Foundations of Data Science:

• Introduction to data types and data structures
• Basics of programming languages (such as Python or R)
• Data manipulation and cleaning techniques

Statistics and Mathematics:

• Descriptive and inferential statistics
• Probability theory
• Linear algebra and calculus

Machine Learning:

• Supervised learning (e.g., regression, classification)
• Unsupervised learning (e.g., clustering, dimensionality reduction)
• Ensemble methods (e.g., random forests, gradient boosting)
• Neural networks and deep learning

Data Visualization:

• Principles of effective data visualization
• Tools and libraries for creating visualizations
• Interpretation of visualized data

Big Data Technologies:

• Hadoop and MapReduce
• Spark and distributed computing
• NoSQL databases

Data Ethics and Privacy:

• Ethical considerations in data science
• Privacy and security issues
• Legal and regulatory frameworks

Domain-specific Applications:

• Application of data science techniques to specific fields (e.g., healthcare, finance, marketing)
• Case studies and real-world projects