1. Programming Foundations

• Python / R basics
• Java for Data Science & OOP Concepts
• Data structures & algorithms
• Version control (Git, GitHub)

2. Mathematics & Statistics

• Linear Algebra
• Probability & Statistics
• Calculus basics for ML

3. Data Handling

• Data cleaning & preprocessing
• Pandas & NumPy
• Data visualization (Matplotlib, Seaborn)

4. Machine Learning

• Supervised vs Unsupervised Learning
• Regression & Classification
• Clustering & Dimensionality Reduction
• Model evaluation & validation

5. Deep Learning

• Neural Networks fundamentals
• TensorFlow / PyTorch
• CNNs, RNNs, Transformers

6. Data Engineering & Tools

• SQL & Databases
• Big Data (Hadoop, Spark)
• Cloud Platforms (AWS, GCP, Azure)

7. Projects & Deployment

• Real-world datasets
• APIs & Flask/Django
• Model deployment (Docker, Streamlit, FastAPI)