NMAK14028U Project in Statistics
MSc Programme in Mathematics-Economics
MSc Programme in Statistics
This project course on advanced statistical modeling is a compulsory part of the MSc program in statistics. The content of the course presumes a mathematical level corresponding to this MSc program, and consists of:
- An advanced subject in statistical modeling.
- Independent literature studies.
- Theoretical work on models and methods relevant for data analysis.
- Practical work on statistical modeling, implementation and/or data analysis.
- Organization of a larger project and writing of reports.
- Written presentation of methodology, data analysis and results.
The exact subjects and how the different components above are weighed depend on the supervisors assigned to the course. A list of projects will be offered at the beginning of the course, and the participants will choose one from that list.
Examples of advanced subjects are:
- Classification and machine learning.
- High-dimensional statistics.
- Functional data analysis.
- Bayesian analysis and Markov Chain Monte Carlo (MCMC).
- Mixed and hierarchical models.
- Graphical models.
- Applied biostatistics.
- Causal inference.
- Dynamical systems.
Academic qualifications equivalent to a BSc degree is recommended.
- Project work
- 7,5 ECTS
- Type of assessment
- Written assignment
- Type of assessment details
- Final individual report
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
If the project report is not passed, an new report must be handed in. It can be based on the original report.
Criteria for exam assesment
The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.
- Course code
- 7,5 ECTS
- Full Degree Master
- 1 block
- Block 4
- Course capacity
- No limit
The number of seats may be reduced in the late registration period
- Study Board of Mathematics and Computer Science
- Department of Mathematical Sciences
- Faculty of Science
- Robyn Margaret Stuart (5-5673667d72447165786c326f7932686f)