CHANGED: Project in Statistics

Course content


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.

MSc Programme in Statistics
MSc Programme in Mathematics-Economics

Learning outcome



  • Aspects of applied statistics.
  • The specific subject as outlined in the description of the chosen project.

Skills: Ability to

  • independently read graduate level statistics literature
  • master the subject outlined in the project description
  • present models and methods studied in a concise way
  • implement methods and/or analyze data and present the results.


Competences:  Ability to

  • document models, methods, implementation and/or data analysis in a coherent report
  • be able to prioritize efforts in the studies, the practical implementations, the data analysis and the documentation so that the reader can assess and if necessary reproduce the results
  • document that the goals of the project description are accomplished.

Practical project under supervision.

The participants are expected to have the mathematical and statistical competences of a MSc student in statistics and have passed the courses DisMod and Regression (Reg).

Continuous feedback during the course of the semester
7,5 ECTS
Type of assessment
Written assignment
Final individual report
All aids allowed
Marking scale
7-point grading scale
Censorship form
External censorship
Criteria for exam assessment

The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.

Single subject courses (day)

  • Category
  • Hours
  • Guidance
  • 14
  • Project work
  • 190
  • Lectures
  • 2
  • English
  • 206