Data science with R for medical researchers

Ph.D. -course

Course description

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 ggplot2

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, and describe the differences
  • can explain exploratory data analysis steps and give reasons for that
  • can give examples of structuring and transforming data for tidy data analysis
  • understands and can explain how and why one uses version control
  • 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
  • can create a report in .html, Word, or PDF, that summarises their work

Study period

Short lectures and practical exercises in groups.

22 hrs / week, 2 weeks (total: 44 hrs)

Th course is taught in the Autumn semester.

Credits (ECTS)

2 ECTS Credits.

Course location

Language of instruction
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 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 groups will be formed in the beginning of the course and you will be working towards your final report throughout the course.
    • 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.
    Academic responsible

    Julia Romanowska

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