Language of instruction
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
Basics of documentation
On completion of the course the candidate will have the following learning outcomes:
- 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
- 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
- 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
September 20th - 23rd 2021
2 ECTS Credits. Comprises of 4 full day's seminars, 6 hours each.
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.
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.
Who may participate
PhD candidates at the Faculty of Medicine. MSc students may also join.
Kjetil Utvik Harkestad
The reading list will be ready by June 1st for the autumn semester and by December 1st for the spring semester.
Type of assessment: Group examination
- Withdrawal deadline
- Examination system
- Digital exam