https://github.com/chevyphillip/python-data-structures-practice
Educational Python repository for WGU MSSWEAIE program. Interactive Jupyter notebooks for data structures with ADHD-friendly exercises. 🌐 Live docs: https://chevyphillip.github.io/python-data-structures-practice/
https://github.com/chevyphillip/python-data-structures-practice
adhd-friendly computer-science data-structures dependency-management dictionaries documentation education exercises github-pages jupyter-notebook learning lists practice python sets uv wgu
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Educational Python repository for WGU MSSWEAIE program. Interactive Jupyter notebooks for data structures with ADHD-friendly exercises. 🌐 Live docs: https://chevyphillip.github.io/python-data-structures-practice/
- Host: GitHub
- URL: https://github.com/chevyphillip/python-data-structures-practice
- Owner: chevyphillip
- Created: 2025-07-08T14:33:46.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-07-08T15:44:05.000Z (12 months ago)
- Last Synced: 2025-07-08T15:44:42.313Z (12 months ago)
- Topics: adhd-friendly, computer-science, data-structures, dependency-management, dictionaries, documentation, education, exercises, github-pages, jupyter-notebook, learning, lists, practice, python, sets, uv, wgu
- Language: Jupyter Notebook
- Homepage: https://chevyphillip.github.io/python-data-structures-practice/
- Size: 73.2 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Python Data Structures Practice - WGU MSSWEAIE
[](https://chevyphillip.github.io/python-data-structures-practice/)
[](https://www.python.org/downloads/)
[](https://github.com/astral-sh/uv)
[](LICENSE)
## 🌐 Live Documentation
**📖 Visit the interactive documentation website:** [https://chevyphillip.github.io/python-data-structures-practice/](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)
```bash
# 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
```bash
# 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](https://chevyphillip.github.io/python-data-structures-practice/INSTALLATION)
## 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](https://chevyphillip.github.io/python-data-structures-practice/STUDY_GUIDE)** - Structured learning approach and memory aids
- **[Solutions](solutions/)** - Complete solutions with multiple approaches
- **[Installation Guide](https://chevyphillip.github.io/python-data-structures-practice/INSTALLATION)** - 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.
---
**🌐 Live Documentation:** [https://chevyphillip.github.io/python-data-structures-practice/](https://chevyphillip.github.io/python-data-structures-practice/)
**📂 Repository:** [https://github.com/chevyphillip/python-data-structures-practice](https://github.com/chevyphillip/python-data-structures-practice)