BIO301 Efficient data analysis in the biomedical laboratory

Course description for academic year 2020/2021

Contents and structure

The course contains the following topics:

  • Applied linear algebra
  • Statistical experimental design
  • Multivariate data analysis and chemometrics

Literature study/project with oral presentation.

Learning Outcome

Upon successful completion of this module, students should be able to:

Knowledge

  • Explain the principles of statistical experimental design
  • Explain how to visualize huge data sets
  • Explain the principles for multivariate regression and classification used on medical data

Skills

  • Set up an appropriate experimental design for screening and optimization
  • Independently analyse data using suitable software
  • Evaluate model quality and carry out necessary improvements of methods

General competence

  • Contribute to efficient resource exploitation through sensible experiment planning
  • Import of data from analytical instrumentation to analysis software
  • Combine data from different types of measurements and present the results orally and in writing

Entry requirements

Bachelor in Biomedical Laboratory Sciences, or similar competence within medical laboratory analysis with evaluation and interpretation of analytical quality. 

Teaching methods

The learning management system Canvas is used in the course. The analysis software 'Sirius' is used for experimental design and data analysis.

The teaching is done as three week-long seminars. The students work independently between the seminars.

The first three topics are covered by regular lectures. In linear algebra practical exercises are given. Statistical experimental design and multivariate analysis contains computer exercises to focus on application of the methods. Submissions are to be delivered through It¿s learning according to specified deadlines. The final project involves literature studies and oral presentation of results . The students comment on and evaluate the contents of the presentations of their fellow students.

 

Student active learning methods are

  • 5 computer exercises
  • Preparation and presentation under supervision

Course requirements

  • Participation in the seminars
  • Submission of five computer exercises.

The assignments must be submitted within set deadlines and must be approved before examination can take place.

Approved assignments are valid for the examination semester and the 2 following semesters.

Assessment

The course has an examination in two parts: Project report and digital school exam

Project: The project is a literature study of 10-15 pages. Deadline for submission will be announced at Studentweb and digital assessment system.

Digital school exam: Time and place for the examination will be announced at Studentweb and digital assessment system.

The digital school exam counts for 60 % and the project for 40 % of the final grade.

Grading scale is A-F where F is fail.

Examination support material

PC

More about examination support material