Kaisa_2012_3_photo by Veikko Somerpuro

10.2.2020 at 09:00 - 9.3.2020 at 23:59


Here is the course’s teaching schedule. Check the description for possible other schedules.

Mon 9.3.2020
10:15 - 11:45
Thu 12.3.2020
12:15 - 14:00
Mon 16.3.2020
10:15 - 11:45
Thu 19.3.2020
12:15 - 14:00
Mon 23.3.2020
10:15 - 11:45
Thu 26.3.2020
12:15 - 14:00
Mon 30.3.2020
10:15 - 11:45
Thu 2.4.2020
12:15 - 14:00
Mon 6.4.2020
10:15 - 11:45
Wed 8.4.2020
12:15 - 14:00


Master's Programme in Life Science Informatics is responsible for the course.

Module where the course belongs to:

  • Systems biology and medicine

The course is available to students from other degree programmes.

Basic statistics.

ABC of medical genetics, Basic pharmacology.

After this course students should be able to 1) understand structure of clinical data, 2) choose proper statistical and data mining analysis tools for solving clinical research questions, 3) understand medical literature related to clinical data analysis and 4) be able to execute statistical analysis of clinical data.

The recommended time for completion is after a basic statistics course.

The course is offered every second year (even years).

5 x 2h lectures, 5 x exercises (returned and graded).

Lecture material, articles.

The following is the current plan for Autumn 2017: teaching methods may evolve from year to year.

One topic of clinical data mining is covered in a one week period consisting of a lecture and exercises.
The lecture gives an overview of the topic. To test their learning, students are given exercises, which they solve at home and then present and discuss during exercise session.

The following is the current plan for Autumn 2017: assessment practices may evolve from year to year.

Grading scale is 1...5.

A student can earn up to 15 points from exercises. The points are converted to a grade 1,...,5. All exercise sets need to be returned in time and have grade of at least 1.
Project work is graded in scale 1,...,5. The overall grade is average of the exercise and project work grades. Both exercises and project work need to be accepted with at least of grade of 1.

  • Can be taken as distance learning course. All material will be available in Moodle.
  • No attendance requirements.
  • Weekly exercises and project work.