Statistical Methods in Natural Sciences
Course, Master's level, 1BG391
Expand the information below to show details on how to apply and entry requirements.
Spring 2027 Spring 2027, Uppsala, 50%, On-campus, English
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 19 January 2027–16 March 2027
- Language of instruction
- English
- Entry requirements
-
Bachelor's degree in natural sciences including 60 credits in biology and 5 credits in statistics. Proficiency in English equivalent to the Swedish upper secondary course English 6.
- Selection
-
Higher education credits in science and engineering (maximum 240 credits)
- Fees
- If you are not a citizen of a European Union (EU) or European Economic Area (EEA) country, or Switzerland, you are required to pay application and tuition fees.
- First tuition fee instalment: SEK 15,000
- Total tuition fee: SEK 15,000
- Application deadline
- 15 October 2026
- Application code
- UU-67464
Admitted or on the waiting list?
- Registration period
- 22 December 2026–18 January 2027
- Information on registration from the department
Spring 2027 Spring 2027, Uppsala, 50%, On-campus, English For exchange students
- Location
- Uppsala
- Pace of study
- 50%
- Teaching form
- On-campus
- Instructional time
- Daytime
- Study period
- 19 January 2027–16 March 2027
- Language of instruction
- English
- Entry requirements
-
Bachelor's degree in natural sciences including 60 credits in biology and 5 credits in statistics. Proficiency in English equivalent to the Swedish upper secondary course English 6.
Admitted or on the waiting list?
- Registration period
- 22 December 2026–18 January 2027
- Information on registration from the department
About the course
The course gives an introduction to the most commonly applied statistical techniques and tools used to analyse experimental data in natural sciences. In addition to providing you with an overview of the statistical "toolbox", the course aims at giving an understanding of the philosophy and reasoning behind statistical design and inference as well as practical experience in running various different statistical models. Throughout the course, participants will therefore conduct a series of practical exercises. Some support for students using the software platform R is provided.
Teaching will mostly be scheduled on Tuesday and Thursday afternoons.