Data science with R for medical researchers

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.

Content:

Basics of programming with R

Tidyverse

Medical data analysis

Visualization with ggplot2

Version control

Basics of documentation

Sharing data/code

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

Knowledge

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

Skills

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

Short lectures and practical exercises in groups.

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

Th course is taught in the Autumn semester.