This Python course was geared toward public health graduate students, introducing them to data analysis. The course was designed to build foundational skills and apply them to real-world public health data analysis. Each class builds on the previous one for a smooth learning experience.
Teaching Strategies:
- Clear Explanations: Concepts are made simple and easy to understand.
- Real-World Examples: Uses practical examples relevant to public health.
- Interactive Learning: Includes hands-on coding exercises.
- Step-by-Step Learning: Gradually increases complexity from basic to advanced levels.
- Supportive Environment: Provides continuous feedback and assistance to students.
Concepts Covered:
- Basic Python syntax
- Variables and data types
- Input and output functions
- Basic operators
Concepts Covered:
- Conditional statements (if, elif, else)
- Loops (for and while loops)
- List comprehensions
Concepts Covered:
- Defining functions
- Function arguments and return values
- Importing and using modules
- Writing and organizing code in scripts
Concepts Covered:
- Lists, tuples, and dictionaries
- Sets and their operations
- Nested data structures
- Practical applications in data analysis
Concepts Covered:
- Reading and writing files
- Introduction to libraries like Pandas and Numpy
- Data manipulation and analysis techniques
Concepts Covered:
- Comprehensive project incorporating all learned skills
- Data analysis project relevant to public health
- Presentation of findings and code review