Public Health Data Analysis

This course is mandatory for those students taking the Epidemiology and data science stream in the Master’s degree program in Public Health. The course can also be taken as a singular course.

This is a hands-on course with numerous practical exercises designed to provide students with advanced knowledge and skills in analyzing complex health data for public health management, health service planning, and research purposes.

The course begins with an introduction to data sources and software tools, followed by training in R programming, data cleaning and preprocessing techniques, and handling of medical codes, dates, and time. Students will also learn methods for visualizing data effectively. Additionally, the course includes an introduction to the appropriate and inappropriate applications of artificial intelligence and machine learning in data analysis.

After completing the course, the student should be able to:

Knowledge and understanding

• Describe relevant health data sources for public health research.
• Explain how health and non-health data can be combined to address complex research and policy questions.
• Describe data structures and storage formats relevant for large-scale health data analysis.
• Explain the fundamental concepts and principles of programming in R, including data structures (e.g. vectors, data frames, lists etc), functions and workflows of efficient data analysis.
• Explain the principles of preparing data for health data research.
• Describe the differences between various data formats (e.g., wide vs. long) and their implications for analysis.
• Discuss the importance of handling dates, time, and medical codes in health data analysis.
• Understand the principles of data visualization and its role in communicating health data insights.

Skills and competences

• Demonstrate the ability to efficiently use R for data analysis and visualization.
• Organize data and workflows to ensure efficient and reproducible analyses.
• Handle various formats of data (wide vs long), date, time and medical codes for health data analysis.
• Demonstrate basic skills in utilization of artificial intelligence and machine learning for health data analysis.
• Critically evaluate health data analyses and their relevance for research, policy, and clinical practice

General competences

• Work independently and responsibly with health data in accordance with ethical and legal requirements.
• Communicate health data insights to both specialist and non-specialist audiences.
• Present research findings in a clear and scientifically rigorous manner.
• Reflect critically on how artificial intelligence can enhance or hinder learning in public health data analysis.

Anbefalte forkunnskaper

HEL-3049 Epidemiology and biostatistics I, HEL-3052 Epidemiology and biostatistics II

Language of instruction and examination

Re-sit examination