Microbiome data science and multi-omics with R/Bioconductor
- 2 ECTS credits
- Academic year 2026-2027
- DP00BE40-3001
Education information
Implementation date
28.09.2026 - 30.09.2026
Enrollment period
-
Education type
Field-specific studies
Alternativity of education
Optional
Location
Venue location
28.09.2026 09.00 - 17.00, lecture room F101
29.09.2026 09.00 - 17.00, lecture room F101
30.09.2026 09.00 - 17.00, lecture room F101
Enrollment and further information
Course is free of charge for participants having a study right in University of Oulu and for doctoral researchers in Universities belonging to Doctoral+ affiliate network (Aalto University, Hanken School of Economics, University of Eastern Finland, University of Jyväskylä, University of Lapland, LUT University, University of Oulu, Tampere University, University of Turku, University of Vaasa, Åbo Akademi). Other participants are asked to apply for non-degree study right that involves an enrolment fee of 40e. Participants are expected to cover their own travel and accommodation.
- Participants having a study right to University of Oulu can register directly in PEPPI to Microbiome data science and multi-omics with R/Bioconductor under course code DP00BE40.
- Doctoral researchers with a study right in University belonging to Doctoral+ affiliate network can register through https://www.doctoralcourses.fi/.
- Otherwise, participants are asked to apply for a non-degree study right. Please email anna.kaisanlahti@oulu.fi for further instructions.
Registration for the course is open 1.5.–31.5.2026. Enrolment is confirmed in June.
Education description
The course will provide a gentle introduction to multi-omic data analysis with R/Bioconductor, a popular open source environment for scientific data analysis. Participants get an overview of the reproducible data analysis workflow and learn to use standardized data containers that support the integration and analysis of biomedical data. After the course you will know how to approach new tasks in biomedical data analysis by utilizing available documentation and R/Bioconductor tools.