NMAK14029U Statistics for Bioinformatics and eScience (StatBI/E)
Volume 2026/2027
Education
MSc Program in Bioinformatics
Content
The course will take the participants through the following content:
- Standard discrete and continuous distributions, descriptive methods, Bayes' theorem, conditioning, independence, and selected probability results.
- Simulation.
- Mean, variance, estimators, two-sample comparisons.
- Maximum likelihood and least squares estimation.
- Standard errors and confidence intervals (eg via bootstrapping).
- Correlation, (generalized) linear and non-linear regression.
- The statistical programming language R.
The first homework covers the materials from Week 1 to Week 4 (the first three bullet points), and the second homework covers the materials from Week 4 to Week 7 (the last three bullet points).
Learning Outcome
Knowledge:
The basic concepts in mathematical statistics, such as:
- Probability distributions
- Standard errors and confidence intervals
- Maximum likelihood and least squares estimation
- Hypothesis testing and p-values
- (Generalized) Linear and non-linear regression
Skills:
- Master basic implementation in R.
- Use computer simulations for computations with probability distributions, including bootstrapping.
- Compute uncertainty measures, such as standard errors and confidence intervals, for estimated parameters.
- Compute predictions based on regression models taking into account the uncertainty of the predictions.
- Assess a fitted distribution using descriptive methods.
- Use general purpose methods, such as the method of least squares and maximum likelihood, to fit probability distributions to empirical data.
- Summarize empirical data and compute relevant descriptive statistics for discrete and continuous probability distributions.
Competencies:
- Formulate scientific questions in statistical terms.
- Interpret and report the conclusions of a practical data analysis.
- Assess the fit of a regression model based on diagnostic quantities and plots.
- Investigate scientific questions that are formulated in terms of comparisons of distributions or parameters by statistical methods.
- Investigate scientific questions regarding association in terms of (generalized) linear and non-linear regression models.
Recommended Academic Qualifications
IMPORTANT: This course
requires and assumes quantitative/mathematical prior knowledge
equivalent to a MatIntro or equivalent course!
MSc students and BSc students in their 3rd year with MatIntro or an equivalent course.
Academic qualifications equivalent to a BSc degree is recommended.
MSc students and BSc students in their 3rd year with MatIntro or an equivalent course.
Academic qualifications equivalent to a BSc degree is recommended.
Teaching and learning methods
28 hours of lectures and 20
hours of exercises
Workload
- Category
- Hours
- Lectures
- 28
- Preparation
- 154
- Practical exercises
- 20
- Exam
- 4
- Total
- 206
Feedback form
Written
Continuous feedback during the course of the
semester
Sign up
Self Service at KUnet
Exam
- Credit
- 7,5 ECTS
- Type of assessment
- Continuous assessmentOn-site written exam, 4 hours under invigilation
- Type of assessment details
- The exam consists of two elements:
(1) two take-home assignments:
Take-home assignment 1 (five questions): posted in Week 3, hand in Week 5 (14 days), maximum five pages
Take-home assignment 2 (five questions): posted in Week 5, hand in Week 7 (14 days), maximum five pages
(2) a 4-hour written exam, under invigilation without aids.
For the final grade (1) weights 60% and (2) weighs 40%.
All elements need to be completed individually. - Aid
- Only certain aids allowed (see description below)
Take-home assignments: All aids allowed except Generative AI.
Written on-site exam: No aids allowed.
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
One internal examiner
- Re-exam
Oral exam in the curriculum, 30 minutes, no preparation time and no aids allowed.
Criteria for exam assesment
In order to obtain grade 12, the student should convincingly and accurately demonstrate the knowledge, skills, and competencies described under Learning Outcome.
Course information
- Language
- English
- Course code
- NMAK14029U
- Credit
- 7,5 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Block 2
- Schedule
- C
- Course capacity
- No limitation – unless you register in the late-registration period (BSc and MSc) or as a credit or single subject student.
Study board
- Study Board for the Biological Area
Contracting department
- Department of Mathematical Sciences
Contracting faculty
- Faculty of Science
Course Coordinators
- Jun Yang (2-79884f7c7083773d7a843d737a)
Saved on the
23-02-2026