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PhD course

Data science with R for medical researchers

Main content

Course description

Language of instruction


Course content

The main objective of the course is to teach necessary technical skills to manage and execute data science projects.

The course will communicate understanding of the basics of programming; creating reproducible, understandable and clean analysis workflows; managing projects digitally; usage of modern informatics infrastructure and sharing code/data.


Basics of programming with R


Medical data analysis

Visualization with ggplot

Version control

Basics of documentation

Sharing data/code

Learning outcomes

On completion of the course the candidate will have the following learning outcomes:


The candidate

  • can distinguish between R, Rstudio, and R packages
  • can explain exploratory data analysis steps
  • can give examples of structuring and transforming data for tidy data analysis
  • has understanding of version control, why and how it is used
  • can identify different visualization methods that are commonly used in data analysis
  • can give examples for privacy and data sharing rules in the medical field


The candidate

  • can use basic commands in R to load data and look into data
  • can perform structuring and transforming data using tidyverse package
  • can create visualization using ggplot2 package
  • can apply the version control principles to track progress of their work
  • can create a basic documentation for own work

General competence

The candidate

  • can recommend necessary steps for making a tidy data analysis
  • can judge which visualization method performs best depending on the type of data, visualization aim, and audience
  • can create data analysis pipeline for their own projects

Study period

September 20th - 23rd 2021

Credits (ECTS)

2 ECTS Credits. Comprises of 4 full day's seminars, 6 hours each.

Specific terms



Recommended previous knowledge

  • Tried writing or reading a script.
  • Tried some analysis - even if only two lines and even if it failed!
  • Knows how difficult it is to understand what someone else's program does.
  • If the student has ever done scripting in R, it is a plus.

Compulsory Requirements

Attendance is not obligatory. Group project at the end of the course is compulsory.

Form of assessment

The course will use the following forms of assessment:

  • Group project, delivered in form of scripts, notes, and documentation.
  • The documentation will be prepared individually by group members to show their individual progress, and will be evaluated alongside the group project.
  • Grading: pass/fail.

Course overlap


Who may participate

PhD candidates at the Faculty of Medicine. MSc students may also join.

Addtional information


Academic responsibility

Julia Romanowska

Türküler Özgümüs


Kjetil Utvik Harkestad

Academic responsibility

Julia Romanowska

Türküler Özgümüs

Reading list

The reading list will be ready by June 1st for the autumn semester and by December 1st for the spring semester.

Course location



Exam information

Study period

September 20th - 23rd 2021