Reproducible Coding Practices for Health Data Science

Course goals

ALL PARTICIPANTS WILL BE PLACED ON THE WAITING LIST UNTIL TWO WEEKS BEFORE THE START OF THE COURSE

GRADUATE STUDENTS:
Please be aware that you can only select a course option that shows the academic year and is offered Face-to-Face (F2F)

POSTGRADUATE STUDENTS:
Please be aware that you can only select a course option that shows the academic year and is offered Face-to-Face (F2F)  or online (depending on your registration)

(FYI: the other options are options for Continuing Education (onderwijs voor professionals))

After completion of the course, the student is 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.

Content

Contact details: Educational Office Epidemiology
E-mail: msc-epidemiology@umcutrecht.nl

Registration:
You can register for this course via Osiris Student. More information about the registration procedure can be found here on the Students' site. NOTE Students of the MSc Epidemiology (Post Graduate) that register in time (i.e. at least two weeks before the start of a course) will always be admitted to the course unless it is completely full. Other students will receive information about their application two weeks before the start of the course.

Course coordinator:
Dr. R.T. (Richard) Bartels, UMC Utrecht, Julius Center for Health Sciences and Primary Care, Utrecht, the Netherlands

Course description:
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. Students 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).

Literature/study material used:
-
  
Mandatory for students in own Master’s programme:
No
 
Optional for students in other GSLS Master’s programme:
Yes
 
Prerequisite knowledge:
Introduction to Epidemiology
Introduction to Statistics
Study Design in Etiological Research
Classical Methods in Data Analysis
Modern Methods in Data Analysis