Data science
You will learn data analysis “best practices” ranging from basic data management to visualization and advanced modeling.
Continuing professional development for personnel working in the field of personalised medicine.
Increasing amounts of data are being collected in the healthcare system from high throughput genomics, wearable devices, and electronic patient records. This course will provide you with the necessary data science skills required to analyse such large datasets.
We will cover the various data analysis steps from loading and transforming data to visualization, statistical analysis, and machine learning (both supervised and unsupervised learning).
You will learn about tools that can help make clear and reproducible analyses such as software for version control and workflow management and be introduced to the use of High-Performance Computing (HPC) and parallelization.
The course will be hands-on where you will analyse relevant data sets combined with a systematic review of the various methods and tools, including sources of error, variation, and uncertainty.
The data analysis will be done using R (tidyverse) and experience with the use of R is an advantage. Experience with R can possibly be gained by self-study in connection with the course.
2 x 2 days on campus
5 online sessions
Project work and rapport writing
The course concludes with interdisciplinary group work based on a case.
Jakob Skou Pedersen
Professor of bioinformatics, Institute for Clinical Medicine - Department of Molecular Medicine (MOMA) and Bioinformatics Research Centre (BiRC), Aarhus University.
Søren Besenbacher
Lecturer of bioinformatics, Institute for Clinical Medicine - Department of Molecular Medicine (MOMA) and Bioinformatics Research Centre (BiRC), Aarhus University.
Upon completion of the course, you will be able to:
Understand the principles behind tidyverse’s data handling, visualization, modeling, and analysis. In addition, you will have knowledge of different machine learning methods (both supervised and unsupervised learning) and when they can be used. Finally, you will have knowledge of using high performance computing (HPC) for analysis of large data sets.
Use Tidyverse to perform a complete data analysis, starting from the acquisition and formatting of raw data, over visualization, to modeling and inference.
Follow and be critical of scientific analyses of large data sets. You will be able to evaluate the possibilities and limitations of different machine learning methods in relation to various uses and the amount of data available.
Continuing professional development for medical doctors, academics within the health care system, research environments, medicinal industries and organisations working with personalised medicine.
You must meet the following criteria to be admitted to this course:
- Hold a relevant master degree or equivalent
- Have a minimum 2 years of professional experience within personal medicine in a clinical, research or academic field
- Be proficient in English
Find detailed information about the current admission criteria (in Danish).
This course is offered as an elective course on the Master of personalised medicine, which is a Danish master’s programme (Master i personlig medicin).
Priority is given to students enrolled on Master of personalised medicin. Once the enrolled students have been admitted to the course, the remaining seats are distributed on a first-come, first-served basis.
Course capacity: 30 seats.
Course Details
| Duration | 4 days on campus (2x2 days) 5 online sessions |
| Dates | Next course will run autumn 2024 |
| Place | Aarhus University, Aarhus, Denmark |
| Price | EU/EAA Citizens: 10.500 DKK Non EU/EAA Citizens: 14.000 DKK Terms of payment (in Danish) |
| Level and Credit | Master course; 5 ECTS |
| Examination | See the exam plan |
| Application deadline |
Autumn 2024 |
| Admission | To be admitted you must meet the admission criteria for Master i Personlig Medicin (in Danish) |
*The form is in Danish, please contact the admission office if needed, master@sund.ku.dk.
The opening of the application period is announced via the programme newsletter (in Danish)