Professor in Bioinformatics, Aarhus University Hospital and Aarhus University
Data Science
You will learn data analysis “best practices” ranging from basic data management to visualization and advanced modelling.
This course is a part of
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.
Teaching form
Combined on campus and online sessions, project work and report writing. The course concludes with interdisciplinary group work based on a case.
Course directors on Data Science
Course details: Data Science
Dates and examination
Course dates
26 April 2024 - online
2-3 May 2024 - campus Aarhus
7 May 2024 - online
14 May 2024 - online
21-22 May 2024 - campus Aarhus
4 June 2024 - online
11 June 2024 - online
The course runs every second year in spring semester (2024, 2026, etc.) - 30 seats
Examination
Please find the exam dates in the exam plan.
You can find relevant information about the exam in the course curriculum.
See an overview of all courses (in Danish) on Master of Personalised Medicine.
Learning outcomes
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.
Admission criteria
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 in the admission criteria on Master of Personalised Medicine (in Danish).
Priority is given to enrolled students
This course is offered as an elective course on the Master of Personalised Medicine programme (website in Danish). Priority is given to students already enrolled on Master of Personalised Medicine. Once the enrolled students have been admitted to the course, the remaining seats are distributed on a first-come, first-served basis.
Tuition fees
EU/EAA Citizens: 10.500 DKK
Non EU/EAA Citizens: 14.000 DKK
New course fee effective as of 2026:
EU/EEA citizens: 11.500 DKK
Non EU/EAA Citizens: 15.500 DKK
Terms of Payment (in Danish)
Contact
Janni Stubkjær Rasmussen
Programme Coordinator
master@sund.ku.dk
Tel.: +45 35 32 12 79
Place
Aarhus University, Aarhus, Denmark
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