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FSK3538 Data-driven Life Sciences 7.5 credits

The future of life science is data-driven, providing major new opportunities to explore and understand biology, human health and changing ecosystems.

This course is offered to master's and PhD-level students.

For more information, please check our course website:

Choose semester and course offering

Choose semester and course offering to see current information and more about the course, such as course syllabus, study period, and application information.


For course offering

Autumn 2023 Start 28 Aug 2023 programme students

Application code


Headings with content from the Course syllabus FSK3538 (Spring 2022–) are denoted with an asterisk ( )

Content and learning outcomes

Course disposition

The course consists of 14 lectures, 7 computer laboratory exercises (4 hours each), and 7 seminars.

Course contents

The course aims to introduce students to the field of computer-driven life sciences by letting them learn about their different application areas.

This course will introduce the student to data sets of different types, such as genomics, proteomics, metabolomics, transcriptomics, biomolecular structure, molecular dynamics simulations, imaging, video / audio recording, organism and habitat monitoring, population scale genetics, biobanks. Models of the biological phenomena and the related scientific breakthroughs based on the analysis of such data sets will be presented, analyzed and discussed.

Analysis techniques that will be introduced and used in this class belong to machine learning, artificial intelligence, other computational techniques for statistical analysis. In addition, visualization techniques will be introduced and discussed.

Another important aspect that will be introduced and discussed is related to ethics for data collection, management, analysis and sharing. The students will be specially trained in good practice related to computer-driven life sciences.

Intended learning outcomes

After passing the course, the student should be able to:

  • describe the field of data-driven life sciences, including an overview of the different application areas, and give examples of applications and their associated analysis methods
  • apply statistical analysis and machine learning analysis to biological data sets and formulate models of biological phenomena
  • present and review scientific literature in the field of computer-driven life sciences
  • reflect on ethical consequences of data-driven life sciences and describe good practice around the computer life cycle (collection, handling, sharing and analysis)

Literature and preparations

Specific prerequisites

Enrolled PhD student.

Recommended prerequisites

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Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale

P, F


  • LAB1 - Computer lab, 2.0 credits, grading scale: P, F
  • PRO1 - Project, 5.5 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Ethical approach

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course room in Canvas

Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.

Offered by

Main field of study

This course does not belong to any Main field of study.

Education cycle

Third cycle

Add-on studies

No information inserted


Wei Ouyang (

Postgraduate course

Postgraduate courses at SCI/Applied Physics