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. At the beginning of the course, all offered projects will briefly be presented and course participants asked to indicate a prioritised list of which project they wish to work on. Course participants will then be assigned to a project based on their indicated preferences. Students can only work on one of the offered projects and cannot bring their own projects to work on as part of this course.

 

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 Mathematics-Economics
MSc Programme in Statistics
 

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 course Regression (Reg).
Academic qualifications equivalent to a BSc degree is recommended.

This course is reserved for students in the MSc Programme in Mathematics-Economics and the MSc Programme in Statistics.

Continuous feedback during the course
ECTS
7,5 ECTS
Type of assessment
Written assignment
Type of assessment details
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.

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

Kursusinformation

Language
English
Course number
NMAK14028U
ECTS
7,5 ECTS
Programme level
Full Degree Master
Duration

1 block

Placement
Block 4
Schedulegroup
B
Capacity
No limit
The number of seats may be reduced in the late registration period
Studyboard
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Mathematical Sciences
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Sebastian Weichwald   (10-7e8270746e73826c776f4b786c7f73397680396f76)
Teacher

Sebastian Weichwald

Saved on the 03-11-2022

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