure methods
• Foundation concepts (~30-45 minutes each)

🟡 Intermediate (Modules):

• Advanced Python standard library modules
• Functional programming concepts
• Performance optimization techniques (~45-50 minutes each)

🔴 Advanced (Applications):

• Real-world scenarios and complex problems
• Integration of multiple concepts
• AI/ML data processing patterns (~45+ minutes each)

Study Approach for ADHD Learners

✅ Progressive Learning - Start with basics, advance systematically ✅ Hands-on Practice - Every concept includes practical exercises ✅ Immediate Feedback - Run each code block as you write it ✅ Comprehensive Examples - Real-world scenarios in every notebook ✅ Manageable Chunks - Each notebook is designed for focused learning sessions ✅ Visual Learning - Rich examples with clear output demonstrations

📚 Additional Resources

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

🔧 Features

📚 Comprehensive Content

• ✅ Progressive Curriculum - 10+ notebooks from beginner to advanced
• ✅ Standard Library Mastery - Complete coverage of itertools, collections, functools
• ✅ Real-world Applications - Practical exercises with business scenarios
• ✅ Performance Focus - Caching, optimization, and efficiency techniques

🛠️ Technical Excellence

• ✅ Modern Dependency Management - Full support for both uv and pip
• ✅ Comprehensive Testing - Verification tools included (verify_requirements.py)
• ✅ Professional Documentation - Live website with installation guides
• ✅ Interactive Learning - Jupyter notebooks with immediate feedback

🎯 Learning Support

• ✅ Multiple Learning Styles - Visual, hands-on, and theoretical approaches
• ✅ ADHD-Friendly Design - Structured, manageable learning chunks
• ✅ Complete Solutions - Detailed explanations and alternative approaches
• ✅ Assessment Tools - Comprehensive testing and evaluation notebooks

Support

Each exercise includes:

• Clear step-by-step instructions
• Helpful hints
• Common mistake warnings
• Multiple solution approaches

🤝 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.

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🌐 Live Documentation: https://chevyphillip.github.io/python-data-structures-practice/

📂 Repository: https://github.com/chevyphillip/python-data-structures-practice