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.
Education

MSc Programme in Statistics
MSc Programme in Mathematics-Economics

Learning outcome

Knowledge:

  • 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.
 

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).

Academic qualifications equivalent to a BSc degree is recommended.

Continuous feedback during the course of the semester
ECTS
7,5 ECTS
Type of assessment
Written assignment
Final individual report
Aid
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