Big Data in Life Sciences

5 credits

Syllabus, Master's level, 3FB034

Education cycle
Second cycle
Main field(s) of study and in-depth level
Bioinformatics A1N, Computer Science A1N, Technology A1N
Grading system
Fail (U), Pass (3), Pass with credit (4), Pass with distinction (5)
Finalised by
The Faculty Board of Science and Technology, 26 October 2021
Responsible department
Department of Pharmaceutical Biosciences

Entry requirements

120 credits including (1) 15 credits in computer science, including 5 credits in programming, and 15 credits in biology, or (2) 15 credits within the Master's Programme in Bioinformatics, including 5 credits in programming. Proficiency in English equivalent to the Swedish upper secondary course English 6.

Learning outcomes

On completion of the course the student shall be able to:

  • Explain theories and methods that are relevant for handling and analysis of massive datasets in life science;
  • Use modern systems for handling and analysis of massive datasets in life science;
  • Analyze properties of data-intensive life science applications and based on this suggest suitable strategies and architectures to meet application needs;
  • Critically analyse, discuss and present solutions in writing and orally.


Methodology in life science applications where concepts and methods for large scale data management are used. Processing of life science data using the programming model MapReduce such as Apache Spark. Batch system on computational clusters. Reproducible data analysis using workflow systems. Analysis using software containers and micro-service frameworks such as Kubernetes. Use of large-scale storage systems and virtualised environments. Applications in life science, for example genomics, proteomics, metabolomics, cell biology, pharmaceutical development, and AI/Machine learning.


Lectures, guest lectures, laboratory work, seminars and group supervision. Laboratory work and seminar classes are mandatory.


Written exam. Written and oral presentation of assignments. Active participation in seminars and laboratory work.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the disability coordinator of the university.

Other directives

This course cannot be included in the same degree as 1TD065 Big Data in Life Sciences.