Statistics for Bioinformatics and eScience (StatBI/E)
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.
- Mean, variance, estimators, two-sample comparisons.
- Maximum likelihood and least squares estimation.
- Standard errors and confidence intervals.
- Correlation, (generalized) linear and non-linear regression.
- The statistical programming language R and R notebooks.
MSc Programme in Bioinformatics
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
- Master basic implementation in R and generation of analysis reports using R notebooks.
- 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.
- 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.
5 hours of lectures and 3 hours of exercises per week. 7 weeks of classes.
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.
- 7,5 ECTS
- Type of assessment
- Type of assessment details
- The exam consists of two parts: (1) two quiz assignments (60%),
and (2) a 30-hours written take-home assignment (40%) in course
The first part consist of two individual online assignments in form of quizzes of 1.5 hours each, which will be taken as part of the teaching.
For the final grade part (1) weighs 60% and part (2) weighs 40%.
All parts need to be completed individually.
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
One internal examiner
Criteria for exam assessment
In order to obtain the grade 12 the student should convincingly and accurately demonstrate the knowledge, skills and competences described under Learning Outcome.
Single subject courses (day)
- Practical exercises
- Course number
- 7,5 ECTS
- Programme level
- Full Degree Master
- Block 2
- No limit.
The number of seats may be reduced in the late registration period
- Study Board for the Biological Area
- Department of Mathematical Sciences
- Faculty of Science
- Dmytro Marushkevych (8-4e77837e7c7938774a776b7e7238757f386e75)
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Courseinformation of students