Programming skills in R for life scientists (R4LIFE)

R4LIFE is a lifelong learning program designed to empower life science professionals with essential programming and data analysis skills through the R programming language. It bridges the gap between biological knowledge and computational expertise, preparing participants to tackle modern challenges in biomedical research and healthcare innovation. 

About this course

The objective of this course is to equip professionals in the life sciences with essential programming skills, focusing on the R programming language, which is widely used in biomedical research and data analysis. The program consists of two integrated modules: 

• Module 1 offers a solid introduction to R, covering its syntax, data structures, and key functions. Participants develop practical skills in data management, statistical analysis, and visualization, along with tools for creating dynamic reports and interactive applications. 
• Module 2 applies these foundations to real-world biomedical and bioinformatics contexts, using advanced R packages, biostatistical techniques, and public biomedical datasets. Through hands-on case studies, participants learn to design and implement analytical workflows for complex biomedical problems. 

R4LIFE couples core programming literacy with domain-specific applications to equip life scientists with the computational and analytical skills needed to drive innovation in research and healthcare. The program supports SUSA’s mission to promote excellence, innovation, and sustainability across the life sciences and medical fields. 

Target audience

This course is intended for students, graduates, researchers, and professionals in life sciences and related fields who wish to strengthen their digital skills and enhance their career prospects through practical training in data analysis and programming with R. 

Eligibility and technical requirements

Applications for participation are open to individuals with a background in the fields outlined in the objective section above. To attend the course, participants must have access to a computer with an active Internet connection. 

Learning outcomes

Through the two structured modules, participants will:  

• Learn R syntax, data structures, and core functions. 
• Explore R’s capabilities in data management, analysis, and visualization. 
• Develop dynamic reports and interactive applications. 
• Apply R in the context of biostatistics, biomedicine, and bioinformatics through hands-on practice and case-based learning. 
• Utilise publicly available biomedical datasets and design problem-solving workflows relevant to their field. 

By the end of the course, participants will have gained both theoretical understanding and practical experience, enabling them to integrate programming into their scientific or professional practice.