Reproducible Coding Practices for Health Data Science

Programming skills are becoming increasingly important for working with data on computers. Implementing best practices in coding - especially as analyses grow more complex - helps improve code to be efficient, reliable, and easy to collaborate on. This course focuses on applying good programming practices, such as modular and flexible coding and version control, when developing scripts and computational workflows. You will gain practical experience in independently solving programming challenges using available resources (such as help files, package documentation, and online forums) and tools (including debugging utilities). These skills are transferrable across different programming languages (such as R and Python).

After completion of the course, you should be able to:

• use techniques to identify and resolve basic errors and computational issues in data analysis pipelines;
• apply clean coding practices, such as proper code documentation;
• apply modular and dynamic coding principles to create flexible, reusable, and transparent code;
• use version control systems (e.g., Git) effectively to collaborate with peers:
• understand methods to maintain and share research software effectively.​​​​​​​

Our courses are aimed at clinical researchers, nurses, general practitioners, and other health professionals who want to improve their skills in epidemiology, statistics and (clinical) research.

1 week, fulltime

Please note that this course is part of an existing program within the Graduate School of Life Sciences. Tuitition fees may alter during the year.