Introduction to data analysis for life scientists
The course covers general rules and methodologies associated with data analysis in life sciences with a user perspective. This includes conventional statistical tools, handling and visualization of large and/or complex data sets (e.g., omics), image analysis, as well as an introduction to machine learning. Pitfalls and good practice associated with data analysis (e.g., bias) are also actively discussed during the course.
After completing the course, the student is expected to be able to:
- Identify and discuss potential pitfalls, like data bias, associated with analysis of complex data sets (module 1, 3)
- Account for and use common statistical tools for data analysis (module 1, 2, 3)
- Perform basic coding tasks and image processing using open source platforms (module 1, 2, 3)
- Show an understanding of available machine learning tools (module 1, 2, 3)
- Load and shape a data set to run existing computational tools and interpret their results (1, 2, 3)
- identify and formulate a research question that should be addressed (module 3)
The course consist of the following modules:
Module 1: Teori (Theory), 3.5 ECTS
Module 2: Laborationer (Laboratory exercises), 1.5 ECTS
Module 3: Projektarbete (project work), 2.5 ECTS
Teaching in the course consist of lectures, seminars, project work and laboratory sessions.
The course is examined as follows:
Examination of module 1 takes place through written exam and written reports.
Examination of module 2 takes place through written lab reports and oral presentations.
Examination of module 3 takes place through written reports and oral presentations.
Juliette Griffie
Email: juliette.griffie@dbb.su.se
Juliette Griffie
Email: juliette.griffie@dbb.su.se
