CS 404/504 – Special Topics: Python Programming for Data Science

Complete course materials with 46 Jupyter notebooks across 4 comprehensive themes

📊 Course Overview

• Total Size: 36MB
• Total Notebooks: 46 Jupyter notebooks
• Themes: 4 comprehensive learning modules
• Format: Interactive Jupyter notebooks with code examples

🎯 Learning Themes

Theme 1: Python Programming Fundamentals (688KB, 10 notebooks)

• Data Types & Variables - Python basics and data structures
• Control Flow - Statements, loops, and file handling
• Functions - Function definition, parameters, and scope
• Object-Oriented Programming - Classes, inheritance, and polymorphism
• Error Handling - Exceptions, modules, and best practices

Theme 2: Data Engineering (8.7MB, 14 notebooks)

• NumPy - Numerical computing and array operations
• Pandas - Data manipulation and analysis
• Matplotlib & Seaborn - Data visualization and plotting
• SQL Integration - Database queries and data extraction
• Data Preprocessing - Cleaning, transformation, and feature engineering
• Web Scraping - Data extraction from web sources

Theme 3: Machine Learning & AI (25MB, 18 notebooks)

• Scikit-Learn - Traditional machine learning algorithms
• Neural Networks - Artificial Neural Networks and deep learning
• PyTorch - Deep learning framework implementation
• Computer Vision - Convolutional Neural Networks and image processing
• Natural Language Processing - Text analysis and language models
• Transformer Networks - Modern AI architecture
• Hugging Face - Pre-trained models and pipelines
• Diffusion Models - Generative AI and image synthesis
• Large Language Models - LLM concepts and applications

Theme 4: Model Deployment (184KB, 4 notebooks)

• Web Deployment - Deploying ML models to web applications
• Cloud Deployment - AWS, Azure, and Google Cloud deployment
• Reproducible Projects - Version control and project management
• GitHub Actions - CI/CD for machine learning projects

🚀 Quick Start

Prerequisites

• Python 3.8+
• Jupyter Notebook or JupyterLab
• Required packages: NumPy, Pandas, Matplotlib, Scikit-learn, PyTorch

Installation

# Clone repository
git clone https://github.com/hbaon/cs404-python-ds.git
cd cs404-python-ds

# Install dependencies
pip install -r requirements.txt

# Launch Jupyter
jupyter notebook

📚 Learning Path

1. Start with Theme 1 - Build Python fundamentals
2. Progress to Theme 2 - Learn data manipulation
3. Advance to Theme 3 - Master machine learning
4. Complete with Theme 4 - Deploy your models

🛠️ Technologies Covered

• Programming: Python, Jupyter Notebooks, VS Code
• Data Science: NumPy, Pandas, Matplotlib, Seaborn
• Machine Learning: Scikit-learn, PyTorch, TensorFlow
• AI/ML: Computer Vision, NLP, Transformers, Diffusion Models
• Deployment: Web apps, Cloud platforms, CI/CD
• Tools: Git, GitHub Actions, Virtual Environments

📖 Course Structure

cs404-python-ds/
├── theme-01-python/     (10 notebooks) - Python fundamentals
├── theme-02-data/       (14 notebooks) - Data engineering  
├── theme-03-model/      (18 notebooks) - ML/AI models
├── theme-04-deploy/     (4 notebooks)  - Model deployment
└── README.md            - This documentation

🎓 Target Audience

• CS Students - Learning Python for data science
• Data Scientists - Expanding ML/AI skills
• Software Engineers - Adding ML capabilities
• Researchers - Practical AI implementation
• Graduate Students - Advanced ML coursework

🤝 Contributing

This repository contains comprehensive course materials for Python Programming in Data Science. Contributions and improvements are welcome!

📄 License

Educational materials - Free for academic use

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Built with ❤️ for Data Science Education

Total: 46 notebooks | 36MB | 4 comprehensive themes