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.
Information for admitted students spring 2025
Congratulations! You have been admitted at Stockholm University and we hope that you will enjoy your studies with us.
In order to ensure that your studies begin as smoothly as possible we have compiled a short checklist for the beginning of the semester.
Follow the instructions on whether you have to reply to your offer or not.
universityadmissions.se
Checklist for admitted students
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Activate your university account
The first step in being able to register and gain access to all the university's IT services.
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Register at your department
Registration can be done in different ways. Read the instructions from your department below.
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Read all the information on this page
Here you will find what you need to know before your course or programme starts.
IMPORTANT
Your seat may be withdrawn if you do not register according to the instructions provided by your department.
Information from your department
On this page you will shortly find information on registration, learning platform, etc.
Welcome activities
Stockholm University organises a series of welcome activities that stretch over a few weeks at the beginning of each semester. The programme is voluntary (attendance is optional) and includes Arrival Service at the airport and an Orientation Day, see more details about these events below.
Your department may also organise activities for welcoming international students. More information will be provided by your specific department.
Find your way on campus
Stockholm University's main campus is in the Frescati area, north of the city centre. While most of our departments and offices are located here, there are also campus areas in other parts of the city.
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For new international students
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.
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Course structure
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)
Modules
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 format
Teaching in the course consist of lectures, seminars, project work and laboratory sessions.
Assessment
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.
Examiner
Juliette Griffie
Email: juliette.griffie@dbb.su.se
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Contact
Juliette Griffie
Email: juliette.griffie@dbb.su.se
Chemistry Section & Student Affairs Office- Visiting address
Arrhenius laboratory, room M345
Svante Arrhenius väg 16 A-D
- Here you will find:
Student administrator
International coordinator
Study advisor
Director of studies
- Office hours
Monday, Tuesday and Wednesday 09.00-11.30 and 12.30-15.00
- Phone hours
Wednesday 10.00-11.30 and 12.30-15.00
