
1. **Introduction to Data Science**
2. **Mathematics for Data Science**
- Linear Algebra
- Probability and Statistics
- Calculus
3. **Programming for Data Science**
- Python or R Basics
- Data Structures and Algorithms
4. **Data Manipulation and Exploration**
- Pandas, NumPy
- Data Cleaning and Preprocessing
- Exploratory Data Analysis (EDA)
5. **Data Visualization**
- Matplotlib, Seaborn
- Plotly, ggplot
- Dashboard Creation
6. **Databases**
- SQL
- NoSQL Databases
7. **Machine Learning**
- Supervised Learning
- Unsupervised Learning
- Evaluation Metrics
8. **Advanced Machine Learning**
- Ensemble Methods
- Deep Learning (Neural Networks, CNNs, RNNs)
- Natural Language Processing (NLP)
9. **Big Data Technologies**
- Hadoop
- Spark
10. **Model Deployment**
- Flask/Django
- Streamlit, FastAPI
11. **Cloud Computing**
- AWS, Azure, GCP
12. **Ethics and Data Privacy**
- Responsible AI
- Data Governance
13. **Capstone Project**
- End-to-End Data Science Project