Big Data Analytics Course

Course Materials for Big Data Analytics - PSAU

📚 About¶

Comprehensive course materials for teaching Big Data Analytics using modern Python-based tools and technologies. This repository contains:

• 📖 11 Chapters covering foundations to advanced topics

• 💻 Interactive Jupyter Notebooks for hands-on learning

• 🧪 11 Practical Labs with real-world datasets

• 🐳 Docker Environment for easy setup

• 📊 Visualization Examples using Matplotlib, Seaborn, and Plotly

• ⚡ Big Data Processing with Apache Spark

🚀 Quick Start¶

Using Docker (Recommended)¶

# Clone the repository
git clone https://github.com/chebil/BigData.git
cd BigData

# Start all services (Jupyter, Spark, PostgreSQL)
docker-compose up -d

# Access Jupyter Lab at http://localhost:8888
# Access Spark UI at http://localhost:8080

Local Installation¶

# Create conda environment
conda env create -f environment.yml
conda activate bigdata-course

# Or use pip
pip install -r requirements.txt

# Start Jupyter Lab
jupyter lab

Build the Book¶

# Install Jupyter Book
pip install jupyter-book

# Build the book
jupyter-book build .

# Open _build/html/index.html in your browser

📖 Course Structure¶

Part I: Foundations¶

1. Introduction to Big Data - Concepts, lifecycle, data types

2. Data Analytics Lifecycle - Six-phase approach

3. Statistical Foundations - Python, NumPy, Pandas, visualization

Part II: Machine Learning¶

4. Clustering - K-means, hierarchical, DBSCAN

5. Association Rules - Market basket analysis, Apriori

6. Regression - Linear, multiple, regularization

7. Classification - Logistic regression, Naïve Bayes, decision trees

8. Time Series - ARIMA, forecasting, Prophet

9. Text Analytics - NLP, sentiment analysis, topic modeling

Part III: Big Data Technologies¶

10. Distributed Computing - Hadoop, Spark, PySpark

11. Advanced Topics - Deep learning, deployment, cloud platforms

🧪 Labs¶

🛠️ Technologies¶

Core Stack:

• Python 3.10+

• Jupyter Lab

• NumPy, Pandas, SciPy

• scikit-learn

• Matplotlib, Seaborn, Plotly

Big Data:

• Apache Spark 3.4

• PySpark

• Dask

Machine Learning:

• XGBoost, LightGBM

• TensorFlow, Keras, PyTorch

• Prophet, statsmodels

NLP:

• NLTK, spaCy, Gensim

• Transformers

Infrastructure:

• Docker & Docker Compose

• PostgreSQL

• Git

📊 Datasets¶

All labs use real-world datasets:

• US Census 2020 data

• Retail transactions

• Customer segmentation data

• Time series (stocks, weather)

• Text corpora (reviews, social media)

• Image datasets

🎓 Learning Outcomes¶

After completing this course, students will be able to:

✅ Apply the data analytics lifecycle to real-world problems
✅ Perform exploratory data analysis using Python
✅ Implement machine learning algorithms from scratch
✅ Build and evaluate classification and regression models
✅ Process large datasets using Apache Spark
✅ Perform text analytics and sentiment analysis
✅ Deploy machine learning models
✅ Work with big data technologies

📝 Assessment¶

• Labs: 50% (10 labs × 5% each)

• Midterm: 20%

• Capstone Project: 25%

• Participation: 5%

🤝 Contributing¶

Contributions are welcome! Please:

1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Submit a pull request

📄 License¶

This project is licensed under the MIT License - see LICENSE file.

👨‍🏫 Instructor¶

Dr. Chebil Khalil
Department of Computer Science
Prince Sattam bin Abdulaziz University (PSAU)
Email: chebilkhalil@gmail.com

🔗 Links¶

• 📚 Course Website

• 💬 Discussions

• 🐛 Issues

• 📖 Documentation

⭐ Star History¶

If you find this repository helpful, please consider giving it a star!

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